diff --git a/week6/exercises/Lecture16_DecisionTrees_Exercises_no_solutions.ipynb b/week6/exercises/Lecture16_DecisionTrees_Exercises_no_solutions.ipynb
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+{"cells": [{"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["# Exercises for Lecture 16 (Decisions Trees)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["from sklearn.datasets import load_iris\n", "from sklearn.tree import DecisionTreeClassifier \n", "from sklearn.tree import export_graphviz\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["## Exercise 1: Train a decision tree classifier on the dual moons data"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Read in example dual moons data"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["from sklearn.datasets import make_moons\n", "\n", "X_moons, y_moons = make_moons(n_samples=150, noise=0.2, random_state=42)"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Train a decision tree classifier with default hyperparameters"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Plot decision boundaries"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["def plot_decision_boundary(clf, X, y, axes, cmap):\n", "    x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 100),\n", "                         np.linspace(axes[2], axes[3], 100))\n", "    X_new = np.c_[x1.ravel(), x2.ravel()]\n", "    y_pred = clf.predict(X_new).reshape(x1.shape)\n", "    \n", "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=cmap)\n", "    plt.contour(x1, x2, y_pred, cmap=\"Greys\", alpha=0.8)\n", "    colors = {\"Wistia\": [\"#78785c\", \"#c47b27\"], \"Pastel1\": [\"red\", \"blue\"]}\n", "    markers = (\"o\", \"^\")\n", "    for idx in (0, 1):\n", "        plt.plot(X[:, 0][y == idx], X[:, 1][y == idx],\n", "                 color=colors[cmap][idx], marker=markers[idx], linestyle=\"none\")\n", "    plt.axis(axes)\n", "    plt.xlabel(r\"$x_1$\")\n", "    plt.ylabel(r\"$x_2$\", rotation=0)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["plot_decision_boundary(tree_clf1, X_moons, y_moons,\n", "                       axes=[-1.5, 2.4, -1, 1.5], cmap=\"Wistia\")\n", "plt.title(\"No restrictions\");"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["You probably found the decision tree was overfitted."]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Regularise model"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["Now train a new decision tree but by regularising it by setting appropriate hyperparameters."]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Plot decision boundaries"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["plot_decision_boundary(tree_clf2, X_moons, y_moons,\n", "                       axes=[-1.5, 2.4, -1, 1.5], cmap=\"Wistia\")\n", "plt.title(f\"min_samples_leaf = {tree_clf2.min_samples_leaf}\")\n", "plt.ylabel(\"\")"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["Hopefully your revised model no longer overfits and will generalise better to unseen data."]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Evaluate performance of the two models on unseen data"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["X_moons_test, y_moons_test = make_moons(n_samples=1000, noise=0.2,\n", "                                        random_state=43)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["tree_clf1.score(X_moons_test, y_moons_test)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["tree_clf2.score(X_moons_test, y_moons_test)"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["Hopefully your regularised model performs better."]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["## Exercise 2: Train a decision tree regressor on quadratic data"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Set up mock data"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["import numpy as np\n", "from sklearn.tree import DecisionTreeRegressor\n", "\n", "np.random.seed(42)\n", "X_quad = np.random.rand(200, 1) - 0.5  # a single random input feature\n", "y_quad = X_quad ** 2 + 0.025 * np.random.randn(200, 1)"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Train a decision tree regressor with default hyperparameters"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Make predictions"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["x1 = np.linspace(-0.5, 0.5, 500).reshape(-1, 1)\n", "y_pred1 = tree_reg1.predict(x1)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["plt.plot(X_quad, y_quad, \"b.\")\n", "plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n", "plt.axis([-0.5, 0.5, -0.05, 0.25])\n", "plt.xlabel(\"$x_1$\")\n", "plt.ylabel(\"$y$\", rotation=0)\n", "plt.legend(loc=\"upper center\")\n", "plt.title(\"No restrictions\");"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Regularise model"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["Now train a new decision tree but by regularising it by setting appropriate hyperparameters."]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n", "tree_reg2.fit(X_quad, y_quad)"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Make predictions"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["y_pred2 = tree_reg2.predict(x1)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["plt.plot(X_quad, y_quad, \"b.\")\n", "plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n", "plt.axis([-0.5, 0.5, -0.05, 0.25])\n", "plt.xlabel(\"$x_1$\")\n", "plt.ylabel(\"$y$\", rotation=0)\n", "plt.legend(loc=\"upper center\")\n", "plt.title(\"No restrictions\");"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["Hopefully your revised model no longer overfits and will generalise better to unseen data."]}], "metadata": {"celltoolbar": "Tags", "kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11"}}, "nbformat": 4, "nbformat_minor": 4}
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diff --git a/week6/exercises/Lecture17_EnsembleRFs_Exercises_no_solutions.ipynb b/week6/exercises/Lecture17_EnsembleRFs_Exercises_no_solutions.ipynb
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+{"cells": [{"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["# Exercises for Lecture 17 (Ensemble Learning and Random Forests)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.ensemble import GradientBoostingRegressor \n", "from sklearn.model_selection import train_test_split"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["## Exercise 1: Early stopping"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Set up mock data"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["# Training set: a noisy quadratic function\n", "np.random.seed(42)\n", "X = np.random.rand(100, 1) - 0.5\n", "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)\n", "\n", "# First create test and train data\n", "X_train, X_val, y_train, y_val = train_test_split(X, y)"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Train with many trees"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["from sklearn.metrics import mean_squared_error\n", "\n", "n_estimators = 300\n", "gbrt = GradientBoostingRegressor(\n", "    max_depth=2, \n", "    n_estimators=n_estimators, \n", "    learning_rate=0.1, # Set a low learning rate here\n", "    random_state=42)\n", "\n", "gbrt.fit(X_train, y_train)"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["### Compute and plot validation error for intermediate number of trees"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["# measure MSE validation error at each stage\n", "errors = [mean_squared_error(y_val, y_pred) for y_pred in gbrt.staged_predict(X_val)]"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["plt.figure(figsize=(11, 4))\n", "plt.plot(errors, \"b.-\")\n", "plt.axis([0, 300, 0, 0.01])\n", "plt.xlabel(\"Number of trees\")\n", "plt.title(\"Validation error\", fontsize=14)"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": "slide"}, "tags": []}, "source": ["### Training a better model with fewer trees\n", "\n", "- Find the best number of trees from the validation error.  Show this on a plot.\n", "- Train a new GBRT using the optimal number of trees from above.\n", "- Plot predictions of the original and best models.\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["def plot_predictions(\n", "    regressors, X, y, axes, \n", "    label=None, \n", "    style=\"r-\", \n", "    data_style=\"b.\", \n", "    data_label=None):\n", "    \n", "    x1 = np.linspace(axes[0], axes[1], 500)\n", "    \n", "    y_pred = sum(\n", "        regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n", "            \n", "    plt.plot(X[:, 0], y, data_style, label=data_label)\n", "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n", "    if label or data_label:\n", "        plt.legend(loc=\"upper center\", fontsize=16)\n", "    plt.axis(axes)"]}], "metadata": {"celltoolbar": "Tags", "kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11"}}, "nbformat": 4, "nbformat_minor": 4}
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diff --git a/week6/exercises/Lecture18_DimensionalityReduction_Exercises_no_solutions.ipynb b/week6/exercises/Lecture18_DimensionalityReduction_Exercises_no_solutions.ipynb
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+{"cells": [{"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "source": ["# Exercises for Lecture 18 (Dimensionality Reduction)"]}, {"cell_type": "markdown", "metadata": {"editable": true, "slideshow": {"slide_type": "slide"}, "tags": []}, "source": ["## Exercise 1\n", "\n", "- Load the MNIST dataset and split it into a training set and a test set (take the first 60,000 instances for training, and the remaining 10,000 for testing).\n", "- Train a Random Forest classifier on the dataset and time how long it takes.\n", "- Then evaluate the resulting model on the test set. \n", "- Next, use PCA to reduce the dataset\u2019s dimensionality, with an explained variance ratio of 95%. \n", "- Train a new Random Forest classifier on the reduced dataset and see how long it takes. \n", "- Was training much faster?\n", "- Next evaluate the classifier on the test set.\n", "- How well does it perform compare to the previous classifier?"]}, {"cell_type": "code", "execution_count": null, "metadata": {"editable": true, "slideshow": {"slide_type": ""}, "tags": []}, "outputs": [], "source": ["from sklearn.datasets import fetch_openml\n", "mnist = fetch_openml('mnist_784', version=1, cache=True)"]}], "metadata": {"celltoolbar": "Tags", "kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11"}}, "nbformat": 4, "nbformat_minor": 4}
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diff --git a/week6/slides/Lecture16_DecisionTrees.ipynb b/week6/slides/Lecture16_DecisionTrees.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Lecture 16: Decision trees"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "source": [
+    "![](https://www.tensorflow.org/images/colab_logo_32px.png)\n",
+    "[Run in colab](https://colab.research.google.com/drive/1P9IoqXN9dbjJ3TN50wa8wwDdvn9P6hX7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:19.492309Z",
+     "iopub.status.busy": "2025-02-27T23:21:19.492086Z",
+     "iopub.status.idle": "2025-02-27T23:21:19.498527Z",
+     "shell.execute_reply": "2025-02-27T23:21:19.497958Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last executed: 2025-02-27 23:21:19\n"
+     ]
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "now = datetime.datetime.now()\n",
+    "print(\"Last executed: \" + now.strftime(\"%Y-%m-%d %H:%M:%S\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Considered conceptually as a *flow diagram* or tree of decisions based on inspecting properties of data-set.\n",
+    "\n",
+    "- Can perform both classification and regression.\n",
+    "- A fundamental component of random forests (a powerful machine learning algorithm covered in the next lecture). \n",
+    "- We will learn how to visualise and make predictions using Decision Trees.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Conceptual example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/DecisionTree.jpg\" alt=\"data-layout\" width=\"500\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[[Image source](https://inside-machinelearning.com/en/decision-tree-and-hyperparameters/)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Walk-through of decision tree\n",
+    "\n",
+    "Let's consider an illustration using the Iris Data set (introduced in  Lecture 3)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Images of different Iris species\n",
+    "\n",
+    "#### Iris Setosa\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/iris_setosa.jpg\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "#### Iris Versicolor\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/iris_versicolor.jpg\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "#### Iris Virginica\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/iris_virginica.jpg\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[[Image source](https://github.com/jakevdp/sklearn_tutorial)]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Load feature matrix, where each row correpsonds to an observed (*sampled*) flower, with a number of *features*, with corresponding target vector.\n",
+    "\n",
+    "Consider two features only for now (petal length and width)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:19.532095Z",
+     "iopub.status.busy": "2025-02-27T23:21:19.531889Z",
+     "iopub.status.idle": "2025-02-27T23:21:20.377758Z",
+     "shell.execute_reply": "2025-02-27T23:21:20.377159Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(max_depth=2, random_state=42)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "iris = load_iris(as_frame=True)\n",
+    "X_iris = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
+    "y_iris = iris.target\n",
+    "\n",
+    "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
+    "tree_clf.fit(X_iris, y_iris)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:20.379798Z",
+     "iopub.status.busy": "2025-02-27T23:21:20.379575Z",
+     "iopub.status.idle": "2025-02-27T23:21:20.569651Z",
+     "shell.execute_reply": "2025-02-27T23:21:20.568896Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# We want to visualise the actual flow diagram of the tree, for this we can use graphviz\n",
+    "from sklearn.tree import export_graphviz\n",
+    "\n",
+    "export_graphviz(tree_clf, \n",
+    "                out_file = './iris_tree.dot', \n",
+    "                feature_names = iris.feature_names[ 2:], \n",
+    "                class_names = iris.target_names, \n",
+    "                rounded = True, \n",
+    "                filled = True)\n",
+    "\n",
+    "#creates a dot file :( so need to convert to something more sensible\n",
+    "! dot -Tpng ./iris_tree.dot -o ./iris_tree.png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "source": [
+    "To run dot locally you will need to install [graphviz](https://graphviz.org/).\n",
+    "\n",
+    "You can install on Mac using Homebrew:\n",
+    "```bash\n",
+    "brew install graphviz\n",
+    "```\n",
+    "\n",
+    "You can install on Ubuntu using apt:\n",
+    "```bash\n",
+    "sudo apt install graphviz\n",
+    "```\n",
+    "\n",
+    "Installation instructions for other systems are available [here](https://graphviz.org/download/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Decision tree for Iris classification (depth 2)\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/iris_tree.png\" alt=\"data-layout\" width=\"500\" style=\"display:block; margin:auto\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Tree consists of a number of nodes.\n",
+    "- Top node is the _root node_.\n",
+    "- Intermediate _split nodes_.\n",
+    "- Lower nodes are _leaf nodes_.\n",
+    "\n",
+    "Decisions based on *features* and *thresholds*.\n",
+    "\n",
+    "Navigate tree to make predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Interpreting node outputs\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/iris_tree_1node.png\" alt=\"data-layout\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "Arguments in the nodes are: \n",
+    "- Top argument shows the _threshold_ upon which the classification division was made.\n",
+    "- ```gini``` (see next slides) is a quantitative measure of impurity.\n",
+    "- ```samples``` denotes the number of training instances that satisfy the criteria.\n",
+    "- ```values``` denotes the number of training instances per class that satisfy the criteria.\n",
+    "- ```class``` prediction for the node."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Decision boundaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/decision_tree_decision_boundaries_plot.png\" alt=\"data-layout\" width=\"600\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "We set ```max_depth=2```, so algorithm stopped after two divisions. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Estimating class probabilities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Also want to know the _probability_ that an instance $i$ belongs to class $k$. \n",
+    "\n",
+    "Class probability founds by finding the leaf node for instance $i$, then returns ratio of training instances of class $k$ in this node.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Example \n",
+    "For case where flower has petals=5cm long and 1.5cm wide\n",
+    "corresponding leaf node is at depth-2 left node. \n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/decision_tree_decision_boundaries_plot.png\" alt=\"data-layout\" width=\"600\" style=\"display:block; margin:auto\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/iris_tree_1node2.png\" alt=\"data-layout\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "\n",
+    "So probabilities are: 0% (Setosa), 49/54=90.7% (Versicolor), 5/54=9.3% (Virginica)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:20.572369Z",
+     "iopub.status.busy": "2025-02-27T23:21:20.572178Z",
+     "iopub.status.idle": "2025-02-27T23:21:20.577551Z",
+     "shell.execute_reply": "2025-02-27T23:21:20.576948Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.   , 0.907, 0.093]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tree_clf.predict_proba([[5, 1.5]]).round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Quality measures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Gini impurity\n",
+    "\n",
+    "Gini impurity is defined by\n",
+    "$$\n",
+    "G_i=1-\\sum_{k=1}^{n}p^2_{i,k} ,\n",
+    "$$\n",
+    "where $p_{i,k}$ is the ratio of class $k$ instances among training instances in the $i^{\\rm th}$ node. \n",
+    "\n",
+    "$G_i=0$ means the sample is 100% _pure_ i.e. all instances are in a single class. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Gini example calculation\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/iris_tree_1node2.png\" alt=\"data-layout\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "$$\n",
+    "G_i = 1 - (0/54)^2 - (49/54)^2 - (5/54)^2=  0.168\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Entropy \n",
+    "\n",
+    "Alternative to Gini is to use entropy as the purity measure \n",
+    "$$\n",
+    "H_i=-\\sum_{k=1}^n p_{i,k}\\log_2(p_{i,k}),\n",
+    "$$\n",
+    "for $p_{i,k}\\not=0$. \n",
+    "\n",
+    "Measures the information content of variable (number of bits required to encode)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Entropy example calculation\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/iris_tree_1node2.png\" alt=\"data-layout\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "$$\n",
+    "H_i =  - (49/54) \\log_2(49/54) - (5/54) \\log_2(5/54)=  0.445\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Does it make a difference what quality measure is used? \n",
+    "\n",
+    "Not usually, although Gini tends to isolate the most frequent classes, and entropy leads to more \"balanced\" trees.  Entropy is slightly more expensive to compute."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Decision tree using entropy criterion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:20.579634Z",
+     "iopub.status.busy": "2025-02-27T23:21:20.579461Z",
+     "iopub.status.idle": "2025-02-27T23:21:20.585935Z",
+     "shell.execute_reply": "2025-02-27T23:21:20.585283Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=2)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(criterion='entropy', max_depth=2)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Redo the first example but use entropy instead\n",
+    "tree_clf = DecisionTreeClassifier(max_depth = 2,criterion='entropy') #making a decision tree of depth 2 from the data \n",
+    "tree_clf.fit(X_iris, y_iris)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:20.587706Z",
+     "iopub.status.busy": "2025-02-27T23:21:20.587538Z",
+     "iopub.status.idle": "2025-02-27T23:21:20.733060Z",
+     "shell.execute_reply": "2025-02-27T23:21:20.732336Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "export_graphviz(tree_clf, \n",
+    "                out_file = './iris_tree_entropy.dot', \n",
+    "                feature_names = iris.feature_names[ 2:], \n",
+    "                class_names = iris.target_names, \n",
+    "                rounded = True, \n",
+    "                filled = True)\n",
+    "! dot -Tpng ./iris_tree_entropy.dot -o ./iris_tree_entropy.png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Entropy tree\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/iris_tree_entropy.png\" alt=\"data-layout\" width=\"500\" style=\"display:block; margin:auto\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Gini tree (for reference)\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/iris_tree.png\" alt=\"data-layout\" width=\"500\" style=\"display:block; margin:auto\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## CART training algorithm\n",
+    "\n",
+    "Classification And Regression Tree (CART) algorihtm can be used to train decision trees -- also called *growing trees* algorithm (used by SciKit Learn)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "- Splits the sample into two subsets using a single feature $k$ at threshold $t_k$\n",
+    "- Chooses feature $k$ and threshold $t_k$ by finding pair that produces purest subset, weighted by their size.\n",
+    "\n",
+    "Cost function minimized for each split:\n",
+    "\n",
+    "$$\n",
+    "J(k,t_k)=\\frac{m_\\text{left}}{m}G_\\text{left}+\\frac{m_\\text{right}}{m}G_\\text{right} ,\n",
+    "$$\n",
+    "\n",
+    "where $G_\\text{left/right}$ measures the impurity of the left/right subset and $m_\\text{left/right}$ is the number of instances in the left/right subset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Note that the CART algorithm:\n",
+    "- Only splits data in two at each stage, i.e. is binary.\n",
+    "- Is a greedy algorithm.  It searches for the optimal split at each level, then repeats for subsequent levels.  There is no guarantee that the overall optimal tree is found.  Nevertheless, usually produces a tree that is reasonably good."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Regularisation and hyperparameters\n",
+    "\n",
+    "Decision Trees are **non-parametric** classification algorithms since the number of parameters is not determined prior to training.\n",
+    "\n",
+    "Tends to overfit if not careful. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Need to regularise the problem. This can be done by the restricting the complexity of the tree, for example though the following SciKit Learn parameters.\n",
+    "\n",
+    "- `max_depth`: maximum depth of the tree.\n",
+    "- `max_features` maximum number of features used in splitting at each node.\n",
+    "- `max_leaf_nodes` maximum number of leaf nodes.\n",
+    "- `min_samples_split`: mimimum number of samples a node must have before it can be split.\n",
+    "- `min_samples_leaf`: minimum number a leaf can have to be created.\n",
+    "- `min_weight_fraction`: Same as `min_samples_leaf` but expressed as a fraction of total samples.\n",
+    "\n",
+    "Generally, increasing ```min_*``` hyperparameters or reducing ```max_*``` hyperparameters will regularise the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Other algorithms _prune_, i.e. make a (relatively) unrestricted tree then remove statistically insignificant nodes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": [
+     "exercise_pointer"
+    ]
+   },
+   "source": [
+    "**Exercises:** *You can now complete Exercise 1 in the exercises associated with this lecture.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Decision trees for regression \n",
+    "\n",
+    "Decision trees can also be used for regression tasks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Train regression model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:20.735593Z",
+     "iopub.status.busy": "2025-02-27T23:21:20.735405Z",
+     "iopub.status.idle": "2025-02-27T23:21:20.743106Z",
+     "shell.execute_reply": "2025-02-27T23:21:20.742456Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeRegressor(max_depth=2, random_state=42)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "X_quad = np.random.rand(200, 1) - 0.5  # a single random input feature\n",
+    "y_quad = X_quad ** 2 + 0.025 * np.random.randn(200, 1)\n",
+    "\n",
+    "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
+    "tree_reg.fit(X_quad, y_quad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Visualise tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:20.744939Z",
+     "iopub.status.busy": "2025-02-27T23:21:20.744742Z",
+     "iopub.status.idle": "2025-02-27T23:21:20.896284Z",
+     "shell.execute_reply": "2025-02-27T23:21:20.895553Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "export_graphviz(\n",
+    "    tree_reg,\n",
+    "    out_file=\"./regression_tree.dot\",\n",
+    "    feature_names=[\"x1\"],\n",
+    "    rounded=True,\n",
+    "    filled=True\n",
+    ")\n",
+    "\n",
+    "#creates a dot file :( so need to convert to something more sensible\n",
+    "! dot -Tpng ./regression_tree.dot -o ./regression_tree.png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture16_Images/regression_tree.png\" alt=\"data-layout\" width=\"700\" style=\"display:block; margin:auto\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Train a deeper model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:20.898435Z",
+     "iopub.status.busy": "2025-02-27T23:21:20.898251Z",
+     "iopub.status.idle": "2025-02-27T23:21:20.904714Z",
+     "shell.execute_reply": "2025-02-27T23:21:20.904136Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeRegressor(max_depth=3, random_state=42)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tree_reg2 = DecisionTreeRegressor(max_depth=3, random_state=42)\n",
+    "tree_reg2.fit(X_quad, y_quad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Plot models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:20.906565Z",
+     "iopub.status.busy": "2025-02-27T23:21:20.906396Z",
+     "iopub.status.idle": "2025-02-27T23:21:21.488766Z",
+     "shell.execute_reply": "2025-02-27T23:21:21.488082Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'max_depth=3')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def plot_regression_predictions(tree_reg, X, y, axes=[-0.5, 0.5, -0.05, 0.25]):\n",
+    "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
+    "    y_pred = tree_reg.predict(x1)\n",
+    "    plt.axis(axes)\n",
+    "    plt.xlabel(\"$x_1$\")\n",
+    "    plt.plot(X, y, \"b.\")\n",
+    "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
+    "plt.sca(axes[0])\n",
+    "plot_regression_predictions(tree_reg, X_quad, y_quad)\n",
+    "\n",
+    "th0, th1a, th1b = tree_reg.tree_.threshold[[0, 1, 4]]\n",
+    "for split, style in ((th0, \"k-\"), (th1a, \"k--\"), (th1b, \"k--\")):\n",
+    "    plt.plot([split, split], [-0.05, 0.25], style, linewidth=2)\n",
+    "plt.text(th0, 0.16, \"Depth=0\", fontsize=15)\n",
+    "plt.text(th1a + 0.01, -0.01, \"Depth=1\", horizontalalignment=\"center\", fontsize=13)\n",
+    "plt.text(th1b + 0.01, -0.01, \"Depth=1\", fontsize=13)\n",
+    "plt.ylabel(\"$y$\", rotation=0)\n",
+    "plt.legend(loc=\"upper center\", fontsize=16)\n",
+    "plt.title(\"max_depth=2\")\n",
+    "\n",
+    "plt.sca(axes[1])\n",
+    "th2s = tree_reg2.tree_.threshold[[2, 5, 9, 12]]\n",
+    "plot_regression_predictions(tree_reg2, X_quad, y_quad)\n",
+    "for split, style in ((th0, \"k-\"), (th1a, \"k--\"), (th1b, \"k--\")):\n",
+    "    plt.plot([split, split], [-0.05, 0.25], style, linewidth=2)\n",
+    "for split in th2s:\n",
+    "    plt.plot([split, split], [-0.05, 0.25], \"k:\", linewidth=1)\n",
+    "plt.text(th2s[2] + 0.01, 0.15, \"Depth=2\", fontsize=13)\n",
+    "plt.title(\"max_depth=3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Training regression models\n",
+    "\n",
+    "CART algorithm works in the same way except the cost function is based on the mean squared error (MSE):\n",
+    "\n",
+    "$$\n",
+    "J(k,t_k)=\\frac{m_\\text{left}}{m}\\text{MSE}_\\text{left}+\\frac{m_\\text{right}}{m}\\text{MSE}_\\text{right}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": [
+     "exercise_pointer"
+    ]
+   },
+   "source": [
+    "**Exercises:** *You can now complete Exercise 2 in the exercises associated with this lecture.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Limitations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Sensitivity to axis orientation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Decision trees are sensitive to the axis orientation.\n",
+    "\n",
+    "Consider same data but with axis rotated by 45${}^\\circ$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:21.490962Z",
+     "iopub.status.busy": "2025-02-27T23:21:21.490688Z",
+     "iopub.status.idle": "2025-02-27T23:21:21.495775Z",
+     "shell.execute_reply": "2025-02-27T23:21:21.495198Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def plot_decision_boundary(clf, X, y, axes, cmap):\n",
+    "    x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 100),\n",
+    "                         np.linspace(axes[2], axes[3], 100))\n",
+    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
+    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
+    "    \n",
+    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=cmap)\n",
+    "    plt.contour(x1, x2, y_pred, cmap=\"Greys\", alpha=0.8)\n",
+    "    colors = {\"Wistia\": [\"#78785c\", \"#c47b27\"], \"Pastel1\": [\"red\", \"blue\"]}\n",
+    "    markers = (\"o\", \"^\")\n",
+    "    for idx in (0, 1):\n",
+    "        plt.plot(X[:, 0][y == idx], X[:, 1][y == idx],\n",
+    "                 color=colors[cmap][idx], marker=markers[idx], linestyle=\"none\")\n",
+    "    plt.axis(axes)\n",
+    "    plt.xlabel(r\"$x_1$\")\n",
+    "    plt.ylabel(r\"$x_2$\", rotation=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:21.497513Z",
+     "iopub.status.busy": "2025-02-27T23:21:21.497340Z",
+     "iopub.status.idle": "2025-02-27T23:21:21.688609Z",
+     "shell.execute_reply": "2025-02-27T23:21:21.687963Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(6)\n",
+    "X_square = np.random.rand(100, 2) - 0.5\n",
+    "y_square = (X_square[:, 0] > 0).astype(np.int64)\n",
+    "\n",
+    "angle = np.pi / 4  # 45 degrees\n",
+    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)],\n",
+    "                            [np.sin(angle), np.cos(angle)]])\n",
+    "X_rotated_square = X_square.dot(rotation_matrix)\n",
+    "\n",
+    "tree_clf_square = DecisionTreeClassifier(random_state=42)\n",
+    "tree_clf_square.fit(X_square, y_square)\n",
+    "tree_clf_rotated_square = DecisionTreeClassifier(random_state=42)\n",
+    "tree_clf_rotated_square.fit(X_rotated_square, y_square)\n",
+    "\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
+    "plt.sca(axes[0])\n",
+    "plot_decision_boundary(tree_clf_square, X_square, y_square,\n",
+    "                       axes=[-0.7, 0.7, -0.7, 0.7], cmap=\"Pastel1\")\n",
+    "plt.sca(axes[1])\n",
+    "plot_decision_boundary(tree_clf_rotated_square, X_rotated_square, y_square,\n",
+    "                       axes=[-0.7, 0.7, -0.7, 0.7], cmap=\"Pastel1\")\n",
+    "plt.ylabel(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Could mitigate with principle component analysis (PCA) or with ensemble methods (see upoming lectures)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Decision trees are not highly stable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "- Small changes to hyperparameters or to data may produce very different models.\n",
+    "- Even retraining with a different random seed can produce very different models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Can leverage this property by averaging over many trees, which gives rises to *ensemble methods* (as discussed in the next lecture)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": [
+     "exercise_pointer"
+    ]
+   },
+   "source": [
+    "**Exercises:** *You can now complete Exercises 1-2 in the exercises associated with this lecture.*"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/week6/slides/Lecture17_EnsembleRFs.ipynb b/week6/slides/Lecture17_EnsembleRFs.ipynb
new file mode 100644
index 0000000..6994d3f
--- /dev/null
+++ b/week6/slides/Lecture17_EnsembleRFs.ipynb
@@ -0,0 +1,2443 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Lecture 17: Ensemble learning and random forests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "source": [
+    "![](https://www.tensorflow.org/images/colab_logo_32px.png)\n",
+    "[Run in colab](https://colab.research.google.com/drive/1SNSqzKBuOnHfK_G_agJQtmE8y3WM2EhI)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:28.589474Z",
+     "iopub.status.busy": "2025-02-27T23:21:28.589245Z",
+     "iopub.status.idle": "2025-02-27T23:21:28.595460Z",
+     "shell.execute_reply": "2025-02-27T23:21:28.594870Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last executed: 2025-02-27 23:21:28\n"
+     ]
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "now = datetime.datetime.now()\n",
+    "print(\"Last executed: \" + now.strftime(\"%Y-%m-%d %H:%M:%S\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Ensemble learning overview\n",
+    "\n",
+    "- Leverage *wisdom of the crowd*.\n",
+    "- The average from many predictors (or people) may be more accurate that the result from any single given predictor.\n",
+    "- Group of predictors called an *ensemble*.\n",
+    "- Even if individual predictors/classifiers are *weak* (only slightly better than random), an ensemble can be *strong* (high accuracy)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Two broad approaches:\n",
+    "- Different predictors.\n",
+    "- Same predictor, different training sets.\n",
+    "\n",
+    "An example of ensemble learning is *Random Forests* where *Decision Trees* are trained on random _*subsets*_ of the training data and then for each sample the assigned class is the one that gets the most *votes* from the ensemble."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Voting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Consider a number of classifiers.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture17_Images/votes1.png\" alt=\"data-layout\" width=\"700\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[Source: Geron]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Majority wins (hard voting) \n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture17_Images/votes2.png\" alt=\"data-layout\" width=\"700\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[Source: Geron]\n",
+    "\n",
+    "*Majorty wins* is often called *hard voting*.\n",
+    "\n",
+    "Also _soft_ voting (see upcoming slides and example)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:28.628304Z",
+     "iopub.status.busy": "2025-02-27T23:21:28.628127Z",
+     "iopub.status.idle": "2025-02-27T23:21:29.475994Z",
+     "shell.execute_reply": "2025-02-27T23:21:29.475366Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Build a voting classifier in Scikit using three weaker classifiers\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import make_moons\n",
+    "\n",
+    "# Use moons dataset\n",
+    "X, y = make_moons(n_samples=500, noise=0.30, random_state=42) #X(features), y(classifications)\n",
+    "\n",
+    "# Split into training and test data \n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) \n",
+    "\n",
+    "# Load three different classification algorithms, and initialise \n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "log_clf = LogisticRegression(random_state=42)\n",
+    "rnd_clf = RandomForestClassifier(random_state=42)\n",
+    "svm_clf = SVC(probability=True, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:29.478273Z",
+     "iopub.status.busy": "2025-02-27T23:21:29.478018Z",
+     "iopub.status.idle": "2025-02-27T23:21:29.628799Z",
+     "shell.execute_reply": "2025-02-27T23:21:29.628226Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import VotingClassifier\n",
+    "\n",
+    "# Voting classifier \"=\" logistic + random forest + SVC\n",
+    "# Set up the voting classifier\n",
+    "voting_clf = VotingClassifier(\n",
+    "        estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
+    "        voting='soft')\n",
+    "\n",
+    "# Classify using voting classifier (auto uses all the assigned invidiual classifiers)\n",
+    "voting_clf.fit(X_train, y_train);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:29.630674Z",
+     "iopub.status.busy": "2025-02-27T23:21:29.630486Z",
+     "iopub.status.idle": "2025-02-27T23:21:29.936574Z",
+     "shell.execute_reply": "2025-02-27T23:21:29.936202Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression 0.864\n",
+      "RandomForestClassifier 0.896\n",
+      "SVC 0.896\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VotingClassifier 0.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's see how each individual classifier did:\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "for clf in (log_clf, rnd_clf, svm_clf, voting_clf): #loop over classifiers\n",
+    "    clf.fit(X_train, y_train) #fit each one individually \n",
+    "    y_pred = clf.predict(X_test) #make prediction \n",
+    "    print(clf.__class__.__name__, accuracy_score(y_test, y_pred)) #print the score    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Voting classifier did better than 3 individual ones!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Hard vs soft voting\n",
+    "\n",
+    "* Hard voting: select class with highest frequency across predictors, without weight taken into account.\n",
+    "* Soft voting: if all classifiers can estimate class probabilities (i.e. they have a `predict_proba()` method), predict overall class probability, averaged over all individual classifiers, and select highest. \n",
+    "\n",
+    "Soft voting is often better than hard voting because it gives more weight to highly confident, but perhaps less frequent, votes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Bagging and pasting \n",
+    "\n",
+    "Instead of using different predictors, we can use same the predictor but different training sets. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture17_Images/bagging.png\" alt=\"data-layout\" width=\"700\" style=\"display:block; margin:auto\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Two ways to create new training sets: \n",
+    "- When sampling is performed *with replacement*, this method is called *bagging* (short for bootstrap aggregating).\n",
+    "- When sampling is performed *without replacement*, it is called *pasting*.\n",
+    "\n",
+    "A big advantage is that trivially parallisable across nodes/CPU."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Bagging \n",
+    "\n",
+    "Given a standard training set $D$ of size $n$, **bagging** generates $m$ new training sets $D_{i}$, each of size $n'<n$, by sampling from $D$ uniformly and **with replacement**. \n",
+    "\n",
+    "By sampling *with replacement*, some observations may be repeated in each $D_{i}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Pasting\n",
+    "\n",
+    "Given a standard training set $D$ of size $n$, **pasting** generates $m$ new training sets $D_{i}$, each of size $n'<n$, by sampling from $D$ uniformly and **without replacement**. \n",
+    "\n",
+    "By sampling *without replacement*, observations **not** repeated in each $D_{i}$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:29.938737Z",
+     "iopub.status.busy": "2025-02-27T23:21:29.938552Z",
+     "iopub.status.idle": "2025-02-27T23:21:29.944402Z",
+     "shell.execute_reply": "2025-02-27T23:21:29.944039Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.856\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example of Decision Trees using moons data from before\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "# Make a single prediction using a decision tree\n",
+    "tree_clf = DecisionTreeClassifier(random_state=42) #setup classifier\n",
+    "tree_clf.fit(X_train, y_train) #train\n",
+    "y_pred_tree = tree_clf.predict(X_test) #predict\n",
+    "print(accuracy_score(y_test, y_pred_tree)) #test score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:29.946181Z",
+     "iopub.status.busy": "2025-02-27T23:21:29.946008Z",
+     "iopub.status.idle": "2025-02-27T23:21:30.703206Z",
+     "shell.execute_reply": "2025-02-27T23:21:30.702590Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.904\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SciKit Learn provides an easy way to do this using BaggingClassifier \n",
+    "from sklearn.ensemble import BaggingClassifier #load the bagging classifier \n",
+    "\n",
+    "# Train ensemble of 500 Decision Tree classifiers\n",
+    "# each using 100 training instances - randomly sampled from training set\n",
+    "# with replacement. \n",
+    "\n",
+    "bag_clf = BaggingClassifier(\n",
+    "        DecisionTreeClassifier(random_state=42), #classifier to use\n",
+    "        n_estimators=500, #number of seperate classifiers\n",
+    "        max_samples=100, #number of training instances \n",
+    "        bootstrap=True, # set to False for pasting instead of bagging.\n",
+    "        n_jobs=1, #number of cores to use -1 is all\n",
+    "        random_state=42)\n",
+    "\n",
+    "bag_clf.fit(X_train, y_train) #fit data \n",
+    "y_pred = bag_clf.predict(X_test) #compare to prediction \n",
+    "print(accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Accuracy of bagging increased compared to a single classifier."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Plot decision boundary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:30.705203Z",
+     "iopub.status.busy": "2025-02-27T23:21:30.705025Z",
+     "iopub.status.idle": "2025-02-27T23:21:30.791583Z",
+     "shell.execute_reply": "2025-02-27T23:21:30.790992Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "def plot_decision_boundary(handle, clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
+    "    x1s = np.linspace(axes[0], axes[1], 100)\n",
+    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
+    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
+    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
+    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
+    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
+    "    handle.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
+    "    if contour:\n",
+    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
+    "        handle.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
+    "    handle.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n",
+    "    handle.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n",
+    "    handle.axis(axes)\n",
+    "    handle.set_xlabel(r\"$x_1$\", fontsize=18)\n",
+    "    handle.set_ylabel(r\"$x_2$\", fontsize=18, rotation=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:30.793758Z",
+     "iopub.status.busy": "2025-02-27T23:21:30.793528Z",
+     "iopub.status.idle": "2025-02-27T23:21:31.420059Z",
+     "shell.execute_reply": "2025-02-27T23:21:31.419539Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n",
+    "\n",
+    "plot_decision_boundary(ax1, tree_clf, X, y), ax1.set_title(\"Decision Tree\")\n",
+    "\n",
+    "plot_decision_boundary(ax2, bag_clf, X, y), ax2.set_title(\"Decision Trees with Bagging\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Bagging versus pasting\n",
+    "\n",
+    "* Bootstrapping introduces a bit more diversity in the subsets that each predictor is trained on, so bagging ends up with a slightly higher bias than pasting.\n",
+    "* But this also means that predictors end up being less correlated so the ensemble’s variance is reduced.\n",
+    "* Overall, bagging often results in better models, which explains why it is generally preferred.\n",
+    "* However, if you have spare time and CPU power you can use cross-validation to evaluate both bagging and pasting and select the one that works best.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Out-of-bag evaluation\n",
+    "\n",
+    "* With bagging, some instances may be sampled several times for any given predictor, while others may not be sampled at all. \n",
+    "* By default a ```BaggingClassifier``` samples $m$ training instances with replacement (```bootstrap = True```), where $m$ is the size of the training set.\n",
+    "* Only about ~63% of the training instances are sampled on average for any given predictor.\n",
+    "\n",
+    "* The remaining 37% of the training instances that are not sampled are called _out-of-bag (oob) instances_. Note that they are not the same 37% for all predictors.\n",
+    "\n",
+    "Since a predictor never sees the oob instances during training, it can be used to give a clean evaluation on the oob instances, without the need for a validation set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:31.422340Z",
+     "iopub.status.busy": "2025-02-27T23:21:31.421969Z",
+     "iopub.status.idle": "2025-02-27T23:21:34.657016Z",
+     "shell.execute_reply": "2025-02-27T23:21:34.656250Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8986666666666666"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use oob_score=True in Scikit to do automatic oob evaluation after training.\n",
+    "bag_clf = BaggingClassifier(\n",
+    "    DecisionTreeClassifier(),\n",
+    "    n_estimators=1000,\n",
+    "    bootstrap=True, #using replacement \n",
+    "    n_jobs=-1,\n",
+    "    oob_score=True\n",
+    ")\n",
+    "bag_clf.fit(X_train, y_train)\n",
+    "bag_clf.oob_score_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Should expect about 90% accuracy on the test set.  Let's see..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:34.659228Z",
+     "iopub.status.busy": "2025-02-27T23:21:34.659028Z",
+     "iopub.status.idle": "2025-02-27T23:21:34.984277Z",
+     "shell.execute_reply": "2025-02-27T23:21:34.983579Z"
+    },
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.88"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "y_pred = bag_clf.predict(X_test)\n",
+    "accuracy_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Sampling features\n",
+    "\n",
+    "Instead of just sampling training instances, we can also randomly sample the (set of) features themselves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Random patches\n",
+    "\n",
+    "Sampling *both* instances and features is known as **random patches** method.\n",
+    "\n",
+    "In SciKit-Learn this is done by using the `max_features` and `bootstrap_features=True` key words in `BaggingClassifier`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Random sub-spaces\n",
+    "\n",
+    "Keeping all training instances (i.e., ```bootstrap = False``` and ```max_samples = 1.0```) but sampling features only (i.e., ```bootstrap_features = True``` and/ or ```max_features``` smaller than 1.0) is called the **random subspaces** method."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Random forests\n",
+    "\n",
+    "A random forest is an ensemble of decision trees.\n",
+    "\n",
+    "We have actually been manually creating these in the previous examples explicitly!\n",
+    "\n",
+    "But we can use built-in SciKit-Learn functionality.\n",
+    "* ```RandomForestClassifier```: specially designed for classification.\n",
+    "* ```RandomForestRegressor```: specially designed for regression.\n",
+    "\n",
+    "Typically trained via bagging, although alternatives can be considered."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Train a random forest directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:34.986482Z",
+     "iopub.status.busy": "2025-02-27T23:21:34.986292Z",
+     "iopub.status.idle": "2025-02-27T23:21:35.726270Z",
+     "shell.execute_reply": "2025-02-27T23:21:35.725659Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16,\n",
+    "                                 n_jobs=-1, random_state=42)\n",
+    "rnd_clf.fit(X_train, y_train)\n",
+    "y_pred_rf = rnd_clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Equivalane to a bag of decision trees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:35.728677Z",
+     "iopub.status.busy": "2025-02-27T23:21:35.728236Z",
+     "iopub.status.idle": "2025-02-27T23:21:35.733155Z",
+     "shell.execute_reply": "2025-02-27T23:21:35.732576Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "bag_clf = BaggingClassifier(\n",
+    "    DecisionTreeClassifier(max_features=\"sqrt\", max_leaf_nodes=16),\n",
+    "    n_estimators=500, n_jobs=-1, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check consistent predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:35.734992Z",
+     "iopub.status.busy": "2025-02-27T23:21:35.734794Z",
+     "iopub.status.idle": "2025-02-27T23:21:36.552229Z",
+     "shell.execute_reply": "2025-02-27T23:21:36.551813Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.True_"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bag_clf.fit(X_train, y_train)\n",
+    "y_pred_bag = bag_clf.predict(X_test)\n",
+    "np.all(y_pred_bag == y_pred_rf)  # same predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Extremely randomized trees\n",
+    "\n",
+    "When you are growing a tree in a random forest, at each node only a random subset of the features is considered for splitting (as discussed earlier). \n",
+    "\n",
+    "It is possible to make trees even more random by also using *random thresholds* for each feature rather than searching for the *best possible thresholds* (like regular decision trees do).\n",
+    "\n",
+    "Implemented using `ExtraTreesClassifier`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:36.554188Z",
+     "iopub.status.busy": "2025-02-27T23:21:36.554005Z",
+     "iopub.status.idle": "2025-02-27T23:21:37.131434Z",
+     "shell.execute_reply": "2025-02-27T23:21:37.130848Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.912\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "\n",
+    "rnd_clf = ExtraTreesClassifier(\n",
+    "    n_estimators=500, \n",
+    "    max_leaf_nodes=16, \n",
+    "    n_jobs=-1, \n",
+    "    random_state=42)\n",
+    "\n",
+    "rnd_clf.fit(X_train, y_train)\n",
+    "y_pred_et = rnd_clf.predict(X_test)\n",
+    "print(accuracy_score(y_test, y_pred_et))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Feature importance\n",
+    "\n",
+    "Can measure relative feature importance by looking at how much the tree nodes that use that feature reduce impurity on average.\n",
+    "\n",
+    "Specifically, a weighted average is computed, where each node's weight is equal to the number of associated training samples."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "\n",
+    "Can access using ```feature_importances_``` variable.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:37.133484Z",
+     "iopub.status.busy": "2025-02-27T23:21:37.133307Z",
+     "iopub.status.idle": "2025-02-27T23:21:37.846441Z",
+     "shell.execute_reply": "2025-02-27T23:21:37.845846Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sepal length (cm) = 0.11249225099876375\n",
+      "sepal width (cm) = 0.02311928828251033\n",
+      "petal length (cm) = 0.4410304643639577\n",
+      "petal width (cm) = 0.4233579963547682\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Rank features by importance in iris data (note switching to Iris from Moons)\n",
+    "\n",
+    "from sklearn.datasets import load_iris\n",
+    "iris = load_iris()\n",
+    "\n",
+    "X=iris[\"data\"]\n",
+    "y=iris[\"target\"]\n",
+    "\n",
+    "rnd_clf = RandomForestClassifier(\n",
+    "    n_estimators=500, \n",
+    "    n_jobs=-1, \n",
+    "    random_state=42)\n",
+    "\n",
+    "rnd_clf.fit(X,y)\n",
+    "\n",
+    "# Print out the importances \n",
+    "for name, importance in zip(iris[\"feature_names\"], rnd_clf.feature_importances_): \n",
+    "        print(name, \"=\", importance) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Boosting\n",
+    "\n",
+    "General term to mean combining several weak learners into a single strong learner.\n",
+    "\n",
+    "The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. There are many boosting methods available but the most popular are *AdaBoost* (adaptive boosting) and *gradient boosting*. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### AdaBoost\n",
+    "\n",
+    "Give greater weight to training instances that were underfitted in predecessor.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture17_Images/adaboost.png\" alt=\"data-layout\" width=\"600\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[Source: Geron]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "-"
+    },
+    "tags": []
+   },
+   "source": [
+    "AdaBoost pays more attention to training instances that predecessor underfitted, which forces new predictors to concentrate more on the \"hard cases\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    },
+    "tags": []
+   },
+   "source": [
+    "Disadvantage: results depend on previous classifier (sequential), so algorithm cannot be parallelized. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "### Gradient boosting \n",
+    "\n",
+    "Just like AdaBoost, Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. \n",
+    "\n",
+    "However, instead of tweaking the instance weights at every iteration like AdaBoost does, gradient boosting tries to fit the new predictor to the *residual error* made by the previous predictor."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Example\n",
+    "\n",
+    "Consider an example using decision trees as the base predictors for a regression problem. This is called Gradient Tree Boosting or Gradient Boosted Regression Trees (GBRT).\n",
+    "\n",
+    "First, let's implement this by hand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:37.848645Z",
+     "iopub.status.busy": "2025-02-27T23:21:37.848463Z",
+     "iopub.status.idle": "2025-02-27T23:21:37.852092Z",
+     "shell.execute_reply": "2025-02-27T23:21:37.851497Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "# Training set: a noisy quadratic function\n",
+    "np.random.seed(42)\n",
+    "X = np.random.rand(100, 1) - 0.5\n",
+    "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:37.853845Z",
+     "iopub.status.busy": "2025-02-27T23:21:37.853671Z",
+     "iopub.status.idle": "2025-02-27T23:21:37.857535Z",
+     "shell.execute_reply": "2025-02-27T23:21:37.856961Z"
+    },
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train Regressor\n",
+    "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
+    "tree_reg1.fit(X, y);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:37.859257Z",
+     "iopub.status.busy": "2025-02-27T23:21:37.859084Z",
+     "iopub.status.idle": "2025-02-27T23:21:37.863265Z",
+     "shell.execute_reply": "2025-02-27T23:21:37.862677Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# now train 2nd Regressor using errors made by 1st one.\n",
+    "y2 = y - tree_reg1.predict(X) #residual from the first fit (data-prediction)\n",
+    "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
+    "tree_reg2.fit(X, y2);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:37.865063Z",
+     "iopub.status.busy": "2025-02-27T23:21:37.864868Z",
+     "iopub.status.idle": "2025-02-27T23:21:37.869942Z",
+     "shell.execute_reply": "2025-02-27T23:21:37.869357Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# now train 3rd Regressor using errors made by 2nd one.\n",
+    "y3 = y2 - tree_reg2.predict(X)\n",
+    "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
+    "tree_reg3.fit(X, y3)\n",
+    "\n",
+    "X_new = np.array([[0.8]])\n",
+    "\n",
+    "# now have ensemble w/ three trees.\n",
+    "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:37.871650Z",
+     "iopub.status.busy": "2025-02-27T23:21:37.871479Z",
+     "iopub.status.idle": "2025-02-27T23:21:37.875614Z",
+     "shell.execute_reply": "2025-02-27T23:21:37.875034Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def plot_predictions(\n",
+    "    regressors, X, y, axes,\n",
+    "    ax,\n",
+    "    label=None, \n",
+    "    style=\"r-\", \n",
+    "    data_style=\"b.\", \n",
+    "    data_label=None):\n",
+    "    \n",
+    "    x1 = np.linspace(axes[0], axes[1], 500)\n",
+    "    \n",
+    "    y_pred = sum(\n",
+    "        regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n",
+    "            \n",
+    "    ax.plot(X[:, 0], y, data_style, label=data_label)\n",
+    "    ax.plot(x1, y_pred, style, linewidth=2, label=label)\n",
+    "    if label or data_label:\n",
+    "        ax.legend(loc=\"upper center\", fontsize=16)\n",
+    "    ax.axis(axes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:37.877341Z",
+     "iopub.status.busy": "2025-02-27T23:21:37.877169Z",
+     "iopub.status.idle": "2025-02-27T23:21:38.520264Z",
+     "shell.execute_reply": "2025-02-27T23:21:38.519612Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x1100 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, [[ax1, ax2], [ax3, ax4], [ax5, ax6]] = plt.subplots(3, 2, figsize=(11, 11))\n",
+    "\n",
+    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], ax=ax1, label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n",
+    "ax1.set_ylabel(\"$y$\", fontsize=16, rotation=0)\n",
+    "ax1.set_title(\"Residuals and tree predictions\", fontsize=16)\n",
+    "\n",
+    "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], ax=ax2, label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n",
+    "ax2.set_ylabel(\"$y$\", fontsize=16, rotation=0)\n",
+    "ax2.set_title(\"Ensemble predictions\", fontsize=16)\n",
+    "\n",
+    "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], ax=ax3, label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\n",
+    "ax3.set_ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n",
+    "\n",
+    "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], ax=ax4, label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n",
+    "ax4.set_ylabel(\"$y$\", fontsize=16, rotation=0)\n",
+    "\n",
+    "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], ax=ax5, label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n",
+    "ax5.set_ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n",
+    "ax5.set_xlabel(\"$x_1$\", fontsize=16)\n",
+    "\n",
+    "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], ax=ax6, label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n",
+    "ax6.set_xlabel(\"$x_1$\", fontsize=16)\n",
+    "ax6.set_ylabel(\"$y$\", fontsize=16, rotation=0);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "The ensemble’s predictions are equal to the sum of the predictions of the first two trees. Similarly, in the third row another tree is trained on the residual errors of the second tree. \n",
+    "\n",
+    "You can see that the ensemble’s predictions gradually get better as trees are added to the ensemble.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Now, let's use in-built functionality rather than implementing by hand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:38.522279Z",
+     "iopub.status.busy": "2025-02-27T23:21:38.522098Z",
+     "iopub.status.idle": "2025-02-27T23:21:38.532656Z",
+     "shell.execute_reply": "2025-02-27T23:21:38.532079Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
+       "                          random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
+       "                          random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
+       "                          random_state=42)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "\n",
+    "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3,\n",
+    "                                 learning_rate=1.0, random_state=42)\n",
+    "gbrt.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "For GBRT, the learning rate plays the role of scaling the contribution for each tree.\n",
+    "\n",
+    "Low values (e.g. 0.1) need more trees in ensemble to fit training set but predictions usually generalize better. This is called shrinkage.\n",
+    "\n",
+    "Can also consider early stopping to regularise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:38.534498Z",
+     "iopub.status.busy": "2025-02-27T23:21:38.534323Z",
+     "iopub.status.idle": "2025-02-27T23:21:38.584825Z",
+     "shell.execute_reply": "2025-02-27T23:21:38.584228Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,\n",
+       "                          n_iter_no_change=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,\n",
+       "                          n_iter_no_change=10, random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,\n",
+       "                          n_iter_no_change=10, random_state=42)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gbrt_best = GradientBoostingRegressor(\n",
+    "    max_depth=2, learning_rate=0.05, n_estimators=500,\n",
+    "    n_iter_no_change=10, # Early stopping (set to None to turn off)\n",
+    "    random_state=42)\n",
+    "gbrt_best.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:38.586570Z",
+     "iopub.status.busy": "2025-02-27T23:21:38.586390Z",
+     "iopub.status.idle": "2025-02-27T23:21:38.590237Z",
+     "shell.execute_reply": "2025-02-27T23:21:38.589641Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "92"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gbrt_best.n_estimators_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:38.591962Z",
+     "iopub.status.busy": "2025-02-27T23:21:38.591770Z",
+     "iopub.status.idle": "2025-02-27T23:21:38.595850Z",
+     "shell.execute_reply": "2025-02-27T23:21:38.595265Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def plot_predictions(regressors, X, y, axes, style,\n",
+    "                     label=None, data_style=\"b.\", data_label=None):\n",
+    "    x1 = np.linspace(axes[0], axes[1], 500)\n",
+    "    y_pred = sum(regressor.predict(x1.reshape(-1, 1))\n",
+    "                 for regressor in regressors)\n",
+    "    plt.plot(X[:, 0], y, data_style, label=data_label)\n",
+    "    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n",
+    "    if label or data_label:\n",
+    "        plt.legend(loc=\"upper center\")\n",
+    "    plt.axis(axes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:38.597668Z",
+     "iopub.status.busy": "2025-02-27T23:21:38.597495Z",
+     "iopub.status.idle": "2025-02-27T23:21:38.801956Z",
+     "shell.execute_reply": "2025-02-27T23:21:38.801261Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
+    "\n",
+    "plt.sca(axes[0])\n",
+    "plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], style=\"r-\",\n",
+    "                 label=\"Ensemble predictions\")\n",
+    "plt.title(f\"learning_rate={gbrt.learning_rate}, \"\n",
+    "          f\"n_estimators={gbrt.n_estimators_}\")\n",
+    "plt.xlabel(\"$x_1$\");\n",
+    "plt.ylabel(\"$y$\", rotation=0);\n",
+    "\n",
+    "plt.sca(axes[1])\n",
+    "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8], style=\"r-\")\n",
+    "plt.title(f\"learning_rate={gbrt_best.learning_rate}, \"\n",
+    "          f\"n_estimators={gbrt_best.n_estimators_}\")\n",
+    "plt.xlabel(\"$x_1$\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": [
+     "exercise_pointer"
+    ]
+   },
+   "source": [
+    "**Exercises:** *You can now complete Exercise 1 in the exercises associated with this lecture.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Stacking (or meta learning)\n",
+    "\n",
+    "Stacking (stacked generalisation) introduces a learner to find the optimal combination, rather than a simple aggregation.\n",
+    "\n",
+    "Done by a _blender_ or _meta learner_.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture17_Images/blender.png\" alt=\"data-layout\" width=\"600\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[Source: Geron]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Can extend to multiple blenders.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture17_Images/blender2.png\" alt=\"data-layout\" width=\"600\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[Source: Geron]"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/week6/slides/Lecture18_DimensionalityReduction.ipynb b/week6/slides/Lecture18_DimensionalityReduction.ipynb
new file mode 100644
index 0000000..3def89e
--- /dev/null
+++ b/week6/slides/Lecture18_DimensionalityReduction.ipynb
@@ -0,0 +1,1943 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Lecture 18: Dimensionality reduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "source": [
+    "![](https://www.tensorflow.org/images/colab_logo_32px.png)\n",
+    "[Run in colab](https://colab.research.google.com/drive/19J80Hg1ZLxLrpHWbgxFZoyPfql_AdeGN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:48.075087Z",
+     "iopub.status.busy": "2025-02-27T23:21:48.074841Z",
+     "iopub.status.idle": "2025-02-27T23:21:48.081076Z",
+     "shell.execute_reply": "2025-02-27T23:21:48.080546Z"
+    },
+    "slideshow": {
+     "slide_type": "skip"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last executed: 2025-02-27 23:21:48\n"
+     ]
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "now = datetime.datetime.now()\n",
+    "print(\"Last executed: \" + now.strftime(\"%Y-%m-%d %H:%M:%S\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Overview of dimensionality reduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "* Many problems have thousands or millions of features.\n",
+    "* Want to reduce the number of dimensions, i.e. features.\n",
+    "* Dimensionality reduction is (typically) lossy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Disadvantages\n",
+    "\n",
+    "* It may (or may not) speed up training but (almost certainly) can degrade resulting performance.\n",
+    "* Also makes pipelines more complex.\n",
+    "\n",
+    "$\\Rightarrow$ Should always try using original data before considering dimensionality reduction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Advantages\n",
+    "\n",
+    "* Can make problems possible, which otherwise would have been intractible.\n",
+    "* Very useful for visualization (2D, 3D representations are more intuitive)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Curse of Dimensionality\n",
+    "\n",
+    "Many things behave differently in high dimensional space.\n",
+    "\n",
+    "Our intuition is built from our experience of the 3D world and often fails in higher dimensions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "For example:\n",
+    "- Pick a random point in a unit square (a 1 × 1 square), it will have only about a 0.4% chance of being located less than 0.001 from a border (in other words, it is very unlikely that a random point will be “extreme” along any dimension).\n",
+    "- But in a 10,000-dimensional unit hypercube (a 1 × 1 × ⋯ × 1 cube, with ten thousand 1s), this probability is greater than 99.999999%. Most points in a high-dimensional hypercube are very close to the border."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Two main approaches for dimensionality reduction\n",
+    "\n",
+    "1. Projection\n",
+    "2. Manifold learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Projection \n",
+    "\n",
+    "- In most real-world problems, training instances are not spread out uniformly across all dimensions. \n",
+    "- Many features are almost constant, while others are highly correlated. \n",
+    "- As a result, all training instances actually lie within (or close to) a much lower-dimensional subspace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "For example, consider the following 3D data-set lying close to a 2D subspace.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture18_Images/Projection.png\" alt=\"data-layout\" width=\"700\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[Source: Geron]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Data can be projected to a 2D plane without losing too much information.  \n",
+    "\n",
+    "We've reduced the dimension of the data from 3 to 2.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture18_Images/projection_2d.png\" alt=\"data-layout\" width=\"700\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[Source: Geron]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Manifold learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "However, projection is not always the best approach for dimensionality reduction.  \n",
+    "\n",
+    "Some data-sets may live of spaces (manifolds) that twist and turn.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture18_Images/swiss_roll.png\" alt=\"data-layout\" width=\"500\" style=\"display:block; margin:auto\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "##### Projection versus unrolling\n",
+    "\n",
+    "Projecting would squash layers of the swiss roll (left plot), whereas better to \"unroll\" the swiss roll (right plot).\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture18_Images/swiss_roll_unrolled.png\" alt=\"data-layout\" width=\"800\" style=\"display:block; margin:auto\"/>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "##### Manifold hypothesis\n",
+    "\n",
+    "Many dimensionality reduction algorithms work by modeling the manifold on which the training instances lie; this is called *Manifold Learning*. \n",
+    "\n",
+    "It relies on the manifold hypothesis.\n",
+    "\n",
+    "> Manifold hypothesis: most real-world high-dimensional data-sets lie close to a much lower dimensional manifold. This assumption is very often empirically observed.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Principal component analysis (PCA) \n",
+    "\n",
+    "Most popular dimensionality reduction algorithm.\n",
+    "\n",
+    "1. Find hyperplane that lies closest to the data.\n",
+    "2. Projects data onto it.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Principal components"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Choose directions in the data that preserve the maximum variability.\n",
+    "\n",
+    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture18_Images/pca_variance.png\" alt=\"data-layout\" width=\"800\" style=\"display:block; margin:auto\"/>\n",
+    "\n",
+    "[Source: Geron]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "PCA finds the direction that accounts for the largest amount of *variance* in the data-set.\n",
+    "\n",
+    "It then finds the next direction, orthogonal to the first, that accounts for largest amount of remaining variance.\n",
+    "\n",
+    "Process repeats, up until the full dimension of the data.  But typically the majority of variance in the data is captured in many fewer dimensions than the maximum number.\n",
+    "\n",
+    "Each axis vector is called a principal component."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Principal components found by Singular Value Decomposition (SVD), a matrix factorization technique, of the feature matrix $X$.\n",
+    "\n",
+    "The principal components are the eigenvectors of the covariance matrix of the data $X^\\text{T} X$\n",
+    "\n",
+    "SVD of $X$ is given by:\n",
+    "\n",
+    "$$X=U \\Sigma V^T,$$\n",
+    " \n",
+    "where the columns of $V$ give the principal components (which are normalised and orthogonal).\n",
+    "\n",
+    "See the [tutorial here](https://arxiv.org/abs/1404.1100) for further details about PCA."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Note: PCA assumes data is centered around origin. Scikit-Learn PCA will adjust data for you if needed.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Example\n",
+    "\n",
+    "Generate example data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:48.115817Z",
+     "iopub.status.busy": "2025-02-27T23:21:48.115633Z",
+     "iopub.status.idle": "2025-02-27T23:21:48.339410Z",
+     "shell.execute_reply": "2025-02-27T23:21:48.338849Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.spatial.transform import Rotation\n",
+    "\n",
+    "m = 60\n",
+    "X = np.zeros((m, 3))  # initialize 3D dataset\n",
+    "np.random.seed(42)\n",
+    "angles = (np.random.rand(m) ** 3 + 0.5) * 2 * np.pi  # uneven distribution\n",
+    "X[:, 0], X[:, 1] = np.cos(angles), np.sin(angles) * 0.5  # oval\n",
+    "X += 0.28 * np.random.randn(m, 3)  # add more noise\n",
+    "X = Rotation.from_rotvec([np.pi / 29, -np.pi / 20, np.pi / 4]).apply(X)\n",
+    "X += [0.2, 0, 0.2]  # shift a bit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:48.342580Z",
+     "iopub.status.busy": "2025-02-27T23:21:48.341611Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.227869Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.227246Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=2)\n",
+    "X2D = pca.fit_transform(X)  # dataset reduced to 2D\n",
+    "X3D_inv = pca.inverse_transform(X2D)  # 3D position of the projected samples\n",
+    "X_centered = X - X.mean(axis=0)\n",
+    "U, s, Vt = np.linalg.svd(X_centered)\n",
+    "\n",
+    "axes = [-1.4, 1.4, -1.4, 1.4, -1.1, 1.1]\n",
+    "x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 10),\n",
+    "                     np.linspace(axes[2], axes[3], 10))\n",
+    "w1, w2 = np.linalg.solve(Vt[:2, :2], Vt[:2, 2])  # projection plane coefs\n",
+    "z = w1 * (x1 - pca.mean_[0]) + w2 * (x2 - pca.mean_[1]) - pca.mean_[2]  # plane\n",
+    "X3D_above = X[X[:, 2] >= X3D_inv[:, 2]]  # samples above plane\n",
+    "X3D_below = X[X[:, 2] < X3D_inv[:, 2]]  # samples below plane"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.230095Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.229790Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.520446Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.519862Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(9, 9))\n",
+    "ax = fig.add_subplot(111, projection=\"3d\")\n",
+    "\n",
+    "# plot samples and projection lines below plane first\n",
+    "ax.plot(X3D_below[:, 0], X3D_below[:, 1], X3D_below[:, 2], \"ro\", alpha=0.3)\n",
+    "for i in range(m):\n",
+    "    if X[i, 2] < X3D_inv[i, 2]:\n",
+    "        ax.plot([X[i][0], X3D_inv[i][0]],\n",
+    "                [X[i][1], X3D_inv[i][1]],\n",
+    "                [X[i][2], X3D_inv[i][2]], \":\", color=\"#F88\")\n",
+    "\n",
+    "ax.plot_surface(x1, x2, z, alpha=0.1, color=\"b\")  # projection plane\n",
+    "ax.plot(X3D_inv[:, 0], X3D_inv[:, 1], X3D_inv[:, 2], \"b+\")  # projected samples\n",
+    "ax.plot(X3D_inv[:, 0], X3D_inv[:, 1], X3D_inv[:, 2], \"b.\")\n",
+    "\n",
+    "# now plot projection lines and samples above plane\n",
+    "for i in range(m):\n",
+    "    if X[i, 2] >= X3D_inv[i, 2]:\n",
+    "        ax.plot([X[i][0], X3D_inv[i][0]],\n",
+    "                [X[i][1], X3D_inv[i][1]],\n",
+    "                [X[i][2], X3D_inv[i][2]], \"r--\")\n",
+    "\n",
+    "ax.plot(X3D_above[:, 0], X3D_above[:, 1], X3D_above[:, 2], \"ro\")\n",
+    "\n",
+    "def set_xyz_axes(ax, axes):\n",
+    "    ax.xaxis.set_rotate_label(False)\n",
+    "    ax.yaxis.set_rotate_label(False)\n",
+    "    ax.zaxis.set_rotate_label(False)\n",
+    "    ax.set_xlabel(\"$x_1$\", labelpad=8, rotation=0)\n",
+    "    ax.set_ylabel(\"$x_2$\", labelpad=8, rotation=0)\n",
+    "    ax.set_zlabel(\"$x_3$\", labelpad=8, rotation=0)\n",
+    "    ax.set_xlim(axes[0:2])\n",
+    "    ax.set_ylim(axes[2:4])\n",
+    "    ax.set_zlim(axes[4:6])\n",
+    "\n",
+    "set_xyz_axes(ax, axes)\n",
+    "ax.set_zticks([-1, -0.5, 0, 0.5, 1]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Compute SVD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.523152Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.522594Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.526229Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.525813Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_centered = X - X.mean(axis=0)\n",
+    "U, s, Vt = np.linalg.svd(X_centered)\n",
+    "V = Vt.T\n",
+    "c1 = V[:,0]\n",
+    "c2 = V[:,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.527883Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.527709Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.531736Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.531386Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.67857588, 0.70073508, 0.22023881]),\n",
+       " array([-0.72817329,  0.6811147 ,  0.07646185]))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c1, c2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Reconstruct $\\Sigma$ from `s`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.533494Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.533326Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.536249Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.535878Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "m, n = X.shape\n",
+    "Σ = np.zeros_like(X_centered)\n",
+    "Σ[:n, :n] = np.diag(s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Check recover `X_centered`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.537981Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.537785Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.540650Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.540108Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "assert np.allclose(X_centered, U @ Σ @ Vt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Projecting down to d dimensions\n",
+    "\n",
+    "Once principal components identified, can project data onto the hyperplane defined by first $d$ principal components.\n",
+    "\n",
+    "Can be performed by the dot product between the data principal components.\n",
+    "\n",
+    "1. Construct project matrix $P$ by taking first $d$ columns of $V$.\n",
+    "2. Multiple feature matrix by projection matrix to recover lower dimensional representation of the data: $X_d = X P$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Check sizes\n",
+    "\n",
+    "For $X$ of size $m \\times n$, i.e. $n$ features and $m$ data instances, the matrices have the following sizes:\n",
+    "\n",
+    "- $V$ is of size $n \\times n$.\n",
+    "- $P$ (first $d$ columns of $V$) is of size $n \\times d$.\n",
+    "- $X_d = X P$ is of size $m \\times d$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.542460Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.542294Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.545152Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.544566Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "W2 = Vt[:2].T\n",
+    "X2D = X_centered @ W2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.546946Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.546752Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.550054Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.549458Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original dimension is 3\n",
+      "Reduced dimension is 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Original dimension is {X_centered.shape[-1]}\")\n",
+    "print(f\"Reduced dimension is {X2D.shape[-1]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Scikit-Learn PCA\n",
+    "\n",
+    "Scikit-Learn's PCA support also uses SVD but makes things even easier.\n",
+    "\n",
+    "You can access each principal component using ```components_``` variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.551841Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.551672Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.555266Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.554662Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=2)\n",
+    "X2D = pca.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.557016Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.556827Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.560687Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.560112Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.67857588,  0.70073508,  0.22023881],\n",
+       "       [ 0.72817329, -0.6811147 , -0.07646185]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.components_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Explained variance ratio\n",
+    "\n",
+    "*Explained variance ratio* is a very useful metric.\n",
+    "\n",
+    "Gives the proportion of dataset variance along the axis of each principal component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.562418Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.562252Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.565839Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.565454Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.7578477 , 0.15186921])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.explained_variance_ratio_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The first dimension explains about 76% of the variance, while the second explains about 15%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "By projecting down to 2D, we lost about 9% of the variance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.567654Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.567484Z",
+     "iopub.status.idle": "2025-02-27T23:21:49.571274Z",
+     "shell.execute_reply": "2025-02-27T23:21:49.570904Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.09028309326742034)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 - pca.explained_variance_ratio_.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Choosing the right number of dimensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Compute the explained variance cumulatively to see how much captured by a given number of principal components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:49.573126Z",
+     "iopub.status.busy": "2025-02-27T23:21:49.572952Z",
+     "iopub.status.idle": "2025-02-27T23:21:53.659798Z",
+     "shell.execute_reply": "2025-02-27T23:21:53.659297Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(154)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "mnist = fetch_openml('mnist_784', as_frame=False, parser=\"auto\")\n",
+    "X_train, y_train = mnist.data[:60_000], mnist.target[:60_000]\n",
+    "X_test, y_test = mnist.data[60_000:], mnist.target[60_000:]\n",
+    "\n",
+    "pca = PCA()\n",
+    "pca.fit(X_train)\n",
+    "cumsum = np.cumsum(pca.explained_variance_ratio_)\n",
+    "d = np.argmax(cumsum >= 0.95) + 1\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "So we capture $\\geq95$% of the variance in $154$ dimensions.\n",
+    "\n",
+    "Compared to original dimensionality of $28 \\times 28 =784$), we compress to $\\sim 20$% of the original size."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Scikit-Learn also has built in functionality to automatically select number of principal components that capture a given level of variance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:53.662280Z",
+     "iopub.status.busy": "2025-02-27T23:21:53.662094Z",
+     "iopub.status.idle": "2025-02-27T23:21:55.179585Z",
+     "shell.execute_reply": "2025-02-27T23:21:55.179013Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pca = PCA(n_components=0.95)\n",
+    "X_reduced = pca.fit_transform(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:55.183080Z",
+     "iopub.status.busy": "2025-02-27T23:21:55.182254Z",
+     "iopub.status.idle": "2025-02-27T23:21:55.187758Z",
+     "shell.execute_reply": "2025-02-27T23:21:55.187347Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(154)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.n_components_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### PCA for compression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "We can also reconstruct high dimensional representation from compressed representation.\n",
+    "\n",
+    "Compression is lossy, so we won't recover the original high dimensional representation perfectly.\n",
+    "\n",
+    "Example applying PCA to MNIST dataset with 95% preservation = results in ~150 features (original = 28x28 = 784)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Since principal components are orthogonal, we can estimate a recovered high dimensional representation by\n",
+    "\n",
+    "$$X_\\text{recovered} = X_{d} P^\\text{T}.$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:55.190225Z",
+     "iopub.status.busy": "2025-02-27T23:21:55.190043Z",
+     "iopub.status.idle": "2025-02-27T23:21:55.643751Z",
+     "shell.execute_reply": "2025-02-27T23:21:55.643184Z"
+    },
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_recovered = pca.inverse_transform(X_reduced)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:55.647099Z",
+     "iopub.status.busy": "2025-02-27T23:21:55.646185Z",
+     "iopub.status.idle": "2025-02-27T23:21:55.864486Z",
+     "shell.execute_reply": "2025-02-27T23:21:55.863997Z"
+    },
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GgzA0NCS85S1vEfR6vVBRUSF89rOfLUjbUGxfHkNDQ8If/dEfCZWVlYJKpRKqqqqEe++9V3jsscfYNn/1V38lHDp0SLBarYJOpxPa2tqEv/7rvxbS6bQgCILg8/mEj3/840JbW5tgMBgEi8UiHD58WPjRj3606HzPPfeccPz4ccFisQharVZobGwUHnroIeH06dMF2/3kJz8R2tvbBY1GI3R0dAg//elPhQcffFAKv95gyARhHcorCW84nDt3Dvv27cP3vvc9vO9979vu5kiQIOENjIceegiPPfaYxFBIWBckjcybGMWKSH7lK1+BXC7HsWPHtqFFEiRIkCBBwuogaWTexPjyl7+MM2fO4NZbb4VSqcSTTz6JJ598Eh/5yEcW5YCQIEGCBAkSdiIkQ+ZNjBtuuAHPPPMMvvCFLyAajaKmpgaf+9zn8P/9f//fdjdNggQJEiRIWBEkjYwECRIkSJAg4ZqFpJGRIEGCBAkSJFyzkAwZCRIkSJAgQcI1C8mQkSBBggQJEiRcs5DEvhIkSJCA4sUMNyrD9XZiKRnkG6F/bxSUuk/SPVoekiEjQYIECRxoQnmjTSD8RPlG65uENzckQ0aCBAkS/h/y+TyAK5O+XH7V8y6e+K8VloPamc/nIQjCIiOtWFtlMtmS3+8UXCuG2XrbSfsXO85m97vYuQVBKDj/Trj2kiEjQYIECf8PNNmTQaNQKBZ9L/6bNw52wqAuhiAIyOVyzJgBrraVN9Zogir23U5CMSNyJ7JoxSZ/cTtXYhDTMynelj8ev/1Ggz+/+Pnhz7mdxr1kyEiQIEECrk74Sw3I/Db8xL9TJ32gcNJRKBRFGRfe2FEqlTvKICgF8T3YiSCjOJfLAbhyvfnrm8/nkU6n2bXXaDRQKpWLDMxizxwZ2fw93IzrIDZkxGyluJ3ZbJY9R3K5HErl5psZkiEj4ZoFv3qmFTT/Ha1AxYOIQqGAQqHY0ZNPMdAgQQOFXC6HXC6HVqvdsQP5tQDecOFdS0DxlTPvpinFbKzEhbMZWG5VLJ78+P5ls9kl999KLNeOYmxYsf1WwnyIt10v+LbRO5tOp9n3arWasSmZTAaxWIxtZzabodPpoFariz5TpVw8W4FirBD/eSaTQTqdRjweRzabZUaZ0WiEXC7fVINTMmQkXLNIp9OYm5vDzMwMBgYGFn3X39+Pubk5XLx4EXq9HmazGTfddBNaW1tx4403wmazbVPL14aLFy/ipZdewgsvvICRkRG0tLSgsbERjzzyCIxG43Y375oGTTqZTIYZJuJBO5vNQiaTQaVSLdqfn5hSqRRisRhUKhVUKhV0Ol3RfbYSSqVykbGiUCgKVsvECmSzWSiVSmg0mu1q7qIFCP2I3WO86y+bzSKXy7FJk+8bLXaI2SBWROw63Chks1kkk0nMzs4iEAhgamoKsVgMiUSCTfiTk5MIBoMYHR2FRqOBwWDAoUOH0NjYiEOHDsFms0Gn07G+5HI5dk3467LZIIOqlGYsm80iGo1iYGAA3d3dOH36NCYnJ1FTUwOXy4X7778fTqcTbrd709q4JYYMWWzZbBbhcBgajQZGo3HVN4GOE41GMTc3B6PRCJPJxCy/7YIgCEgmk0in0wgGg+yBoxUzWdgmk4m1U1pBrx/JZBJDQ0MYHx9Hb29vwXepVAqXL1/G7Owszp8/D71eD4vFArPZjEwmg7q6OmSzWTgcjmuGmclkMojH4xgcHER3dzcbFMPhMJt4pOdqbSCmK5FIQKFQQKVSlRxTyGghip13E8TjccRiMfh8Puj1euj1embQrBUrYROW0+ukUimk02n4fD5kMhkkk0mo1WpotVro9XpoNBrmdlru3Jv5jBVjx/hrXMy9wd8PnikrduzltllLOwn88YiZmJmZwczMDAYHBxGJRBCPx5mxOz4+joWFBYyOjkKn08FoNEKv1yOVSqGqqgq5XA6VlZWMPeYNOnHb1yMiLoZSInDxvvx9SaVSCIVCGB4eRl9fH0KhEMLhMILBIAwGQ8F+xTRN67kfWzb7p9NpeL1ePP7442hra8Ndd9216mMIgoB4PI4XX3wRn/3sZ3H8+HEcP34cHR0dcDgcm9Dqlbfr0qVLGB8fx/e+9z0EAgF280wmE/bu3Yv6+nq89a1vRVlZ2bavzt4omJqawhe+8AVMT09jamqq4DsynGkwjMfjSCaT+NnPfoann34aQ0ND6OzsxEc+8hH2ku10NDQ0wGg04uTJkzh37hwmJyehUCjw2muvoaGhAbt27druJl6TyOfzCAaDiMfjmJubg1arhdlsht1uL2Al+NU7TUZarRZqtRrAFWOhr68PXq8XPT09cLvdcLvd2Lt3L3Q63brbWSyChNpPTApBqVSydgmCgMHBQUxNTeGxxx7D5OQkLl68CJvNhsrKStx0001oa2vDTTfdBLvdvkjDkcvlttwVS9oRcgPT5K1UKpnBKNZflJrkAbBFJTFtG7HwLSZ+pbb7/X5MTk7iiSeewOXLl/HKK68glUohk8kwliWRSCCbzSKVSrH9Z2dnYTKZMDU1hZaWFrz73e+G2WyGwWBYdA+22q3E95lcZVqtFkqlElarFVVVVWhvb8ezzz6LQCCAWCyGaDSK+fl5mM3mgmNlMhnk83nmZltvX7aMkYlEIggGg/D5fIhGo2s6Ti6XQygUwsLCAoLBIPL5PLvB2w2tVguDwQCr1YpEIoHp6Wkkk0mEQiEolUr4fD7Y7XbU1NRg796922bM5HI5TE5OIpFIIBaLLfK5xmIxNiBqNBo4HA4YDAb2MtHguJ0g6z8Wi8Hv97NJaLmVaz6fRzKZRDabRV9fH1QqVQFVu9ORTCYRCATYwEcDc7GVtIRCLKUtyOfzCIfDCIfDmJubYzoFcmPwzECpYwJXWB2/38/cnXq9Hk6ns0C/tdbIjlL6BNqPnoNiK13qr1wuh8FgYCxRLBbD2NgYTCYTAoEAVCoV3G432traoNFooFarmSFDx9sMY4auMU30wWAQiUQCPp8PAAp0PclkErlcDul0Gmq1GuXl5TAajeyeqVQq1lZeIEtGAO8yXO87U0wLBVx5nkKhEObn5zE4OIjJyUmEQiHm+spms5DL5chkMgAAlUrFjpVIJJDP5zExMQG9Xo9EIrHICC7Fuq2nHzyWY+V4Foa0euT2S6fTyGQyzCNBBjXNd7x2cSOxJYZMLpfD9PQ0xsfHMTs7i3A4vKbjpNNpjI2NYXZ2FgBgs9nQ0NCw7StqmUwGl8sFvV6PY8eOYXBwEPPz8wgEApiZmUF/fz8EQUBvby927dqFL3/5y9tmyKRSKTz77LOYmprC8PBwgbgxn89jaGgIkUgEMpkM5eXlOHr0KBobG9HZ2Ym6urptZb4INPEsLCwgHA4jmUyuaD96eTKZDF5++WWEQiE2mFwL8Hq9OHXqFPx+PwBAp9PBZDLBarVu+ztwLUDslqDBN5PJYGpqCj6fD8PDw3C73bDZbMygJ12JWDMiHvAzmQxGR0cxNjaGgYEBmM1m1NTULBKi8/+vdDJaShtCIJaBNzzovdZqtbDZbNi7dy8qKioAAENDQ7h06RKGhoYgk8lw+vRptLa24s/+7M9QVlYGp9PJdENkDPCGwkaBXPE+nw/BYBDd3d2Yn59HT08Pc4kR6zQ3N4dUKgWlUgmbzYbOzk40NTVh165daG5uRllZGet3IpFghoNOp2Oi+I1qfykDNZ/PY2pqCv39/Th58uSiMSqVSrG/VSoVTCYTaydJFC5fvszGOb1ez87BBzZsFUPGi9p5g5beHXouiNGMx+PI5XIwGo2wWq2w2+1sfOINOfrZCGy5sGSplcVyiMVieP755zE6OgqbzQaTyQStVrsjNA70kuzZswc1NTWoqamB1+vFxMQETp48idHRUUxPT0Or1aK7uxvV1dWoq6vbdH9zPp/H5OQkpqammMvrqaeegs/nQyAQYPfCZDJBp9OxiT2RSGBmZgbPP/88Lly4gJdffhmdnZ3weDy44YYbYLPZto0JSKVS+MUvfoGLFy8iFAqxgUGtVjPqGbgySOTzeSQSiZKq/2sJgUAAly9fRigUgkwmg8lkgs1mQ0VFxTUnXN4O0HORz+cX6Q0ikQjC4TASiQRkMhmsViszXGgi5SNJaPWp0WiYMDWRSGB8fBx+vx9arRYOhwMej6dgRU20Os+oAStnB3g2qZQORDweymQyJhxVqVRoaGhAdXU1xsfH0d/fj97eXszMzGB6ehoKhQJerxcqlQpOp5O5mTYj6iSZTDKW3ufzYWpqCgsLC7h06RL8fj/6+/vZvaJ3myJigKs6lIGBAbz22mvo6OiAx+PBoUOHYLVamduD1zsVcwetVV/CLwL5Y+RyOYyOjmJ0dJS5wEiLJ5PJEI1GmaBaJpMhlUox1yAZrIlEgum1yGglI4DavBRDtxost734evEMF/2fSCQKRM2CIDAm32KxMEOGf+7Fzz6vXVptHzbVkBFPGmul9Gi/WCyGF198EbFYDHa7nRky2w2ZTAadTgedTger1QoAuOWWW9hAMTc3h+npaczOzkIQBHR3dzPB6WaE0NH1opdjbGwMp06dwvDwMObn5/Hss88iEokU3JOqqio4nU4AV1Z2JCLt7+9nD9/BgwfR0tKC5uZmGI3GklT2ZiOVSuGXv/wlLl++jEgkwgY7jUYDrVbLBjqDwcC0DJtBZ241FhYW0NfXh3A4DJlMxlY8FRUVBT5oCcVRzJil5zYajRYYMjabjblRM5kMstlsQWQYP/EAKDBkyB3gcDhQXV0NnU5XoDGgFSlNAqsxYor9FoM3dOj4ZOi6XC7kcjns3bsX09PTGBkZweOPPw5BEHD58mVkMhl4vV72PPGRTcXOux6RaTKZhNfrxeDgIAYHBzE2NoaFhQUMDw8jGAxieHgYSqWSMY8860iBI4ODg8hms8hkMtizZw8aGhpQUVEBhULBwn75a8EzGqS5WesYLBYPU7+y2Sxj5uLxOAwGA5xOJ1QqFeRyOWZmZpBKpaDT6ZDNZtkYRq4w0s8kEgkolUp2/emZE2tl1no/ViLsLrX44zVGmUwGiUQCCwsLiwwZCrAgY57YRN6QWcrtu1JsCSOTSqXw3HPPYW5uDiaTiVFlK4UgCAgEApiensbw8DDKy8tx1113oampaZNavDFwOp3QarW4++674XQ68atf/QrhcBi///3vkcvlcPPNN28Km5TP5xGNRjE6Oopnn30WZ8+eZSyQzWbDn/3Zn0Gn0xUwKm63mxkyuVwOwWCwQIF+/vx5TE5OYmZmBna7HW1tbXjXu94FvV6/5RolclUGAgEW4tfa2or6+npUVlayib65uRn9/f34+7//e0QiEcRisS1tp4SdA2JNgMKMqLlcDqlUCl6vFz6fjwlbxUyveHBVKpXQ6/WMAezv78fAwABGRkZgsViwb98+eDwe2Gw25kYmgSMN4qtZedK21Ad+ZS52VfF5SsTnIDeZWq1GZWUlTCYTRkZGkM/nMT4+jmg0ysSa1dXViwwIsVtjLRMPaYnGxsbw/PPPY2BgAMPDwygrK4PRaMTx48chl8uRTqdhsVhQWVnJVvU04UciEYRCIfT392NychLDw8OIx+O4ePEinnjiCTQ2NuLOO++ExWIpYCvJjZVOp5lmaCmGa7l7ItZ8+P1+zM7OoqenB8PDw9BoNKivr8exY8eYztBut0OlUiGdTmNiYgK/+tWvsLCwgEAgwDRAKpUKWq2WRbzSNSfDhjeEieEjI2GjF5U8k8k/uwQS/8ZiMabhk8vlcLlcqKqqgsFgYOxmKSaJT0S54xgZ4OoKZGhoCMFgEA0NDWs2ZObn5xEMBtnEtRP0GktBr9dDp9Ohvr4e4XAYzzzzDCKRCEZHR1FfX490Or1kmOdaIQgCW+28+uqruHz5MoaHh+FyuWC1WnH99dcXrDgBoLKykl1PElUHg0G43W5oNBoEAgGMjo5iYWEB58+fRzKZxK233gqr1crYma0yaAThSgh+KpVCWVkZGhoacPDgQbS2tsLj8WBhYQEA0N7eDqPRCI1Gg0QiwfanF347Q/ZXA9IpkBiSBha9Xi9pY1aBYuwhXddwOIxoNMoMGJoQxMYAr7EhvUg+n8fs7Cymp6cRCoVgNptRWVlZ4J4iQ6qY3mA1xgwdi2d1eBfHcu8gv6Im4a/L5YLL5YJarUYikcDQ0BDsdjvC4TBUKlXBM8azMmtl1vP5PCKRCObm5pgRMz4+jrKyMlitVrS2tsJgMEClUjH3HBkBJJgNBoMIBAIwm82MBe/v70cgEMDQ0BByuRx27dqFbDbLDE66Nvy92AhGnGctEokEIpEIFhYWEIvFYDKZUFlZiba2NtjtdpjNZjQ0NECn0yEYDMJiseCFF15AMplkBkoul2MGC99u3i3D94V3lS61MF5O1EvblLoedB7xNiTUjsfjiEQizMgmI1KtVhc8l2LXGO+mW8plthQ2fSSn2PIzZ85ArVbjE5/4xKoT42QyGfzwhz/EhQsXIAgCKisrcfTo0R3hVloJyLqWya6o7i9evAi73Y7u7m54PB5UVVVt6Pmy2SwWFhYwNDSEp59+Go2NjXjb296Ghx56CLt27YLZbF5kufOTulwuh8VigdFoRFlZGXbt2oX77rsPly5dwsTEBH784x/j6aefxvj4ONrb23HfffehpqYGHo9nQ/uxFPL5PCwWC44fP47du3fjlltugUajYZEXmUwGExMTzE/NZ9aUyWRoaWlBW1vbNWHM+P1+lgjv3LlzTJvx4IMPYt++fRsS2vtmhdfrxczMDMbGxpBOp7F3715YLJaCbbRaLRu8yRWg0WjYdU8mk3jhhRfQ29sLuVyOiooKHDx4EC6XCwAKjE8SRgKrYwD4pGRkSIlZHbFwn89lRduIyw8IgoDq6mrE43FYLBa2UFEqlbhw4cKi1Bar1fQUQyqVwtDQEHp6enDq1CkAgNFoxN13342DBw+ioqKCGYl0vagflJOnrKyMhY8fOXIEoVAIJ06cQF9fH0ZGRnDu3DlMT0+jpqYGR48eRWtrK5qbm9nCkT/+WsGzIHRdKB+R0+mEXq/H7t27sXv3bhw/fpzpfNRqNTKZDMbGxjA1NYVz584hlUoxtkwmkzFtCT9O031cLov0WkHPDf+8yGSFCSD5xILAlbktHA7jtddew9mzZ3H+/HnEYjGo1Wq0tLSgtbV1UZbiUteS7+NqsemjeDqdRiqVYpRtWVnZiv35tPKIx+PsppeXl7OQu50g8l0JjEYjbDYbC18mkdvk5CQsFsumGDJTU1Pwer1M9V5fX4/q6uoVGZH0cioUCqjVaqjVahiNRgiCAJPJBIfDwUR2ANDS0gK9Xo+qqqoNFwQWg1KpRHt7OzKZDFpaWlBTUwObzVZA9abTafT29mJwcJDVMiHI5XKUlZWhvLz8mniG4vE4+vr6MD09zZglmUwGp9OJsrKya6IPOwHFBstEIsHElyTKJC0MDdrFdBb0fzweRygUwszMDHw+H6xWKxwOB+x2OzN0yJ3BuwTWqmUgBobPrSI+FrWT/+FdQrwrhOr7mM1mJviPxWJM/JxKpTZMVAqA5U0JBAIs6tDhcMDpdDJmiMZ2foKmyZMMMbqW5AY0m81obm4GcMXwDwQCGB4eRjqdZmxzXV1dQR2jtbqUxPvyzxO1x+PxIJvNorOzEw0NDbDb7SxzcjKZRDQaxcTEBGZmZhCPxwsMCKVSCbvdXpDTh3/mxO0o9RxsBMTH5Z8l4Mr9jMVimJ6exvz8PKLRKEsCa7VaYbVaV2Sc8NdzRxoy5MusrKyE3W5HRUUF9Hr9ihsbCATg9Xpx7tw5zM7O4sEHH8TevXs3fbLcSDQ2NsJqtaKlpQXpdBqjo6Pw+Xx4/vnnYTAY0N7evqHnC4VC+NnPfoaenh7k83nU1NTgpptuWrMrjoyZ3bt3o6WlBRMTEzh//jy+973vYX5+HnNzc3j44YfR0dFRQIVuFkwmE77yla8AAMrLywtWDIIgYGpqCiMjI/jc5z6H6elpJmwmKBQK7N69G3v27LkmGBmv14sf/ehHmJ2dLZiMJawcvH6Afz5DoRD8fj80Gg0sFgv279/PQngp9JfXgdEKla7/2NgYRkZGcPHiRSwsLODee+/Fvn37UF1dDeCqBieXy0Gr1W7Iu1GKSRBrenimhgTvZCBQro9MJsMYjqamJuRyOZY1mo8IFAta19IPQRAQDodZfhXS5Fx33XU4duwYGhoaYLFYkEwmmbCVDAW6Fw6HA2q1uqDUAmUnPnr0KHbv3g0A6O3txaOPPsrOodVqUVdXx5IcbtT7I2a8bDYbtFot3vWud0GlUrGcYQqFAoFAAH6/HzMzM5idncWjjz6KiYkJ5PN5VspCLpdDo9Hg4MGD6OzsZG57CjkXj1dixmQj5sWljsG7JgVBYEb8K6+8gqGhISQSCVRWVqKiogJ1dXWorq5e9KwUM8bW+15siSGzsLDAUqivljqanp7GwMAAIpEIFAoF00FcS9BqtTCZTKiqqoLf78f4+DhUKhULEdxokFslEAiwMNCqqqo1uyD41YZGo0FHRwfkcjl++9vfsjBtCt8kQ3UzIZfLYbPZ2EsVDocRCoXg9XrZ9aUVMiXKI8rWZDLBYrGgoaEBtbW1OyKZ4nLIZDLw+XyIxWLM9+xwOFhis2vJqN9OiHUpJFJMpVJQq9XQ6XQwm83snSRRKGVipf34MWxubg6jo6NM41BTU4OKigrI5XLmZuAZgI24V6TxoBU+5XkhLQJNdvyzTakIeEOYZ4mUSmVBmCxtQ9vzZVfWo40hHYXP50MkEmHvcm1tLTs3H/XCXzNy4dB949sok8nYfauvr0cqlWILN8qyOzg4iKamJthstnUbM+JoHgLpDquqqth4GQ6H4fV6MTIygpmZGUxNTTGxcyAQYMejopFWqxV1dXWoqalhxrf4XlDf+d/A6gwZsUuKd1uJWbxS+0ejURZCT8EUNpsNVVVVBe/SeiPdlsOmGzI+nw8zMzNMJ7JaXL58mSUvs1gsOHToECorKzehpZsDesHkcjlaW1uRTCZx+vRp6PV61NbWMqHaRiKdTmNgYICJ4dxuN1pbWzeEfVAoFLjxxhvh8Xjw61//GsPDw+jp6cHg4CAuX77M6stsJsiHTCGYU1NT6O3tZRoSSso0Pz+/KBlZWVkZqqursW/fvmtGI5NOpzE7O4tMJgO5XI7KykrU1dVtmiH8RoV4ACVRfDweZ0aM1Wplzy/VJDKZTGx7AAWs49jYGLq7u5FKpWA0GtHe3o6amhoAVw0hXpOxESCGhX7INUYTNGU75w2Z5Qz2ZDIJh8MBs9nMJjRe+0Eh6Dwbtdo2UxCBz+fDxMQE/H4/lEolKisr0dnZCZPJhHw+j0AgAIVCUbIOGhlE/Ngqk8lYNuK9e/fCZDKhvr4ePp8PXq8Xly9fhtVqZdGaG1FjjTfQyODgXfHAlWfO6/XixIkTOH/+PAYHBxlLPDU1xZIX5nI5JJNJVFVVwe12o6uri43Z5G6i54fC/nnt0FraTe3jwf9POcV4wTpw1bDJ5XIsSmtqagqhUAjAFSOura2NZYXn992sRdem55EJBAKYnZ1FeXk5XC7Xqi96MBjE3NwcKioq2Gp/s9Lk0wBBGpbx8XFMTEyw7+lBLSsrg16vh8lkQkVFBex2+5LHJR/n6OgoRkZGkMvlNtWQEZ97o9PYy2RX8ubs2bMHANDT04OLFy/ihz/8Iebm5tDY2IjrrrtuUw2adDqNcDiMixcv4vz583j66acxNTWF+fl5Vkyx1IqJXJs7PbdMPB7H73//e5w6dYppGQwGA+644w7ccMMNLKJMYmRWBj7agu57PB5HOBxmrhZeX0KuIKLRafJUq9UQhCtlMubn5zE9PQ2r1Yry8vICDSBpzPjkYattL/1Op9NIp9MIBAKsaO7CwgLTmeTzeTQ1NUGn00GpVDIWmIwTXihKv+mH3DiRSKRA41BeXs4mIjKE1pqNNZvNshBdkhskk0mW+4ncdzKZDAaDYZGWiN5pvV4PuVzODAWlUsmizqiAMDEC+/btw/DwMM6cOcPG8fLycgiCwCJKxTqZ1ehmxJopEmELgsA8Ed3d3ejv78err76KyclJzM3NMe0Rvx/9JmaQtFekq6QFCxk9pDmle7LRLiWKgOPdk5TFlz7PZrMYHh7G6OgoKxlksVjQ1NSE3bt3s6g9sRGzkuip1WLTl6OhUAiBQAA2mw0Oh2PFjabBhB6IsrIyVFVVsWyNGwnesqbEWJOTkzh9+jTOnj0L4KovUq/XM80L6TP4RFk8+Bcxk8lgenoa09PTLGV4ZWXlloTP8uF66wVP47a0tMDv90Mmk2FoaAjz8/PQ6/WIxWLo6OjYNEOGJpFQKIS+vj689tpr+M1vfrMio4RWxxR2S8nzNiIaY6NA/Ugmk3j11VdZtB6t9A4ePIi77rprx9QZu1Ygfj4EQWA1x/g8FvQdra5psk+lUlAoFNDr9cywIFqdxgOe0eHdAmtd/dM4SOGt09PT8Pv9zE1Bgn4ydqhGlNFoZAJXagu5kKg99C4QcxSNRhGLxSCXy6HVamG329kEyhsWa3Ff0ERI1zsajbJ6SRqNpqD6Nm9g0P5U+V2j0bAkeQRyb5BRRBXuW1tbkUqlcObMGczNzSEUCmH37t2wWCxobGxk95YMkrXeI3EeH5q3KDP60NAQuru7EQ6HEYvFii6yeO0R1cEKhUIsjw/fX2K3eFfhalBqH3Gb+LGFLx7Ku2UnJiYwNTWFaDQKlUoFs9kMj8eD5uZmmEwmqFQqNsYud971jL2bbshEo1GEQqFVC03D4TBLUz04OIj3v//96Ojo2LTBOx6Pw+v14pvf/CYmJyfR39+PcDiMSCQC4ErOjg9+8INQKBR48cUXAVxd3Zei9ysrK1mtlUQiwZLKid0d1yK0Wi127doFr9cLAKyS68LCAkKh0Kb2MZ1O4/e//z0uX76M7373u6y43EowMjLCSkdUVFTgzjvvRFNTE2655RY2ce0EzM3NYWpqCq+99hpGRkYYm0BiQFqZSlg5eEaGhK7EEABXJxE+UoTYCgAF13xhYQHj4+MIBoOQy+U4cOAAmpqaYLfbodfrC2j49QzQlJbe5/PB7/fj9OnTmJmZYWn8A4EACwWPRqMsD0wqlWLCS7vdXjD5kADZbrejvLyctffUqVOYnp6GxWJhBhkJScV5QFYLupaZTIYZgZTIje4FjRnEeJGglzcqi9Wo44XcBHJZjY2NIZFIMNbK7/ezDM6824b+XmnfaGHLhyPzbqKBgQFcunQJzz77LPx+P3w+H8uBQ88hMU/A1bDt2dlZli7C6XSyWlK33XYbq7RObd3siuT8taBoLHqO+vv7MT4+jmeffZZd44qKCrS3t6OzsxPNzc1sXizlDdhI3cymGzJEKdKDu9yqmR7caDQKr9fLEuzU1NSgvr5+zT7a5ZDL5RCNRnHhwgWMjY2hr6+PrViI7iNtTjKZZJEIpfqQyWTgcrlY1dNkMgm/38/Ep+QTJX/nRgkB+TYQ+JwS6z0H9Y0GRLoG9IKudZWwGlDxuMnJSQwMDBQUYRO3VUyXRqNRVlqeQmVlMhluuOGGHcNuCIKA2dlZjI2NYWZmhtXEIuE2JfiSsHqIk3BlMhk2oQKFLCptQxMsb+BEIhFMT08zoXB1dTU8Hg9zR9F7UCwNO4/l3hVqA7kaiNWdnp5mDApN8MQE0Vg2Pz/PWCMAbEFFBpzdbkdlZSVb7c/PzyMSicDpdEImkzFdjDi3yHquebEfmiN4cSl9TteIdEn0Oc8OkfuP15rEYrEC44mSSVJ/6IdnQlbC6IonX2ornZu2IaZuZmYGkUikaGFbnqmj60tGzMDAAObm5ljeGWLK+Jwsq50zSrl2xExksWPSuWjumpubw8TEBCYnJzE/P88SD7rdblY+iI/022xsWfXr4eFhdiGWAlG4vb29+PWvfw2tVotjx47h+uuvR0NDw6YN4JR7hF5mjUaDhoYGNDY2Ys+ePfB4PDh+/DgTvJH2pxgikQiGhoYwOTmJ1157DYlEghU4o/4Hg0G8/vrrEAShaPbDjeoT5eEJh8PM37reYw4NDWF0dBTf+MY3MDo6WuDrra2tRVtb25YwG6UiB/jvlqKMQ6EQfvrTnyIajeKBBx7YtorkYmSzWXz961/Hyy+/zFbX+XweBw8exMMPP4xdu3ZtdxOvefBGChnkFOJLEyPp5WgVrFAomJbk/Pnz+PnPf458Po+GhgYcPXoU9fX1rGQBiXz5Z4+fmHkjZynQRDw3N4exsTGcOXMGCwsLCAaDqKioQGtrK1pbW+F2u3H33XdDoVCgv7+fVV+myZLYAIrSIo3K9PQ0K9ZIrIHf78fQ0BBOnDiBgwcPsuJ/4urfqwVNxFqtltVBI8Z+dnaW1eSh6xwKhZgImybDQCCATCbDCmBSdmudTodUKoWFhQU8//zzmJ6eRl9fHwYHB5lRI5NdiVqkEGwyHEgkvBbwQlz6IcGy3+8vacSQ+4k3Logxk8lkCIfDLOIpGo0W1LgjxmqjF75k2BGDwi9OyRVJLs6XX34Z3d3dGBoaYtFKlJxQnJ9L7HrbDMNm0w0ZEnL5fL6CKsRAoaVGq4VwOIyhoSFcvHgRQ0NDqKurg8vlgsViWfeLVAr5fJ6FxVGYd0NDA7q6utDZ2YnW1lZWmC+Xy6G+vp4lvSoGMoQoR8Do6CijrwnhcBgXLlxgD35LS8uyouGVgsRwRDVHo1H4fD7GLq0WNOBPTk7C7/fjwoULGB8fx8jICObn5wGADVBlZWWorKzcUsaANEB6vb7gGaGXkJJwiQeUfD6PWCwGv9+P4eFh1NXVsQiV7QLR7JSCnSZEp9OJqqoq1NTUSGUJ1oBiAkNyJanVaoRCIcZ6AFdyFdFzRQuMYDCIcDiM4eFhDA4OYn5+Hh6PB5WVlYtCTfkVLH1GPytZsIiZI5rwfT4f0uk0jEYjqqur0dLSgsbGRpSVlcFut0OhUBQwQwSapCgSa25uDrOzs5iYmGALHZqUKbqIIn3sdjuru0Q5UfgorOVcBLxOiMYJo9GIZDLJ3Pezs7Ms2ouuO52LF2ATeDaDGO/p6Wn4fD709PRgbm4O8/PzzKVEUbM2mw12u50tHOm6rEanwW/LM3m83oa8EIIgsGzEOp2OZYmme0HjE42xpNujyT8YDMLv98Pr9bJM0SttZymI9+X7U2xhyH9Ggm2qWE5ldqqqqlBbW7tofFqK7VkJsbFSbPpsU1ZWBrfbjf7+flblsxhyuRympqZw4cIFfO1rX8Ps7Cy8Xi/uvPNO3Hfffayg4WYgm83iueeeQ3d3NyYnJ9HQ0ID3vve9OHr0KK6//vpFIrfrr79+yRuQSCQwNzeH4eFh9Pb24vvf/z78fn/BNiMjI/jGN76BxsZGNDc349Of/jSOHDmyIf1Rq9VoaGhg13R8fBzd3d3Q6/UlhclLgQyBH//4xzhx4gTOnj2LUCjEXlQAsFqtqKysxJ49e3DddddtOrvB3xO1Wg2DwYDm5uZFWZJTqRT8fj+mp6cxNjZW9BhTU1P4wQ9+gHvvvZcNptsFGih4l6PD4cCxY8dw4MAB1NfXS4bMGsEPpMSwUHXeyclJzM7OYmBgAB6PByaTCVqtlhknyWQSPT09uHz5Mn74wx8iFoshkUjg2LFjOHz4MKxWKxQKBXMv8LoAEmaShgJY+YRJ7x4ZHcPDw7DZbLj++utxxx134LbbboPFYmFtlclkLMvtwYMH2UTNF8GkqvavvvoqS1GQSqVY3hYSFf/0pz9lZT5uvvlm1NTUsIzF5H5aKfhcNU6nE9XV1RAEAV6vlzHXjY2NrDwBn66DImSIDac6UdSfYDCIqakpPP3007h48SLOnj2LRCIBp9PJDFOLxYKKigq0tbWho6ODpW9Yq95HEIRFZV14w46eD+qHw+FAU1MTqqqqmHvG5/MxY44WWhTGzDM7k5OTOH/+/KpL+6wGxVyrxfoWCoUwNTWFsbExjI+PI5vNwu124x3veAduueUW3HTTTWxOoOed3HbF5syNYmc21ZCRyWSora1FLpfDL37xC4yOjuLkyZNwu93weDysE0SVnjhxAgMDAxgfH4fRaMTevXtRXV3NBonN8rXlcjkWxpvJZGA0GtHW1oby8vKizEKpdvCRSSdPnsT09DTLLFlWVoZDhw5Bq9VidHSUFWEMBAIYGRnBiRMnEAgE0N7ezujPtWqBdDod9u/fD5lMhvPnz2N4eBi//e1vGQ1ORcuoSi6/euTpxUwmg4WFBQwMDOD8+fN45ZVXMD4+DrfbjcbGRtTW1mJ6epplKC4rK2PU8GaCIhscDgd27doFu92OxsZG1NXVFeQYopURCTP7+/vR09ODmZmZguPlcjnE43GWN2E7MTExgZGREZYLRxAEOBwO3H333Whtbd2S6/tmAD3vNLECVwT/VHDQbDYzl4HP58PCwgJOnjyJkZERjI6Owul0orGxEQ0NDaipqVnEFvODv7h2zWrea5r0otEo0wtqNBq43W5Wb4gifsS5VcgFAVwx3LLZLMbHx+H1enH69Gn09fXB5/PBZDKhsbERLS0tUKvVmJmZgd/vZzlPzp07B71eD6/Xi8bGRuYKpx/xQq/YdSZDxmw2I5lMwuPxIJPJYGRkBIFAABcuXEBdXR0CgQDq6upYPio+dQQ/FpNrZmxsDKOjo+jv78f58+cxPj7OIshqamowMzODyclJOBwOuN1uxpzx7j3+91L3gX6XStvA32+n0wmPx4Ndu3bBYrGwSJ7q6mpmpFDkVjweZ5o/0vPQOchNSYYRZfjlSzisJ48M327xsegc/AKA2Guv18sixWgcdrvdBSyX+LlfjRG/Wmw6I9Pa2gqLxcJqC/3617/G7t27CyIA+vv7MTExgUcffRQzMzOYmJjAkSNHcPPNN6OhoWHTc63kcjm89tprOHPmDIAr1vvu3buXdPUUe5AzmQz6+vrQ3d2Nf/3Xf0UwGGS+bJfLhQ984AOw2+34zW9+g97eXlZNOhwO4xe/+AW6u7vx/ve/n9UOWisMBgOOHTuGXC6Hxx9/HJcuXcLIyAiSySQ6OjpYaCYVj+SFbry4L5lMYnJyEr/73e/wne98B8FgEJlMBvfddx+am5txzz334OTJk3jhhRdgMpngcrkK0rlvJMT0tdFoREVFBY4cOYKWlhbccsstcLlci6Lj0uk0IpEIBgYGcOHCBUSj0aKGDAmvtwvUv6GhIbz00ktMzAlc8T3/wR/8wY6JqLoWIZ5oaXAmwatMJkM0GkVPTw/S6TQLPVYoFLhw4QImJyfxm9/8BjMzMxgeHkZZWRm6urrQ1taGxsbGRecSD+JirdZKB3M+kVwoFGKlDmpqaliFbeCqwBe4yjaJF2HpdBoXL17E4OAgnnnmGRZNQ9mIKaS/t7cXvb29TOc3PT2NdDqN6upqJBIJuFwu2Gw2GI3GgjFkKZAxQjXRmpqakMlkcPHiRczPz2NsbAwajQZDQ0M4fvw4c1ETOyPuE0VIvvrqq7h48SJOnTqF2dlZJBIJXH/99aziNC3CysvLWd4uPpRZXGKgGMTuQT7Io9g8oFAoGHsSi8VQXl6O9vZ2NDU1wePxsGvBi5F7e3tx+vRpzM3NIRAIsGASXpwdj8eRTCZZeDoxHrxRA6ysxIDYnVTMyChmnHq9Xly6dAlTU1Ms4WpFRQX279/P3iPeoOPB/7+REUvAFhgySqUSer0ehw4dwujoKH73u9/hzJkzePrpp1mn/X4/UqkUK9sejUbR2dmJe+65Z8MLKhaDTCZDdXU15ufnMTU1BZ/PhxMnTuC6667D3r17i+5Dvk3yhY6NjcHr9eKHP/whpqamkE6n0dHRgY6ODnR2drJsslSg7eLFi8jlckw4OzQ0hNnZWQQCAXR1deELX/jCmksKkGspEAjgjjvuYOc4efIkenp68Morr8BisaC2trYg5JhWOblcDhaLBbFYDIODg/D7/bDZbIxePnr0KMrLy1FdXY2+vj4AV1xLtbW1m1aJmaLe6KXt6upCc3Mzdu/eDbPZjMrKyqJ5a+RyOfNPk49fDNLXbKfYNxgMYnp6Gi+//DKeffZZLCwssJUdhc9KWDt4jQol9qIsyTKZDOXl5ZiZmcGpU6fQ3d2N3/zmNwWTHF+uoKamBi0tLdizZw9bcCSTSQiCsGkJCmmyISaV11EAYEJWAAWTPU2AVHT08ccfRyAQQCAQgMPhQFtbG3bv3o2amhrs3r0bGo0GtbW1qK2tZe//yMgIi6JxOBzIZDJoamoCABYSvJwxQBMujX8dHR1QqVSYnp7GyMgIxsbGcPnyZUxMTMDr9cJgMLDFLr3D4tV+IpGA1+tFLBZjaf3NZjPuvvtulsxPq9XiySefhEqlWmR0UHt4Lc5y94CPYgOuZm8mI8Nms0GlUrFikZ2dndBqtcyFybv46Hmk68ezLnzCPK1WC5fLxUL7+bZSm1bCdohZJeBqWL3YwBF/trCwwFyAr776Kks7QLqYsrIy5vKma8Rrhvi28ce9JlxLwNX6PK2trchms3j99deRy+Vw5swZNpgQXXrw4EEWEVRVVYWurq4tmVzkcjnLHEwh3729vaiuri4Qg/GFysh3HYvFEIvF0NfXh7GxMZw+fRrRaBRqtRr19fW45ZZbcODAAdTU1DCBXEVFBbRaLfr7+xGLxTAyMsLEU5OTkyxB11qhVCpRVlaGmpoa7N27lwnhKELhwoULMBgMaGpqYop9MiqDwSByuRycTieSyWQBVXvgwAHs27cPXV1dbNCkh5eK7m30/aJrn0gkmIiaxGUrFU2KB30xlEoliwbYLkSjUYyOjrL8E/F4HAqFAna7fdMZyTcLeGOGJlaLxYJ8Pg+73c70CJRbhrarra2FyWRi7g6lUgm3211QH4hSS5Dbgs63VFsIKxnMycVCCUEpBUKx1AO0mqfQ61AohJ6eHgwPD+P06dPIZDKwWq2w2Wzo7OzEvn37UFdXB4/HA5VKhfLycigUCvT19SESiTBRPxV8tFgsiMfj0Ov1ixIJFkMxt1pVVRWSySTq6+tZqDhFToXD4UVsEkWOEWvKZ13W6/Ww2+1obm6G2+3G9ddfD6fTiVQqhYmJCWYokFaJT3dBbM9y7hn+2aH96e9kMolQKIRgMAiFQsEyvi9n3PFGEbEufJg4PX9qtRp2u71gjBKzROJrvZJ+0P/88yo2aOizcDjMFtxDQ0OIx+OQy+UoLy9HeXk5y6pM+/DjbTGDZaNEvoQtMWRMJhPe8573YHx8HBaLBdPT0xgeHkZ5eTkcDgd2794Nh8MBl8uF6elp/PznP0dFRQWbYDcbKpUKb3/729Hc3MwGshdffJHVKAqFQohEIjh58iTC4TCzwKnyJxUnlMlkTDvylre8BR6Ph4kzeZW/XC5HU1MTPv7xj0OtVjM/cTqdhtlsLgg3XA/q6+vx0Y9+FO9///sRjUZx5swZptsJBoMYGhpiqc5ra2tRUVEBk8nEit+53W4cPXoUFosFFosFNputZC2lubk59PT04Pbbb193u3kkk0ksLCzgV7/6FZ588klcf/31qK+vx/Hjx1kq+KUwPj6O73znO7h8+TKrwyRGTU0N3v/+97OKxduBoaEhfP/738f58+dZuKjRaMTb3vY27N69W0p+twEgtwAxe2TAqlQq3HbbbWhtbUVZWRnLkUERNrfeeiscDgcrjDc3N4e2tjZ4PB5myIjDTekcJHYkVwI/ES7HAtBEq9VqUVVVxSZmjUaD3t5exONxDAwMsGPG43EkEglMT08jHA7D5/Mxlwy5hRsaGlBdXY277roLNTU1aGhogMFgYEUU8/k89Ho96uvrcf/990OlUrFjZrNZGI1GFh1ITMZqc8wolUrY7XaWoXf//v2s8Gw4HMb4+DiLZOKFoxQBpNVq0dnZCYfDwSK2XC4XzGYz9Ho9KyhLLmMqWiyXyzE/P49AIMB0l3wW4ZU8P3zUGQmjx8bGcP78eVy8eBFWqxVmsxnHjh2Dw+FARUUFYwF5LWImk8HY2Bjm5uZYGYPu7m54vV7WHpo7XS4XWltbiyaV5Y2Q1dyD5aK0qK2kH5yYmMBLL72Ey5cvs2fBYrHg8OHD6OrqYuwZubt4Q5R/3vnPxILp9WDTxb4A2ApGoVAwo0Wr1aKiogJOp5NRtLlcjiVq4lXpmw25XA6Px4N0Oo3m5mZEIhGm3L98+TICgQBCoRBLM03UH++3zOfz0Ol0aGhoQHNzM7q6ulhZhmIgg6C+vh5NTU2Ym5tDMplEZWUlPB7PhvSdNzposCsrK4PVaoXP52Nhvmq1GlVVVaiqqmIPIYX67t69G0ajcdlop0QigYWFBVaVdqNALxIp900mE+LxODweD8s+Sj56GlTj8TgLySRtzODgIIaGhgqOTZS12WxGVVXVtoRek4syFAphcnIS4XCY6SDMZjPzq0uupfWDJmrg6sBPA6nL5YJKpUI0GoXT6WS5SfR6PTo6Ohhzo1KpkE6nGUPDV5qmYxaj7umc9B2va1hKJEthyDabDWVlZaioqGDjpNfrZat30sjEYjFMTEywtP1WqxVGo5GVHairq0N9fT06OztRXl5eII4ndoeigqqrq1FTU4Pa2lr2vc1mY6kwKDlpKaEv329x36jchsvlKgiLDofDUKlUCIVCTKxM7iU+q3VDQwOcTieamprgdDqZnoYMFj7ZIQloE4kEc93QvVqpnq+UWJvGJ4pSpRw5LpeLvctkVFHbKAS+v78fMzMzLFrW6/UWpIiQy+UwmUysmKk4g/xyepbV3I9SnwuCwGpZUTkMKpBKNa3KysoKXFRLubl4V9NGYsuSfZBv+eGHH2aDN+//jEQi+O53v4tQKITm5maUlZVtVdMgl1+pTF1bW4uGhgZ0d3fju9/9Lk6cOIHHH38ckUikIC+AIAhMMPuOd7wDN910E0KhEJRKJa6//nqYTCY2eCyHt7zlLSzVfzKZRHNzMyu2tdHYt28fdu/ezRKAkWo+kUjAYrEUGCtkFKyUFSMRbaksu2tFOp1meRQo4kKhUOCrX/0qbDYbjhw5ArPZzFwwer2epXE/f/48EokEkslkUVedSqViK1RKsLXVSKfT8Hq98Hq98Pv9SCQSAMDC8u+8806Ul5dLhsw6QZMnGRDiytAtLS1oamrCgQMHEI/HEQwGmbGiVqsRiURw5swZxGIxmM1m9h1BnL+Ij9Qh8An4xFEdpUC1hXbv3g232w2lUsnCxInZjkQibLKhCBdyb7S1taG+vh7V1dWw2+2sMjQfFUntjUajTLOh0+lQXV2Nw4cPw2AwYHZ2Ful0Grt372bh01TbaKWJ/cTXXaPRsEKbfLbdaDTKkttRAUw+VxRfRoJ+k2YmkUjAaDQil8thfn4eoVCIMQXkng6Hw2tK50H3imfXKZHi1NQUuru74ff7kc1mceHCBdjtdtTX17MSEkajEVqtlkX8UHJDqoItzr6uVqvR1NSExsZGuN3uRQst3nheLUrdM/Fzmc1mMT8/j8nJSbaol8vl2Lt3L1paWnDw4EGmM6M2iVEq8ncjx7QtMWT4TpYqJBiNRlmF0r1796K8vHwrmsbaRy9MZWUlgsEgurq6WApmq9XKcg7wxR4tFgv27t2LhoYGlkKaD4dcyY0ymUxwu93Q6XSsrMFq6M7V9JEiAIArA4tarWZUu06nW5PxRIwG+VGpgu5G1QKikE2Xy4WmpiYWzUMrroGBARgMBlZcTavVoq+vD4FAgGUqFV9LmUzGspW2tLSgurq6IPX3ViKTyWBqagpzc3OIRCLMJVFXV4fW1tYNycYsoTA0lg9fJf0UTY68sUMTD02Q9DxVV1ez1XGpUFMxUyHWMazk/ebbbLFYIJfL0dzczHKgkP5Fq9UilUrBYrEwQTAlsfN4PKioqGA6BofDwcTC1E6afPjoRWIryH1jt9uRyWRYYjzS6lBfSvWnmMCU+sT/TZMdGTuZTIYlraN7QoyMXC5nCyaayGUyGQu+oHPSvkajkY3JNN6thhXgI3v48wFXxj8yDMvLy1liRXKTJRIJ5qIkYTRVMJ+ZmUEsFitgYeiemM1m2Gw2NDQ0oKqqqqSGby0GwVL3ithC+j+VSrHipGSkabVaVFdXo7a2FhaLBTqdbtXP90bPbzumYAtVKaVSANtB89OLS0rz0dFRjI+PF3xfXl6O++67jzEVfIbJtcBkMjFjZishk8k2hPXR6XTweDwIBAJMUDw7OwuPx7MhE7DBYGAp4LPZLH7605+y1OvxeJxVJyfw1GWp1YpcLmeutOPHj6OlpWXbksyRdqm3t5eFhSuVStx00004dOjQpkWBvVlBkyFw5fmg/EFWq5W9xySqpYmdRPj9/f0wmUw4cuQIE2CTW4ePViKWudi4wBs5K4VMJmPMRXV1NXw+H6qqqjA3N4e5uTlEo1FW68ZqtbKFILmExOdKp9MIBAJMLEvGMj1r/PbV1dWorq5mtZDWEpVFEyQZEbxLjwwX/lpRJGWpdzKfz7NEd6Sz0el0TLtIRh9dD2IMqPQBZZhfLYrdO/I07Nq1C6FQCOFwmGlK8vk8ent7C4xFcjMtdX6KBvJ4PLj55ptRX1+PsrKyRczGWoyBpYwMuke0CM9mswiHwzh37hx6enowMjLC2O+9e/diz549KC8vX2TUr2QR/4YzZGgwiUajbAAgOnerQReXxHXkJ+a/J+ueX3Gt56Zsl8tgo85LoYFUFJMycG6UH5RWaw0NDQCuFLaz2+3o7u4uOiAtdV6ZTMZ8/G9729tQX1+P/fv3M5p5O+5FKBTC7373OwwPDwMA7HY77HY7PB4PXC6XlPxuDSgWeUHg31dyN9FESi5vPgMvAOaepHT9VVVVzJDhmQbah4+M4SFOMLZSg4bfhhhKyoIbjUaZJox0PS6Xi2Xp5ftDrKxCoWCub/EkS1E4fBkCEmWK2YiVgh8n+X3pfKRno+tI94TqVmWz2UW5amghyVfEJkaNzkPf0XGoJAmxJqtxnfPgF0gkGKdcQpQ35uzZsyxDN81rFDVF991kMjEXPz0XFABz7Ngx1NXVoaurCw6Ho6Bg6Ua5ZXi2kC/YSfc8Eolgbm6OCXwBsLGpoaEBtbW17NrzhTzpmPxv/nybMc5uuyEDXMmoyVf33C69AoEsfL62hYTiIENmdnaWGTEbmViOVlJ1dXWoqqqC1+uF0WhEf38/EolESaGa+BgEehHvvfdeFgmwnRFB4XAYzz33HGKxGGSyKwna6urqUF1djYqKCsmQWQd4cS2Bn7gBFBTgI3el+FmiaCCbzYby8nJUVVWxiDn++GI3SrGEYLwhsxaDgAwZqvtGhkcul2PCd/685G4hw4mimIqJ92lFTvVz+Nwrq2WR+DYv1V9BuFqXiHRsFOXJF+Dkc9XweideAKtUKhljRNeKfqiuEdWsIpfPSgowLjW2kFyioaEB5eXliEajsNlsGBoaQjqdLjBkyLCk/Ww2G2ORiM0oLy9HfX09brrpJpaHhmev1qqJoXtRqn9icXo+n2eRb319fYwtpvGpvr4eHo+HGbjE5Ihz3JQSFm80tt2QEQQBY2NjmJqaQldXF+rq6raNpZCwNvC5CTZDkQ5crZx7yy23oLm5Gc8//zyAKwM1RQEUOzcNIlarFQ6HA//pP/0nHDhwgGkNdtqzVllZifb2dlbTZqe171oFz5LwE6uYoaGBmAwCQbhSD2h6eppFalitVsYYk4GwEtC5NpKtlMlkbDKnSYh0P6S3IgaZX23ncjn27mi1WlY8k4wZPsJrIxgA/prSNSM3BLmHCLwxRm4+sSHFjzVUp4g3cEikurCwwMYGmUzGok3D4TDLcbbSEHI6JzHOfFSZTqeDUqlEZ2cnS3g6Pj7OIiVpPzKUc7kcvF4v6wMVXbzjjjvQ0dGB/fv3syKgAAqM5fUYM6X6RYY3byzSu5DJZJiRaTQaUVZWxvRjvGFZ7DnZKPZoOewIQ2ZhYQELCwtwu92oqKiQBm8Ji0ADW01NDUwmE6vVQtkwARQMwvQiUsKsiooKeDwe7N27FwcOHGD1YrYbtDqmgc5qtbJSD9vJSl7L4I0VHjRp8PQ+bU/f0yRL+hii/qPRKHQ6Hcu5wucSKaWF4c9Z7Dvx32IU21dM2fN6E/qc3gN+lSzWLfBuHV4cS33h3RhLtX+lEE9opa4b32e+XWK3Bf8/sUi8AJncSOR+pvGA+kwJBYsxZ0uBrhufzI5YLoVCgYqKCgBAbW0tBEFAIBBgbJA4GR/1Ta/Xw+12o62tDZ2dnejs7GRh6XTOzVog8v0Sg94DMnI1Gg2MRiMLv+eZzaXeAf7vzWJpdoQhMzg4iJmZGRw7dqwglEvCtYFSPvDNgtlsxle/+lXMz8+zGitUjXV2dhYOhwMmkwltbW2wWCyorKxkUUBUa2WjEjGtFy6XC4888ggGBgZw9uxZHDx4kCUhlLA+8M8k6TzEz6m4MjWf5ZW0e6R1oCSRvBFUbDXPMw6k71jre0GDPrWT3BWkY6GkaXw0IpVgMBqN7Lz8pE0uJorU5A060tGQMJfaQNdlre/Napgo3sVFhoNWq2UibZnsSmQWzzqRrpLucS6XY0lGKysr2UTscDhgNBoLGLfVRjABV64J5fBJp9PQaDTQaDSoqKiAzWbDhz/8YczNzeH111+H3+/H9PQ0/H4/wuEw60tdXR1sNhtqa2tRXV3NRL2UpJHOQc/Tati/tYAMLXKDGY1GuN1u3H777fD5fPB6vTh8+DB27drFgnF4Jn4r5wAxtn00J0YmEAgw2lYyZK4dUH6GfD5f4JPeTCgUCtTU1MBisSASibAkfw6Hg4mBjUYjmpubYTKZUF5eDrfbjZqamk1t11qg1+vR2dkJvV4PQRBYptKdwBZdyyi2eudZjFITK2+UkH6BaHWdTreoBIHYCOA/F7tbV8rErLR/S00a/ORHBhWlXOD7zzM8PBsjvkbFXMer7UOp44nvS7G/gatRMcDVYo88uyY2GMntVlZWBpVKBY1GU5DIcCXJ/Ph28+3hnxO+jA2xM263GwaDgeXDcTqdCAQCiEQi7DnyeDysMrbT6YTL5WJ5g+gaiduxWXOj+FqTQWsymdDS0sKSJzY2NqKysrIg8m+p4xE2m1HaEYbM0NAQJiYmUFFRUZBpUsLOB6XopqgOq9UKi8WyJSJVs9mMG2+8sYDu5ScufqDaTkHvUrBYLLj99ttZ+/kMxRI2FsUmAbHbhf8djUbh8/mQy+WgUqlYVBBQWCenmC6Ax0ZM/jzzQxR/sdQPNKGLP6f6S3zG9Ewmg0Qiwa4BaWnoupCLRhzFtV7whhXvdiHXULEFEWl6SCxLbS3lfqWcXjxbIJfL4XK5WPJPCrFfad94xorXW5F7LpPJMAPJ4XDAbrczFxOJmrPZbEGkHG80UuI++gFQkPBvs0BMm/gd0Ov10Ol0eNe73sXuERlqfK4Zeua2E9tuyMjlchw9ehSBQGDF2XAl7BxQPROLxYKKiootFamK9QHXImhglLA5WO455I1g8T5UjLCurg6pVAput7tAIL7UsTdKW0L7iScMMSNDeh6+8jAPjUZTNHRX7BYWs0r8cdbTfjqm+DP+mKXYK76dfP8J4mrLtJ3JZIIgCCx5nkx2Jf0C6ZzIhbKSfhW7LhRVJWa/eOaIby9pdPhwf/HxxSxTMWZqM1DsHHwkk/i6823nsRpX00b2Z9tnAaVSiYcffrjgM8m1dO2A6r60traio6MDHo9nRcUcJUjYCaDVL4XJ8rDZbCzDaj6fZ2n9aXyiiamUi4oXy67FrSR2hy23L7+aF/elWHI5moDFOVr470uxAes1aui4pFMBrhpjxQx7MgTECxdiRLLZLMuRQwsch8PBWAWaWMmIEbuXVtsH8XXhC4KKDRle30L7kOiYwPet2P3Y7DmxmKuRT9zH95c31sS1xMiYJlelmBnfLGy7IQNIhsu1jLa2NvzFX/wFK/omibUlXAsQazRKsQYUVSKOlBFvWww0Sa/FiFnL9jRp8oJlAp/wjD92qYl8qX6u9/0Wn5cixJYKgy52DenaUn/FkWjkOuL7TNE2fF6a1badB6/ZKcYmLTWJi9m1YsffSojPLS4Eybex2N8ACoydrWSVZMJmq3AkSJAgYYeBDz/mXRPF8mEUE6OKhaprTRi3USglmqXviqWPpzBkPpJpK/ogFvwSNuMaUvQTUMgCrSeSrBRKhRYXO89S9+taBfVHrFUEVsYorgeSISNBgoQ3HZaaTJdCMeZG/N12YCnWpNSkKdbTbNWEutSUsxnGRTEtymb0daURPMW2fSMZMktBMmQkSJAgQcKqUWrSXE801bWIN6LxIOEKJENGggQJEiRIkHDNQop1liBBggQJEiRcs9gRUUsSJEiQsFPA62eK/RZrLsQiYP47fhv+s81uO49ibSsWhbXc/lsJcVvpd7FrWizj8Ha3udj/K22XOAx6p0Q17WRIhowECRIkFAEvFKXJhTLdilPl82GnBHF9nK2ahJbTvlA/KLxcvM92ameKGQN8Xhax4chvw39fKrHeZreX/2ylQvJiEXLi/ESlnrFix1grViLCXq0SZSWGcqltVwPJkJEgQYKEIuAneuDqxFSqpAGfEEycFIzfdjshrnbNg59ExQnfthLUDjIYKV0/5YwpdR3pmvPFQbcS4txEvMFIEOeboeeGftMP9X21uYs2qh+E5SKueAOHr+5drHwGbbcZYeeSISNBggQJKM5KiI0QsXEi3p8mXfGEtlkr5tUel9rHT6jic4hdONsB3hDg6xrxbSrF3vAp/vl9ljMCVtPXUq6vUj9iQ6XUefk+8M+a2OAR779ZDJT42S3mxuPbzWeV5g0Z/hrx95N/l9bTB8mQkSBBgoT/B0qgFo/HWap4SmdP4JkXAtXt4QsKAlhydboWrGS1nEqlSqbzpwKQxbL/ptPpgrwy28XI8O4jQRBYyQL+s3g8DgCsLAFl8BWEK8Ubc7kc+6zYhMqfa60oZpiIWQbx8fkcPslkEnK5HFqtdtE21GfeLUn3j1g1OtdSGZHXgqWOQ+dXqVQF11VcjqAYyEDji5BSP9bb/i01ZDKZDMLhMHK5HDKZzIr2SaVSyOfzcLvdrOLpTqBo34gQBAGxWAypVAo+nw/5fB4qlQoWi2VRnZntbidRr8XSYEuQsBrQKjGbzSIajSIWi2FhYYFNgiaTCSqVqmDVyddnksvlsNvtUKvV0Ol0iybP9T6b4tUsodhqNp/PI51OsyrFpZiIYnqLYivirdbLiCc4/pz5fB7RaBTJZBJTU1MAAK1WC4vFApvNVlBraSnh7UYxZEv1gRgVvh9iN+NS7EwpUTP95guAUl0vKvWw3r4t5fpZjmkUG798m0v1ke9PsbIeK8GWGjKBQAA///nPMTc3h+np6WW3FwQBfX19iEQi+Od//md0dnZCr9dLE9YmIZ/P4/Tp0+jr68Pf/d3fIZVKweVy4YEHHsDHPvYx6HQ6qNXq7W4m8vk8/H4/BEGAXq+HWq2GRqPZ7mZJuEYhCALC4TCCwSC6u7sxMTGB3t5eZLNZ5HI5mM1mKJVKpFIpZLNZJJNJpNNppFIpaLVa6HQ63HXXXaipqcHevXuhVCoZM1OqSvBqQQZUOp1mk4hGoyl47smIWVhYYHWGyKAh0ERPEynfPo1Gs2hsLab12Q5kMhmk02mcOHECAwMDePTRR5FOp+F0OnHrrbfi/vvvR01NDaxWa8kxiu/vRix8xC4d3jhMpVJIp9OIRCJQKpXQarXQ6/WMfSml91mqXcS+8NohsZG0nn7R85rJZJDP55eca6nsA5X04K9BKaaqGDNJfeGLtq6lD1tqyCQSCfT19WF2dhaTk5Mlt6OaJxqNBrFYDLlcDslkEolEAnq9fgtb/ObDwsIC5ubm4Pf7kUgkkMvlsLCwgEwms2OMBUEQEI1GEY1GMT8/D4fDgfr6euh0uh3TxmJIJBJIJBKYn59HOp2G2+1mFHI0GoXf70c8HkcqlQJwZWCJxWIQBIFNSkqlEmq1uoA21+l00Ov1cDgcUKvVResFSSgNQRCQTqcRjUYxNTWFkZERXLp0ibmPjEYjFAoFUqkUY2Po+prNZuYmICODX2mSZqAY8wEsPWjzE0I6nWaMEUEulxc87/xEJi6iWMwg4TU94om1GAu0UQbNUlqVYmwQ369YLIZgMIjZ2Vmk02nkcjmEw+GCKB9eB8T3Zzntz1qYAF67wl/rYDDInieNRgOLxYLKysqC+1VMSC5mYPgK2eIIuWJsyVruDRnJiUQCyWQSoVCIjfdUYDObzSKVSiEUCiEWiyGZTC7Sg6lUKqjV6kXuPq1WC41GA5PJBIPBAIPBsIiZ4iMBiVkq1sdS2FJDJhwO4/nnn8fMzAxmZmaKbkMvlMPhQEVFBXsAEokEgsEgbDbbtvlu3+gQBAFerxcTExPMMp+dncXCwgKSySR0Ot12NxHAlRdvbm4OQ0ND+OEPf4g9e/bgXe96F9xuN8rKyra7eSXh9/sxOTmJ559/Hj6fD29/+9thtVoBAAMDA3jppZcwPj6O2dlZAFdWoUNDQ8jn89BoNDAajbBYLLBardDr9WxF7na7UVNTg6NHj8LpdMLpdG77CvpaAmkuAoEAenp60NPTgxdeeIF9r9VqIZfLkUgk2CLLZrOhrKyMvRPF2JdsNot0Ol1QgZkmDQCLjI1ioOPS5DE/Pw9BEJjGxWg0sm1pxU4TBxnJwNWVL89WkDuN37fY5Mj3jV+BbzZ49wnpfRKJBEKhEMLhMFKpFORyOeLxeMGckEwmIQgCdDpdAVOyGcZ9MX1HLpfDyMgIZmZmcPr0aZjNZlRXV+PAgQMF8xevYaLnggwA+oz6IpPJ2CKlmF5rPSCiYG5uDnNzcxgdHUUoFIJcLodKpYLdbkckEsHc3BxGRkYwNTWFubk5JBIJRCIRJhUxmUzMzUcGi1arRXl5OZxOJ5qbm9HQ0ICGhoYCZhC4MtbRM6hSqZjmbKXYUkNGq9WisbERuVwOMzMzaGhoQFVVFerq6tgLSZ0hQ0ar1UKr1aKpqWnDjRhBEHDixAn09fXB5/NBEASUl5ejpqYGN954IzQazY5wpWwleEpwqZXTdiKfz2N+fh5TU1MYHByETqfDpUuXoNPpdrQhMz4+jldeeQWvvvoqZmdn4ff7odVqIZPJ4Pf7MTExwVY8wJUBJhqNMqo6mUwiEonA5/NBpVIxt8HQ0BAsFgu6u7tx0003MYNGYi9XVshPJpNBp9MVuGN0Oh1cLhfKysrQ1tYGs9nM6G9aYTscDhiNRuj1erS0tMBqtbLVJE38YlaEjAd+JV9s9UkMQzqdRjqdhtfrRSgUwvDwMFv9ZjIZ5rIgo0WhUMBgMLDJjhe+8gwATX68sSOTyZDL5RCLxdg1KKY52agImZUIl8U6CnF+FWCxQcjrLMTXVJyPhR/rVjO3iO8pb6im02mMjY1hdHQUFy9eRFVVFRwOB2PySuliin2uVqsL2pnNZgsMY/F94bdd6f2he+71ejE0NIQzZ87A6/Uik8lAoVDAYrEgFoshEAhgYWEBkUgE2WwW2WyWeUyIsUkkEggEAow9ViqVMJlMsNlsGBkZwb59+5BMJlFXVwez2VzAIBa7BivFlhsyTU1NCIVCAID6+nocPnwYN998MyoqKgBcNWTsdjsqKys3vU3PP/88fvWrX+HSpUsQBAGdnZ04evQourq6mG98uQei1INYCuIHTVo5rw68ITMyMgK9Xo++vj7U19dvd9OWxOTkJF599VW89tprmJmZwSuvvFJ0u1L0Ov0u5aYArgx0tDB4sxoySwk9i72rMpmMaV1oRajT6dDQ0ID29nbcddddcLlcbBIkmtxmsxUwBsVcNjzLwYuKaeLlJ0G+XXSMVCqFeDwOr9eL+fl59PT0MH0MGe5k1AJXJnGDwcCOl8lkkEqlYDQaCyZbmhDFkU3kqqHrQePxVi9qxIspfpIulVlZPJ4Wc60VOw9/jrW6Zuj8vCEzMDCAnp4e5PN5dHR0MEOGZ1B4vZK477whQ4xNJpMpGTq/VpBBMjs7i6GhIZw6dQojIyOIxWKQyWQwGAxIJBIIh8NsH5PJBIVCgVgsxtqWTCYRj8eZ8Uz9JGNmaGgI6XQaarUaDocDZrO54BrQ32vBlhoyKpUK5eXlrANutxu7du1CY2MjHA4HgKud4lcKm4mDBw9CqVRibm4OMzMzGBoaQiqVQjQaRUtLC5qbm1FfXw+bzcYid9LpNBvs+JuQy+UwNzfHRIJikN7kF7/4BTweD+69916YTCaYTKYt6esbBblcDsPDwxgbG0M+n4fL5cJtt90Gj8ez3U1bEk1NTbjjjjswPj6OQCCAVCpVckAidoCnxauqqtDQ0ICpqSksLCwgHA4jnU4zHQ0A9Pb24ic/+Qkeeugh2O32LevbTgRdE54dEa/y6f0lZsNkMsFqtcLpdKK+vh6dnZ1oampCeXk5m3Dy+TzTAgCFA3Emk2Hb8DoBOj8AZvQslTSMRMU+nw8LCwvo7e3F5OQkXnrpJRb+6vf74fV6UVdXB6vVCpvNBr1eD6fTyQwhpVIJo9EIpVKJXC4Hr9fLJiXSIdKKemFhAdFoFCMjI3A4HNizZw/KysrgdDoLxjhgcTK9tWCpSavYxEYTO7GYwFX3SygUQnl5OQCseO4Qi3/XAp5hk8vlTJA9NTWFyclJRKNRKJVK1NbWsnGed/PxRhe1QxAEFsxA157mHXI1kaB8I6BSqdg8ZDKZYDQamfGSzWYRDAYL7jvpZkg3plQqYbFYUFZWBrfbjUAgwPRByWQS2WwWiUQCY2NjjCluaWmBw+FgkcharZbdj7V4XbbUkCFhIgmeDAYDnE4nrFYrLBbLVjaFoaqqCslkEna7HYFAAOFwGKOjo0ilUggGg4jH48jlcqioqGCiy3g8Dq1WC7PZXDBQ0QOcTqeLhpdns1nMzs7ixIkT6OjowJEjR5i1ulNQbJWWyWQQj8eZAbpdoAc9k8nA5/MhEAgAuLI6qKurW/F1JLGwIFyJeuIV/5sJu92O5uZmuFwuTE9PIxqNMopWLpcX+I0tFgsbLKhdDQ0N2LVrFxP80qTJY25uDr29vYhEIpval2sFpVbcYvEnTQykQ1Cr1TCZTLDb7QXjEx/eLKbDecaAjs1P+GKmoNQqlIyQbDaLeDyOcDgMn8+Hubk5TE1NseeF8tukUinY7Xa43W42JpExZDAYoNPpWBTN1NQUCy/XaDTQarVsvJqbm0M4HMbAwABzq2m1WjidzoK2if9e7r1Zzr23lBCa/5uYLxqfxKwV6X2KhaUv1ba1vPdiMTQvNM5kMlhYWEAoFEIul2OaKopYErNK1D+eMaN9jUZjAePG52EppSNZbX+IZTQYDDCbzcwoTqVSzKXN50milBykx1Kr1SgvL4fb7UZdXR0mJyeZAU7t5Q3zmZkZxGIxZDIZZsyJBb6r7cObPiGex+OByWTCH//xH+Py5cv4/ve/j2QyyUSXL774IqNxDx8+DJVKhUAgAIvFgqqqKqbQBoB4PI7f/e53LNdBsZcom80iEAggHo/j17/+NW666Sa2kthuyGQyVFdXIxQKFRgz/f39+NGPfoS3ve1t2L179za2EPB6vZicnMT58+cxNjYGg8HAVhErXaEkk0l8+ctfRjwex8c//nHY7fYtMaTdbjecTif+4i/+AvPz8xgZGcH8/Dxef/11JoYj10ZXVxdsNhvLSwKAfXfmzBkMDAzg3//935mbluDz+Zi2RsIViFe9wJX3kI/MoAUJGQ80EIfDYTZBUsgpMSqkYaHJSi6Xs/em1GBMrIK4bWLQhEjRmpQETavVIplMIpPJYGBgAKOjo8wAa2xshMFgKHiWnU4nzGYzfD4fIpEILl26xHJz0Q9FYwFgAv+6ujrGHFC0zUZlYS0G3uAsJs6liZ4MTrpXwWAQMzMz6O/vh8vlgt1uL7g/PPMlXunzhuZaJk+KauPbHgwGMT09jb6+PkxPT6OiogI1NTVobm5m90VsyJJhIpfLmc7kpZdeQjgcRn19PdxuN/bu3cuMBkpcSFAqlYsM89WAWLv29na43W54PB7Mz89jdnaWeSfI0LFarYy1JBcr/SYj86mnnsL58+cRDAZZhCa1N5lMIhgMIhQKIRqNFoxv63GXbakhQy8+T8UVU8pvJXQ6HQRBQGtrK1QqFdra2hAIBBAIBJBMJplfkKxQpVKJUCgEk8mEhYWFgjwNiUQCQ0NDiMViLBSzFEKhECYmJgr8jjsB4qJ4wJWHb2FhgQ3k2wmfz4fp6WkWIlheXl6QDGslIOp2YWEBAwMD8Hg8W2LIkDuipqYGDocDer2eUcgOhwMNDQ1sMCDxKEXMAEAkEkEwGGQhqDQB8fdKrVZDr9dvCPV/rUIsoOUHeLGORczSEDvBC3vF4a7iSY/fX+wi4Ffr/KRJ2y/VB9pHpVKxiDWK4IzH44jFYgiFQmw85bU+tL/NZoPRaGR0/8jICDPMSMdAmgZyPwWDQVgslgJGupjhstpxuxTbwhsCy10P/jh0D5PJJFvh8+7C5cTE/Odr1caIRcXBYBBzc3OIRCLIZDKw2WywWCyLskPz7RM/g7lcDoFAAD6fjxlbFMbPewCKtXupfizFphHDp1AoUFtbC6vVCofDwdgutVrNjGRibjQaDdORqlQqxONxRKNR5gKjuZ533ymVSvZOlRI9r8kYW/Ue6wCp78PhMBQKBWM6tjOcmqIVbrvtNhw+fBgtLS3o6+vDb37zG/T19aG/vx/AlRXc66+/DqB41kmCeGIphVAohHPnzuHAgQMb26F1QBAETE1NYXR0tKggbbshCALOnj2L06dPY3Z2Fmq1Grfffjv27NlTIHBcDjKZDGVlZQiFQvjqV7+K66+/Hp/+9Ke3rI92ux12ux1VVVUQBAF33XUXM+yLCRUJw8PDOHnyJH7wgx/g9OnTRV1L9fX1OHjwYIE74M0KmiyKXUvSNvDXj1yWkUgEMpkMTqcTdXV1zCVQjPEjNzmfNp4/HhmbMpmM5aNZCUiHQO7c9vZ2eDwe1NfXIxqNYm5uDj09Pejt7WXZuAOBQNGJja5DMZcGQTyZZrNZhMNhxgaR5qcYq7ERKCbQ5msmESgxHr99Op1mzNlq3cRrbT8v6CaGSBAEnD9/HmfPnkUgEIBCoUB7eztqamrY5M2Xr6B7wofjA1fTS4yOjqK/vx+RSARHjx5lIfWlcmWtpC9i9ycf8UXpHNra2iAIAnt2i7m+eYYulUrB7/ejr68P586dw/PPP4++vj6MjY0hkUgwSYZGo0FFRQWam5uZq63Y4mDHa2RI7JpMJpnGxG63I5/PF4SVlpeXLwoX3AyIaUudTofa2loW2kgMTCAQQCKRWPN5iMLlkwhRPoqdtnLeyGykGwkawCgaIJPJwGg0wu12M1HrSgcluVwOh8OB+fl5XLp0CT6fbzObXoBiL2wplxjR/n6/H+fOncOlS5fw+uuvY2pqiiXNA64MTmazGR6PB/v27cPhw4dhs9k2vzM7FCtZiYtXp8RMxONxCIIAk8nEcvVEo1FWXkWhUMBkMi1iPmgiI3aEBn1xxM9K6H8aj1QqFQwGA5sA0+k0XC4XotEoZmZmmOuJ2GM+4yu5YcThwbw7gyYxmtiCwSB7z4iN4tP+b4TRUqr/yzEL5AokxkzMuhRjjEppb0qdY7WgNpBLKJlMYnR0lOV+MplMqK+vR3l5OdNU8VoYnnXjBeHEwOn1ekxOTmJhYQGBQGDJrMXLQazpIeOUnlHSwPDPLN17ipyi9vHPjc/ng9/vx4ULFzA4OIienh6Mjo4iEAgwBl+v17NcOh0dHWhtbYXdbi/qrlwrK7MtjEwsFoPRaER5eTmqq6sZRXrmzBmmnCbf9VaC/Mx1dXXYv38/iwbo7u7G3Nzcmo/Li+n4kDSLxbKjM9HuJKRSKYTDYfT09OC1115DKpWCXq9nESWrAWmBgsEgxsfH4fV6N6nV64MgXEmdf/HiRXzxi19kkRDibQCgrKwMd9xxB+644w7cdtttb7r8R+sFGQWhUAj5fB5Op5ONQz6fD6lUCkNDQ9BqtWhoaIDT6SxIN08DO19sktfMiGvv0H7FIJPJ2CqfDKeysjLIZDLo9XoWEUKGFmXhphIKqVSKRVUWK6DIC5bVajUz4i5duoRwOIxMJsOCEOgc62XNS4muxdeB/5uf5CjyJR6PI5FIsGvO60tKGVt0vpUYuCsFf06FQsFYsu7ubpw5cwb5fB5WqxV79+5FfX39onw94uMQiHGpqKhAKBTChQsXMDc3h8nJScjl8nW5wEk2QPefDF8yWMTsF7mCFApFQcg+zcvpdBpDQ0Po6+vDj3/8Y0xOTmJsbIyFYANX5jmr1Yr6+nrcfPPN2Lt3L/bs2QO3212URV/rfdkSQ4bCksfGxtDf3898r8888wz8fj9mZ2dZHLvRaMRLL72E/fv34+abb97S+j786kqtVqOrqwsWiwXHjh1bFyMzMzOD+fl5PProoyyjcWVlJe6++240NTVtSNvf6JiammLlLTKZDJqbm9HZ2Ymurq5VJ8FTKBRobm5mK89oNIrBwUE4nc4dEbIsCAJCoRD8fj++973vYWBgAOPj40UFvPR8trS04Pbbb0djY+Oqs2K+UbHUxMVT5fl8HoFAgJVOCYVCSCQSePnll1miMIr0sVqt2LNnD2pqatDY2Iiamhr2zPCZcukcvJGzkoUZ307ansScPHNcWVmJ9vZ2aDQaZqzQ5MFPmpQKgnLkiDP5KpVK9Pb2YmJiApcuXWJJ0IxGI6qrq2G1WhmbUErTsBqsJEJJ/B2/DekxKJKHJmNiGshoo+1540ZszKy13QRiLiis/dKlS5ienkYkEkFbWxva29vR2tpakFqEd+vw0UfEOFG/ampqkE6nodFokM1m4fP5WFi9WFPEMxpLGZz89eC3o+tFbiR+G/7a8RXGp6en4fP58Nxzz2FoaAiXLl1CJBJhZW1kMhmL9tu/fz8aGhpwww03oKamBmVlZUUzRK9nzNoSQ4aK/FH6+2QyCQB47bXX0NfXh4mJCZZ8R6/Xo7u7G5lMBrt372a0F7B1ieNo5ULplNcKeuAuXbqEkZER/PKXv2Srf4fDgSNHjmxJ0r/VQPyQbzfohSU3UCAQQDabRW1tLZqamtDQ0ACNRlPwgotp5mL+9+rqakQiEcjlcsRiMYyPj7N03NsBfjDO5/NYWFjAxMQEHn/8cZY3BljsDiVDprW1FYcPH4ZOp1sUPv9mQzGXA31O4A0ZMhwDgQDm5uYQCoUQj8dx8eJFhMNh9PX1IRwOI5fLwel0IpvNsvBYKhlBxyGXDZ2PzkNYKStQbMIhkJYhl8uxLL7EThCDQ2HJtAKnBGbJZLJA4yCXy5HJZFg2aTJk9Ho9KioqYDKZiibsW+9YvJYIG974EhfDBAoDFcTsTDEjZj0MDbWFDEi/34+hoSH4fD4kEgnU1taipaUFHo8HGo2mwF1Pbh1aSCkUCqTTaSQSCXY/y8vLWcFJEmDTvCk2ZFbquhQbMuK/SfxN/4sXRGT0U2b+8fFxnD59mmUx5p8NuVwOq9WKiooKdHV1oampCbt374bFYmFpMjbCMCZsyYgnCFeL/GUyGbZy8Pl8CIVCqK6uhtFohMlkYiGCv/jFL9Df34+HHnoI119/PdOrXEuIRqNYWFjAT3/6U5w8eRKzs7NQKpVwOp0oKyuDwWDYUS4AmexK0rWFhYWiBc22C4IgYHx8HCdPnkQgEIBKpcLu3bvR3NyM6elp9lxQXSh+kqFMpXz7lUolurq6kEqloFarEQgE8NRTT+H48ePblh04EAhgYmIC/f39GBsbw6lTpzAzM4ORkZECPQwAFql15513oqGhAffccw8sFktB9lYJhRAP8DT5UxhvNBpFOBxGJBJhE4bP52N0us1mY4bPuXPnMDo6inPnzrEJqL6+ntXDAlAgkFxpm1YLqrnFh6/SBAsUurMoJJl3xZCrxuv1skyucrkcdXV1aGlpQUdHBxONk4FAuorVJmMrtqhYbtLlo11oBU/BGeJjLXfeUv+vpC1LgbRTVDGdFkcUlTg6OsqeL8oZRZFm1Ce5XI5wOIxAIICOjg6Ul5djfHwc09PT8Pv9UCgUmJ6eRlVVFYLBIDN2yHAmg2glWejF7iOeNSRjj9xH9LxQtOTMzAzGxsYwMzODM2fOYGZmBr29vUxXRm4x0i0ePXoUHo8HN954I6tNJq4TRfd4vTKSLbUM6ILRj06ng06nQ319PUtZPDMzg56eHkbRHT16FDU1NSystFgEwk5FIpFglnpvby8SiQQbFM1mM8veupOwloJdmwlauQQCAUxNTTEXn0KhQCaTwfDwMBvIp6enMT8/j0AggFwuB5vNxgTmPMj/T9EpiUQCw8PDi7bbTPARC9FoFLOzsxgeHkZPTw/6+/tx+vRpzM/PF+QjokRtVVVVqKqqwr59+9DQ0ACPx7OjDOJrAfyED1yd9CknCwAm6DWbzSxPTDwex8zMDIsSGhkZQXl5OSoqKti7w9cE4tPXE9Y6aYonbr6tYiayFCvJ95kikyjUlyZEu90Oh8MBu93OylzwLhFxe9bal2LXhD7jQ5uBqwwajf98UALPNCzHcvHtXq5NKwFVjQ6Hw/D7/SyiShAEJJNJjI2NseSqpJVcWFhANptlBUdJC0c1iqLRKCYnJ5lLMxqNsmraVLyXv8di7UqpvosZE7H7SHwtSW9Fbtfh4WFcvnwZY2NjOH/+PHw+H8tbRSyf2WxGVVUV3G432tvbUV1dDY/Hw7RW/PXn3w/xfVntfdgSQ0ahUMDlcmF+fh6VlZXMZ/uOd7wDx44dQ1NTE0wmE/L5PLq7uzEzM4Pp6WlMTU3hm9/8Jp588kl85jOfQUtLCxO9XQuYnp7GyZMn0d/fD6/Xi1wuB6vViv3796OrqwsVFRU7ypARBAETExMFuSaA7Q2/jkQiGBkZQW9vL3p6elitkR/84AdQKBSIRCLsBSBqlAZBcQE/gkwmw6OPPgqZTMaS/50+fRrHjh3b0r6lUilMT0/jhz/8IUZHR3Hp0iVMTU0xcak4lL+2tha7d+/GBz7wARw+fJjlptiqch5vVMjlcpjNZrjdbhw5coRFW9x6663Yv38/mpqaoNPpWN6h73znO0zk+MQTT+Dy5ctsQUY1mQThak4avqgjYT1sTLFKyQAYk0Tnp9ww5LoQV7u+fPkyTp06hRMnTuDs2bNIp9OwWCxobm5GY2MjXC4XO77YKCtmoK0VZJSI61KRm4s30ilhHCUFpP5Sjib+GHzulc1CKpXC7OwsZmZm2EIrm83i3Llz7PmIx+PMkCHjIJ/Ps2eCXE25XA5PPvkk9Ho90uk0O3Y8Hsf58+dRWVnJkh6Sa5BciHSfl+svXZti2/DHyOfzrMr173//e1YAc2JiAnNzcwVuS742WUdHB2677Ta0t7ejubkZRqOxQA9DrBTfno1gkbfEkJHJrhSeKi8vx6FDhwBcCcnq6upiL4xWq0Uul2MrTZVKVZDmeGJiAhaLhdU72smglz4cDmNqagrRaLQg5wBleF0JFbjVINHZTkEmk2FJ4HgXSyAQYNqG1bq9ZDIZ0woQfUopAEKhUNGJZ6ORy+UwOTmJkZERXLx4kRnuCwsLTK8ghsPhQEdHB2pqanZMNuhrAbzRUEpEajQa4XQ60dHRwZ7/lpYWNDQ0wO12s5xXyWQSTU1NiMfjLDSWQm4VCgWcTieLOKLzFXPNrua951ewfO4bntUTF1IECjPIUnv4cHByK4XDYTYZUkkCEvny4I+3XiNGvG+x6yNmaMgQ4ydCfn8xYyRmfMSfr9TNVay9BBqfwuEwG+fz+Tx8Ph+USiWSySRL2Edtp/ZT1A6JbMkdSeUW6IevuUVRZWKDj3fFrbYPxXSFVJh3cnIS/f39mJycxOTkJAKBQNHxSS6Xw2azobm5GbW1taxcRinGkN9vI+bALTNkHA4HTCYT/uZv/gYqlYpRTbwbQ6FQoL6+Ho888ggef/xxVsgxFArh5MmTiEajaGpq2vErUHKHTE9P48yZM/D7/QDAogGuu+46KVpphUgkEpicnCzIgCwIAoLB4LqOyxtFNNBPT0+jv78fTU1Nm56HJZVK4amnnsKFCxfwi1/8YskCkoSWlhb8wR/8Adxu96a27Y0IMaNAAyu5ud1uN6xWK3Q6HWPCPB4PKxYJXMmS29jYiPvvvx92ux3xeJyVCaioqMCuXbvQ3NzMBnBiTHgRMD85r2YApxV7NBplDAQZJxTuSgxdMS2h2I2WyWQQjUYxPDzM6nIZDAY4HA60tbWhurq6YH/SZGxUQrxSRob4nPzEyueRIeaBDBzSXlLkj9h1yN/v5dqzGkSjUQwMDCxiKoaGhpbsH2klqY3hcBgLCwuLhLcAEIvFMDw8jNHRUUxMTKC2trbg2OR+FouKVwr++aTrk8lk0N3djd7eXjz11FMIh8MIhULs+aU5mMYtQRDQ0NCAe++9F3V1dSxSC7iqr6Lrz8/5G7WQ3zJDBriaO4UikYplilSpVLDZbNizZw/e+c534plnnsHAwAAuX76MdDqNPXv2oLKyEjU1NTuOzSDQgxEKhTA2NsYisux2OyorK1FXVyetqFeIWCyGkZGRAv2KeGDQ6XQwGo0oKyuDzWZDVVUVDAZDQeZIHoJwJXyQ8m7Mz8+zCJXp6emCQZzoXUrV7Xa7Vx3uXQo0OYlX1OK2AldXqNtd0uNaAr9q5Q2ZYrQ6JYCj4rCCcKWgKLEgAFh69erqauzatQuxWAwKhQKjo6OYn5/H0NAQenp6UFVVhaamJuYWIcOJzr+WVajY8OIjrqg//HgqNp74a0JRNlRPJ5lMQi6Xs+rq9fX1qKioAHCVCRGH4m4E+OOJr5H4OolZrWLXsBhLJO67GGvVZdBilUrNBAIBlmmb9J/UJzIQKb0/VR2vq6tjkzol1KPrHYlEkEqlsLCwgIWFBYyNjSEajRbocCj3USQSQS6Xg16vL2CllusPbUdMFwng6X8qg0Fh1dQ3KhYpk8kKNEE0nhHErnExoye+7usJKNlSsa9CoVi2gjIZMkeOHEF7ezv8fj/m5uZw7tw5jI+Po7OzE52dnaipqdmiVq8eNPj5/X4MDAwAuNL38vJy1NTUoKWlZduqfV9riEQiuHz5Msu+W+zl1Ov1qKqqwu7du9HS0oKbbroJlZWVcDgcRSetbDaLl19+mWWZfv3113HhwgU2YOzatYu9VMlkkuU+mp+fx9GjRzfMkOFdBKW+F38ndlVIRs3y4F0wvNuFX0TJZFciY1wuF/ufjExader1emi1Wla2oKysjInPvV4v4vE4XnvtNcTjcWbIUO6W5RiBpdoOXBW/0jF5Q4a0DcUYDLGORqFQIJVKFegQ4/E45HI56uvrWe4Tq9XKjkMRNutx6Rd7lgm860ss8KXvxc/8SlxbYkNpIyEIAhKJBAKBAAYHBzE/P1/AFNGCPZvNwu12o7OzE3V1dXC5XCzRYHNzM0v8SvtRTSyfz4dYLIapqSn09PRgaGgI4XAYXq+XPY+0uAoGg8jn87BYLCtiZMQuNbrHdL35LNckNiZ3K5EQFHhDTDkdgy9ASs89b3zT/ViqPWu5Vzs2nlmj0cBms+GBBx7Arl278PWvfx3BYBCvvPIK5HI5br/99h07iAcCASbyJSiVShw8eBB79uzZ8a4xoPDhojoZ2yFMDgQCePXVV1kdGYfDAavVire+9a0oKyuDy+WCTqeDwWBgVawrKiqg0+mg0WiKPiP5fB579+5ljIxCocDTTz8Nv9+P3/72tzh06BDq6uowMTGBgYEBfPWrX2VJyDo6OjakX2q1GrfccgurNBuNRhGPx+FyuVgfk8kk/uM//oNFV505cwb/8A//gOPHj2PXrl2oqalhoagSiqPY6k/8GQlzyZihz2Qy2aJKygSLxYKmpibcfvvtqK2txQ9+8AOEw2G89tprkMlkOHbsGBOg0mSxViOG2i2Xy9lKmGck+Irb4r5QX/mVejweR19fH2OSSB/jcrlQVVUFo9FYEGEi1p5sNEpNbGJ3B90DmUzGEv0BV+cKEsHyxyh274qda7VzCbn5KHElGbSUafzgwYMwGo1QqVRwOp2oqalh2iNKeke5ffhn0mAwIJvNwuPxsGhNjUaD559/HsCVlACDg4PIZrPMeA4Gg6ioqIDdbodWq13xs0bGPRnIvI5Kp9PhwIEDqKiogFqtZlFVNpuNZd9Pp9N44oknEA6HkUwmMTAwgKeeeopVT29tbYVer1+RIVksIGM12LGGDN3s/fv3o7a2Fo8++ijm5+cxPDyMurq67W7ekojFYujt7cXs7Cz7jPIz1NfX76hIpWIQD1pKpXLbKionEgmMjY2xdjgcDlRVVeGOO+5AfX09mpubC8IyV/oCeDweNkhPT0+jsrKS5TAipoY0M8899xzcbjcOHz7MIkPWC6VSidbWVhgMBuzduxehUAihUIgl0aqsrEQ4HMZjjz3GMvqOj48jHA6jqqoKVqsVlZWVkiFTAjxdLXaJiI0YYlB53z8/uVAUEB8STKkjOjo6YLFY8Mtf/hI+nw+jo6Oorq5GKpViq9el2IiVgJ/AxZE9QGGRPWJi+Age8bmJkaECmaRbMJvNsFqt0Gg0i/J98L+LYS2LSrHbgT7jXRX0HX3GM070vVqtZsYX1ZeibfnfGwliRJLJJBKJBPL5PJRKJdxuNxobG3HkyBHYbDZWZ6iiogJGo5GFXIuPRf0idyYZn06nEzMzM7BarZDL5YhEIiw7/OjoKHPt6HQ6VnZgtf3gnyv6W61Wo76+HmazGYJwpabS1NQUKioqWEX1SCSCF198EalUCpFIBF6vF93d3Uxn1tDQsEinVOpe8O/IWuaZHWvIEIxGIwRBgNPphNfrxdzc3LqFnpuNRCKBkZERJvK91iA2COrr6/HWt751kQBwK6DT6eDxeFhZ+fe9733o6upCfX09dDrdunPekNDznnvuwSuvvILTp0/j+eefx+TkJJ588kl4vV6YzWYcPHgQH/vYxzY0YZ5Go4HH48FHPvIR5sbQarVsJT89PV3A3lHk1okTJzA7O4uGhoZlXbUSrk6SvKuCwOsygKuhsORO4UN/yd0Uj8dZ8i+Xy8VcTrlcDn6/HwsLC0gkEgXsTj6fX7N7ppSuh45NbadJjYwYHuIw7bGxMaY7I+0PaSTEx+ajsPiJkncJraYvdGzxZ+L/+XGINypJ5CuTXckE73A4UFNTw6KAihl4xZiX9Rg3dF1sNhva29tRUVGBcDiM+++/H+3t7di9ezdzv5BByxtr9JsS6pHOLxqNsjB4Cg5xuVzYt28fEokEYrEYfvvb3wK4UrZFrVajs7MTOp2uaMqG5cAbrPy1USgUqKqqQkVFBWpraxEOh+Hz+ZjWJ51OY2ZmBgaDgT1bMzMzWFhYgEwmQzgcxv79+xlLJg79541s8Tu4Fux4Q4YeAnrJ+MzAOw10Q9LpNBNrXcugl9BkMqG8vHxbVv8mkwltbW2w2WwsPJYoy/UyRPwqqKamBhcvXkQqlcLw8DDi8TguX76MRCKBsrIyVFVVob6+fsO0TTQwa7XakgYiRQkQSBQcCATg9Xp37Huwk1BK7FnMbcNjKd0S72ohgwZAAaXPrzDF2o719oVnLXixL38O2pZ3HdD/qVQKoVCoIO8MX1SSb+NKr8tK219M81Wsj3xfxN/zGhq9Xg+j0Qiz2bxml/1y96VYn8l1ZzKZUFVVBYvFgng8jvr6etTW1sJuty9KUsmzTOLPxMJb3rVJ5SJmZ2eZfiYej2Nubg4mkwmNjY1MaMw/c6vREInbIghXoqBImKzVapkLz2g0IhgMQqvVFjDhqVSKJf+jMPHVRlGt9f3Y8YZMNpstqNS5mb7a9YJyx4RCISwsLCAej293k9YFlUrFLPCNqIC7FuzZswdf/epXWVipw+EoGY20VlgsFuzZswcvvfQSgsEgfvnLX0IulyOdTsPtduPtb3878xfvhBxG9E6sJdRSwhXwropiScRKFbXjIy6BK3oTMgry+Tyj3nlN2UaLTfP5PFKpVMFERCHZYlDyOLPZzMSks7OzGB8fZ4yMWq2GTqdjzBLfXzp2sRwtq3kXyIgRsyT8MyyOyOP/J1cSf16dToe6ujrU1taiurqaMTLFFjjFjKjV3hfeIKV6SDQu0XfNzc2snAWBonlI18MLfMk4oO2LlRqwWq3Yt28fzp49i6mpKQwNDWF+fp49b8QcrjeikXKI0dhCi7ZEIsHKLqjVagiCwOomUnI/PhSeFxDzgt9iNRPF92Wt78qON2R8Ph9mZ2cRDAaRSqXYxLoTkc/nEQwGWWgjhV1rtVqYTCbY7XZWYO5agFqthtPphNls3jaxr0ajYbk8xPkzNgqkD6CQbYoKoLDJjo4OVFdXrzl5UzqdLqi1QqK8Usjn8xgfH8fQ0FDRasoulwtNTU2SPmYN4KlsMZtBbCpwdbIqVvCRF0YGg0GWSj6Xy8FsNsNoNBbsyw/OaxGXirUFxdwypQSz/MRIxQcXFhYKakppNBoYDAbWdvFkItabiM+30r4UYwBKtZv/m9598ftH6TzovRW7bpZyWa0WxdpFYdYUqk6uLopWEp+Td3GWMtjIKCLjhwwAq9UKrVbL9Fy5XI4xUXQNeAN7qT7y1z+TybBQazJktFptgcuJnh+6B4IgsKKRfJQSfW+1WlFWVsaOI77uxdq2nnsDXAOGzKVLl3DhwgWMjIwgEong8OHDOzb0OpvNYmJigpU1p4eZ8se0trYuEkDtZFCIIEUybAeUSuWm60DUajUcDgccDgecTierO+N2u9HW1objx4+vq//RaBSDg4OIRCKIRqM4dOgQC/Mthnw+j2effRbnzp0rYPVoUDt8+DDuuOOOTU/a90YETQT8ZESTdCaTYdFx5FLV6XSL/Pg88zE2Noaenh6WDK2yshLl5eUFVcjJMFgJ3V+svfRbvC/vEiq2nyAIBav1RCKB0dFRjI2NYW5ujmVoJddxVVVVQVkCHqVWyut1lVHCQHH/+OtNIeS0oqdrolarUVFRAYfDsaiQIlCo/9goiEWpBoNhkW5OEASWd4UYG7VaXbQ9YmaL3DnkliG3TmVlJWOc6Nwejwc1NTUscSNlJF/pPcnn84xRHB8fZ65rYpXonosFyplMBufOncO5c+cKMg3TPaqrq8OuXbtgt9uLipuLYb2L0y2rfp1MJjE/P4+nn34aOp0OZWVlaG1tLchSyIOq0Z47dw6nTp1CKpWC0WjE7t27UV9fv2NZDbH/WiaTsRWPxWKB2Wze0W0nalHsxtuuNm/FeYnp8Xg8uO6661i4d11dHWpqagqEemuB3+/HyZMnMTc3h0AgwEIw+TTwmUwGqVQKIyMjmJiYwHPPPYf+/n6WtZOeI/LJU7VlCSuDWIMgrvBLSc0uXboEnU4Hu92OxsZGuN1uZuzQapTS0s/Pz+PVV1/F+fPnkUgkWI6ZysrKosJZmpxXGhor/psoej47Ks9C8CyAWCtD25CriX+/TSYTC/knRoZnBIqJpDcCy7E6fPupr5Q7hdg0YmPoemyW/KBU34t9zrvQ+LGD2sczGMQCUs00vr+8sa1SqWCxWOByuZBOpxGPx9HS0sJC5onlXU3ZG9547+npQSQSYQEFHo8HTU1N0Gg0LD8MFbf1+Xx4/fXXcfnyZaYFpTqCxBbzjDFfOb1YcUsxg7ZjXUv0Ao2Pj+Pb3/427HY72tvbodfrmSEjfnEjkQgmJyfR3d2NV155Bel0Gna7Hbt27drQyJGtgFqtZtStyWTa7uYsCd5H+mYBGTLV1dXYt28f+vv7EYlEmCEjnpRWi0AggJdffhnj4+OYnp7GgQMHUF5eDoPBwI5L7qcLFy7g1KlTeOGFFzAxMVHwXqjValitVrjd7pILAAlLgyYSSiAHXGFSx8fHMT4+jhdeeAE2mw11dXUsJxEvaMzlckin05idncWlS5dw6tQpvP7660gmk3A6naxwZLHwZbEYeCUDNr8oAq5O7nx+GwIxTTRZFBPqJpNJpmugfY1GIzNkTCYTOy6f7XUjsRJDQ8xU0P/JZLLAkCFdj7jqeCmXFX/NVusSW2lfiJ2QyWQFxgn/HFC4di6XQzKZZNGKxdx3xAKSoaDX65HL5dDS0gKHwwGXywWbzQatVrsqnQwZMgsLC4xV9Pl8MBqNyOVybOxLp9MIh8OYnZ1FT08PBgYGcO7cOZZtmPplNptRV1eHxsZGNDY2QqPRsOeID5cv5q6lv3e0a4lWPMPDwxgeHobD4cD1119fQK9ns1mMjo5iamoKL774InPRjI+PI5lM4q677kJrayuuu+46lnXyWgHl/NjpifDy+TzOnDnDcgO43W7s378fHo9nu5u2JaBVhMFggN/vx1vf+la2ul4PKCrK6/UiEAjge9/7Hp599ll4PB4YjUZUVFRgfn4eg4ODGBkZwfT0NPx+/6IVa0tLC+68807JiFkjSFsgFq7SQiscDmNsbAzxeBxWq5VN5DKZjI1PXq8Xp0+fRn9/Py5evIhAIIBcLodbb70VDQ0NuP766wvqzIiZmNUO3OJJsNR+vN5HXAGaJk25XA673c5yg9AxaYKkZGe0H2Wq3SgUm/SXEnry5Qri8TgikQhGR0cxOjrKwuMrKyuZ65lnzpY7/3pdYvwx+YmaNHzFhNf0/JF+io5DTArPvPFjjkqlgslkQn19PWOlBEGAy+WC0WhEZWUl9Ho9cyGuFBT1RcwuGSrBYBBOpxPd3d0wGAyMgaRM0PPz85iYmChwnxmNRtTW1uK6665DVVUVS/ZH/SHju9S9WS/bt2WMTCQSQTAYRCgUQiaTgclkQjabZYWyUqkURkdHMTg4iBdffBGTk5MYHR1l4XWdnZ3o6OhARUXFNSdyNJvNrNr1Tsf8/DxmZmYgCAK0Wi3cbveOFVdvNGw2G9RqNZvUdu3aBZPJtO7BXKVSsXDMZDKJnp4ejIyMsHTltbW1mJmZQW9vL4LBICviB1xd3dG96OrqkrQxa4SY2eA/p7o1wWAQarUaoVCIVTQWBIGNT2NjYzh16hRGRkZw+fJlWK1WWCwWtLW1oaWlBVVVVdDr9QXnE0/gKzVgSrW1mBFDn4sncp6BkslkbOXPG3N6vR5Wq3VR1ffNcNMUuxYEcT950P0Jh8MIh8PMOOOzENMxirmsimmNVsvMFNuWDBgyGMlg5QW9/LlzudyiPD/iGllAoWaEMjrbbLaCjMbEwhiNRmbErCYggZge+iGDJR6PQ6fTQa1Ws/pNwWAQMzMzCAQCLJMvlfygYImysjJ4PB5YLBZmyPH5YYqxhMu5F1eKbZlZu7u78dd//dfQarWsbHw+n8crr7yCWCyGeDwOk8mE1tZW3Hnnndi1axduvPHGorH51wIOHz6Me+65B3a7fbubsiwo7f/s7CwrkPdmMWRokD948CByuVyB62c9MJlMaG9vR09PD1QqFaLRKCsAp1AocO7cOaaRKebSc7vduP/++3H06FHccccdbKKUsDrQilDspyeXSzQaZVE94+Pj6Ovrg8vlQjQaRSwWw+XLlxGLxRAKheB0OrFnzx7ccsstaGtrw+HDh2G32wueGUpyyBsZqwU/CYg/I5AwVLxQIraAMv0CYHqHYDDIFiuNjY3Ys2cPSycPXNVlbCQjw0/sxZgY+r6YC4L2I+GsRqOBXq+HzWZjglLxcZdjZjZC80Psy0r0T3z2XL4tpG0Sh5vT8ZVKJQwGA1wuF+x2O2OAyHihiNLVBpGQYVtZWYkjR44gHo8jEAhgenoawWAQL7zwAqurREYk/0zL5XKYzWZUVlbi6NGjOHz4MI4dO1ZQEJkXx2+m3nHLql/rdDpYLBZUVVUhk8lgfHyc+TdJ9ZxIJKBQKFBfX4+ysjJUVlaiq6sLbW1tKCsrK1BtXwtQqVRQq9Ww2+0oKyvb8a4lmUyGyspKeDwehEIhJlK+Fo3HtYBWUhvNnGm1WlRUVMDj8aCxsRHT09OIRCIsoR1Fj4ihUChYnZb29nZ4PJ4dLRbf6eDZCvFKkCZGi8XCBLFzc3NMU5JOp5HNZqFSqeByuVjx1/b2djQ3N6OioqKAiSGIGZTVGDT8xL/UPsXEvfwEKY7MouRpKpWKafcokyyvy6Cf9WgXxP2g/5dyKZU6BkWS2Ww2VumeWCRxgMVybdgoUNvFFcJLbcvfC/ExCGQUAVfvHW+wkCFD94tnOlZ7n8iYKSsrY9o7KrlA6UPomeETvBKLQ/NFa2srPB4Py6FUTNe1mdgSQ0ahUKCurg4ajQYPP/wwuru78bvf/Q5GoxFWq5Upz48fPw6Px4O77roLDocD5eXlJYVr1wJsNhvcbjczwnZ6HxQKBd7ylrfA4/FgYWEBdrudrTIlrB02mw2HDh2CXq9HZ2cnvvGNb+DcuXMseqQUDAYDHnjgAbS3t+Pee+9lQkwJawcN/DwUCgVqa2uhVCpxyy23YHZ2ltWx8fl8cDqdcDqduO222+BwONDS0oLKykrU1NQwjQHPwognolL5QzbqXhZbIFH1YiqoClxN2BmLxaBUKpmR7Ha7YbPZFhnwKzWkVoNiDJMYpCXiNTIqlYrVttLr9WhoaGCh11qttiCkfjO1GKWwUmNMvA3loiGQe4ryGfH3hA9DF7sT19Mvg8GAxsZG5PN5lJeX4/nnn8fIyAiLxqMK1zzKysrgcDhw2223obm5GW9961thsVgYe081vwBsSXTlljIyDocD1113HZxOJ8uASYnI1Go1tFot7HY7EzGtNAZ9p8JoNKK6uhpWqxU6nW7HGzIymQw1NTVQq9V44IEHUFFRwbL6Slg7aDIrLy9HW1sbbr/9dlRXV6Onp6eg4BytclQqFcrLy2G323HDDTcw8Vypat4SlsdS142ylioUChw5cgTBYBDt7e0sWRi9v3QfKisrYbFYWOI7WpGLWYFi7Ab/2VrbXkzvwX9Hn4uzvQqCgFgshkwmA4PBAKPRyDRgxNLw51xrAsiV9qNYnwAsyvNDEWZarRZNTU2wWq0Ih8OwWCxM7MsLqUudp5TuaCNAx17KvUXbiNkWwnJiZJ4do2duIwwZqnZtt9tRXV2N5uZmVkU7kUiwMHwSKSsUClRWVrI6U6SLIe0qL6rfyHYuhS3TyFC12MrKyq065baDwsyp8ulOh1wuR0dHBzo6OnDbbbdtd3PecHC5XHC5XKiursb8/Dz+7d/+DfPz80in09DpdEw4ajabceTIEbhcLtTX118TIvFrGXK5HFVVVaiqqkJnZ+ci8eZS5TloW76YIhmu4kms2N+bAXI7iHOK5PN5Vk7B4XDAbrfDZrNBEATE4/ECfRYxV1vhFuBBwmqKwqLJU6VSQaVS4dChQwXRP6tNlinuy0b3jZ4ZmrzpHvDnIdaumMueDKFSRiT/mdidtNa+kAaHAmkEQUB9fT2qq6uZa5UYMaq55HA4WB08i8UCq9XKjLRUKsUi3nit1RtCI/Nmx7VyDa6Vdl6LoBUVFbh74IEH2ARCFYiJlaF8EeutnyJheRSj++l3KaGuePXNTypLsRkbfS/Frkmi9Kkd5GaJx+OIxWIsoeKBAwdgs9lQWVkJp9MJjUbDcuRsl+HM54EBFkeZkdiX+sWn41+KpaJtNhPi+1CMhSOIw/DF+/F5i0ptU0x7tVrwBhAZM3K5HLW1tXA6nSgrK2NsDGnIqIAz5e7hA3Co7RqNhuXP2Qxmrxikpd4Ggm4aCbP4kDgJEoArz4jBYIDBYMDNN9+83c2RUALFksmJ/ycGYzk30laCRL40FtEPrayNRiOrXWY2mxkro9FoCrL5bkf7+aRppULX+batxuW2FRC7rcTtLCZ+LbXPcthol59SqYROp2Oh4ZlMBi6XCzKZjLEwZOgsNZ+RS3OrIRM2Wsb9JkYul0MkEkEoFMLIyAiMRiNsNhvKy8t3fEZfCRIkXAE/IS63DaEYvb9Zk2mpdhGbwetdiNGjyCtyZaZSKVbHh2pKUWbYUoUHN9M44I0W3sXFGy7FDBv+u2LYKoNGHKnGn19stBTT6SzFKm0Vm0TXlxesiw3i7TLQl4NkyEiQIEECh2KGjHhyKSbMXK9WYbUQt48mH5qI+LaQjodcmeTOpBQRVNxQPFntxEnrWkUxVo+/zqWMYwnLQzJkJEiQIEGCBAnXLCTxhgQJEiRIkCDhmoUk9pUgQYKEZbAa2r8Yyb3Vepli5+VdZkvl9liur9sppC3WjuXyr1wLKCVu5vtYTDezldFY4igy+r0TrrlkyEiQIEFCCRQTca504OYnn83QP5QSmC51fDJiKLJJXGiw2GS6kW1eLUoJr3fC5LkREBuXSxmmYqOB19Vs9vXgEz7ybSkltF7OOC62zXogGTISJEiQsATWIiPkw4g3c/VcKudKqfOREJiSrpUqm7CU0Hm7sZPaspGgCCEefCQR9Zvy0GxFfhZCsdw3/Ll5Q2e53Feb8TxJhowECRLedFjNKrJU/o/lkuWJj12MoVnLgF7MeBHntRGzLMXaxk+cxdwzpQy4rWIBxOcTT97F2rcco7HUNtvl/qNzFzu/TCYrMIrp91Zd+6Xcd/SbjBiKhlsqz8xmxRZJhowECRIkFAEN0OLJM5fLIZVKIZlMwmg0shoz4n0pOeZmQRxuzdfwERdOpG3IeClWaJI/Lh2n2OfA1rMidC/EEz6v9RFvy/dju7UcfHg89aNYBmVx6Dy5/vj7tR39SKVSiMVi7FoaDAbWJmonPYt8LiIxNqvtkiEj4Q0FPqlWKpVig3s0GkUoFCrwQ9Ngotfr4XQ6WW2XrUQ2m0U0GkUikUA0GmUZoSnbqoStBz0flHtFrVYXZV2KZaHlay4VS4bG/y1maTYKYoEv/yNOMJfNZotqZIrlNxHrITaSmVnPSn25dhS7xssxZuvBckwWb4DmcjmWdTmZTCKRSCCbzQK4kszQYDBAo9HAZDJBrVZv2ZhAzwblHorFYggEAizfUHl5OXQ6XUEh26Vy4hS7Rxv5/Eh5ZCS8oSAIVzKbJpNJ9PT0IJ1OAwCeeeYZ/OhHP0IsFkMqlQIAqFQquFwuXH/99fiv//W/wuFwwGazbWl7fT4fnn76aXR3d+OZZ55BW1sbGhoa8PDDD6OxsXFL2/JmwlKuJT55XCqVgtlshlqtLmAoxK6lTCaDdDoNn8/HiurxK1N+EiO9w1qZgtUM2VTBm3QL/MqejGez2cwmSHp/iNURT0qlyjKsF2J33FLHXEqYLN6vGEvAMyPE6GxkgUwyAoDibBBlWZ6ZmYHP58PFixfR19eHc+fOYXx8HAsLCwAAvV6P3bt3o6WlBW95y1vQ3NyMpqamdbdvJcjlcvD5fPD5fHj11Vfh9XoxPDwMi8UCk8mEo0ePwu12o7m5mZUkEN8LvnYWXWPxNhul87kmGJl8Po/Z2VmEw2EMDg7CarWis7MTWq22KK273YjFYhgfH0c0GkU4HEZHRwdcLtd2N2tZCIKASCSCdDqNSCSCRCIBv9/PBjylUgm1Wo3rrrsOFoul6Ep1OxEKhRCNRuH3+xEKhfDSSy8hmUwCAM6fP4/Z2Vkkk0mWwl2pVEIQBAwODuLUqVNobW1FS0sLdDrdpjMz2WwWg4ODmJiYwKlTpzAwMIDp6WkolUrEYjE899xz6O/vBwAYDAZ4PB5GM9OPw+FY0kUgYTGKiViLhRcTW0fiRQBscqLq0vR5Pp/H6OgoQqEQvF4vrFYr5HI5jEYjDAYDOy4vtBW3hc67EqzmnSP3FhlhqVQKiUQCXq+XlVOpr69HWVkZq6VDxorYbbNZriWx26WYW4LYAaqsTFW8g8EgYrEYIpEItFot1Go1mpubYTKZ2KKklG6DN2TEQtv1apeKIRKJIB6PY3JyEsFgEMPDwwgEAhgaGsL4+DhGR0cxPz+PaDQK4Mo8Mjw8DIVCgcHBQeh0OlgsFhiNRmZYb/T4KwgCEokEEokEhoeHMT09jbNnz2J+fh7T09MwmUzMnTo5OYn5+XnodDoYDAZW8JZqNmm1Wvb8idmajeZPrglDJpfL4cyZM+jp6cE//MM/4ODBg/irv/oruFyuHWnIzM7O4rHHHkN/fz8uXLiAL3zhC7jvvvu2u1nLQhAETE1Nwefz4fLly5iamsLJkycxPj6OwcFBGI1G2O12fOtb38Lu3btht9t3lCFD7ezu7sbExAR+8pOfsEGh2KSRz+cxPz+Ps2fPIhKJ4B3veAeMRiOqqqpgNBo3ta3JZBKPPfYYLl68iF/96ldIp9PI5/MIBAI4f/48XnvtNajVagiCgPr6erz3ve9l9XCIYj5y5AisVuumtvONiuVEoUSh89snk0nG1mi1Wuj1egBXJtlnn30WIyMjGB8fR319PeRyOWpqagoMGTKgxWLczXyHxAbXwsICxsfH8eSTT8Lv92N+fh533303urq60NzczKpgk9FFVabFLln+Wq0XtHKn66PT6dh3NOklEgkkk0nMzs4iFArh/PnzmJmZwdmzZzE6Oor+/n6UlZXB6XTik5/8JNrb23HgwAHI5XLmPuNBxxW71taraRLrqngjcGpqCiMjI3jqqacwMjKC7u5uxGIxxGIx5HI5ZigTUqkULl++jEgkAo1Gg2QyCZlMhubmZuZ63oxnZ35+HnNzc/jd736HwcFB/PKXv0Q8HkcymWQFJM+fPw/D/8/ee8ZGml1nwk+RLFbOObBYzKFJdg7T0z0z0mg0ox2FkWR7bY29XuxisWssDHgBY/9s+LXphwNgwwt8a0D2SjAsOawsjUZx8nT3TEd2N8lmTlWsKlbOuYr1/SDO7VtvF9nMZI/4AAS7i1Vv3fuGe895znPOUSig1+uh1WrR1dUFvV4Pk8kEm80GnU7H7n+lUvnEPcNrgfYCR9qQqdVqyOVySCaTeOeddzA+Po5sNotyuXzYQ6sDPRDlchmxWAwrKysIBoNYXV3F6uoqpqen0d7ejq6uLshkskMXngGPNSRerxdjY2NYXV1FLBZDKBRCLpdDKBRCKpWC1+tFKpVCrVZjgi/yig47Kkk0fTqdRiQSwbVr13D79m0sLy8jHo+jUCjUNaBr9HkAyOVy8Hq9uHbtGnK5HL75zW8yynQvr1MqlUIymcTo6Cg8Hg8++ugj+P1+lMvlOi+4Vqshk8kwKnZpaQlvvfUWxGJxXX+cyclJ6PV6WK1W6PV6dHR0QC6Xsw32GJuD6qnQJioSieq8SKCetSHDkvoUAWD0+507dzA/P49YLIampibkcrm66wqgrhljo1Tbp6GR7oBnAYSMCb9BE5uxsrKC5eVleDwehMNhBINBzM/PM9aP5s+Pkz6/trYGsVi8bwYYn+JbqVSQSqUQDAYZMxCJRBAIBJDJZBAMBpFOp7G6uopEIsGMr7W1NUgkkrp1ls47dfYWzo0/ZxtByORtZNQJjZdisYh4PA6fz4fR0VFMT09jdHQUwWAQiUSCGS/88eheA9Ydnkwmg/n5ecjlcohE6w1AbTYbOjo6WKPP3VyLarWKcrmMVCqFRCKB8fFx+Hw+3LlzB4FAoE63U6lUUKvVEIlEkEwmEY/HoVAokE6nmYOlVCohk8nQ19cHrVYLu90OtVrNfqtUqg2Zvqddh41w5AwZ4Q2TTqcRCoXw3nvvYXx8vGE++2GCbmqibFdWVuD1ehEKhRAOhxEOhzEzMwOr1Qqr1QqpVHroRgywvojn83nMzs7iBz/4AR4+fIi5uTm2GJBokR8raU9KpdIT3sNBghf0lkolxGIxzMzM4Pr163jnnXeQSCSYNmYryOfz8Pl8KBaLmJubw5kzZ+ByueoyP3ZzzWi8yWQSXq8XP/7xjzE6OopHjx4xL4u+g+6nXC7HPpdOp+H1etl7mpubIRaLMTo6CqPRiJMnT6KrqwsqlQpGo/HYkNkEwpTRUqmEcrmMXC7H2AtiIYQ6GIlE8sT6FA6Hsbi4iHv37mF2dhalUgkajQb5fJ45XPQZXoexF/cTH47hxcfA44wj+l0ul5HP5+H1euHxeODz+RAMBhEIBLCwsAClUolLly6xz/C6ETpf1Wq1zpDZyZh5CI0A/lmrVCqIRqOYnp7Gu+++i6mpKSwtLcHv9yOfz2/6HTKZrM6QoRChMIzEX9utzok/5/wcGq0TpLOi8MyNGzcwNjaG2dlZxhRTRpLwXJBmqVgsIpfLYWFhAU1NTSiVSiz8bDabN80Q2spcgMdObTAYhNfrZSzXvXv3WAiPxkXMEekMAaC1tRWRSITtD8D6vT4yMgKTyYTe3l44HA6cPXsWTqeTMTTbEWc/DUfOkKH4p9frxdzcHG7cuIHZ2Vl4vV6o1Wp87nOfw4ULF2C32+to28PCwsICvF4vbty4Ab/fj8nJSYhEIuh0Ouh0OnR1dWF8fByBQADDw8PMWj1sxONx/OIXv8CtW7fw0UcfIZVK1VHfCoWCxdZJUd/e3g6HwwGHwwG9Xr9tj3KvQJqp5eVlfPe730UoFILf78fKygqSyeSOjSzSB/2f//N/8P777+Nf/st/CavVCqPRuKuNp1AoIBaL4ec//zl++tOfYmJiAuFwGKVSiXlfdrsdAwMDmJmZgdfr3ZTxIgNueXkZq6ur8Hg8cLlcCIVC+NznPocXX3xxx2P9VQFt1pR5QYZLo6wjftHN5XLw+XwIhUIIBAK4f/8+FhcX4fP50NLSgitXruDChQs4e/Ys1Gr1nrOvZLTQOElTInTwyuUyMpkM/H4/QqEQHjx4gGAwiJmZGZTLZbS0tEAmk0GpVGJubg6JRAKXL19GS0sL0/gIBb18zZntzKmRdoT/PF/gjZBOp/Hxxx/j3r17+OlPf8o0e8ViEc3NzbDZbMzgyWQySKfTsFgs6OjogFarZYaM0PDgWSreiNiK3oQ/9/TvRin21WoVi4uLWF1dxbVr17CwsID79+8jHA4jkUigWCw+4SAKmTX+mJVKhellyGBYWVmBwWCAzWZDZ2fnjo2ZarWKZDKJhYUFPHjwAA8fPsStW7fg9/sRjUYhEongdDqh1WphtVqRSCSQTqfh8/mQy+WYYZNKperCYyKRCI8ePYJEIsGjR4/gdruRy+Vw8eJF6PV6xiQRGhnh28GhGzL8jVYul5HNZrG6uoq5uTmMjo7ixo0bmJqaQqFQgMFgwNDQELq7u6FUKg9F6EjjrVQqKJVK8Pl8mJ6exq1bt7C0tIT5+XkYDAZ8/vOfZ15MMBhEKBRCJpNh1NxhszLFYpHF9IPBIFvYKHxB9F+1WkUqlUKhUIBer4fT6YRarT4UbRKd+3K5zCjx9957D9FoFLFYbFOmjsIxQL1XXigU2Ocom2B0dBSrq6v44he/CJlMBoPBsKtxl0olRKNRzMzM4NatW4jFYsxzaWlpgUajgcPhwPDwMBNWE51LCwOFMkSi9QwZCqmlUimEw2Hk83lotVoMDg4yCv2w77GjgM3uCRJN0+ZEWg16bunvFLbgnSzSY1GmSS6Xg1wuR29vL7q6umC1WvfN0BeGlIRGRrVaRalUYqHhhYUF3Lp1C4FAAOFwGAqFAt3d3Sz1N5VKIZ/PI5lMIp/PN3RSyKjZjREjZLg2CykUi0X4/X74fD74/X5mrFEassPhALBu8KytrbHsK7PZzFKWNxoL/z20mW6VeRUKkwHUaakohOfz+bC0tITbt29jaWkJExMT7D7ijUP6N7GAdC3lcjmLSNA6QMkMHo8HIpEIoVAIMpnsCVH2dtilarWKfD6PSCQCr9eLmZkZLC4uIhwOM4fWbDbDarWis7MTwWAQsViMMUoUkiVmSCwWs2cpmUyiVqshGAyiUqmgo6MD3d3dKJVKaG1tZeeNZ8v467SduRy6IQMA0WgUiUQCN2/exNLSEt577z1EIhG2+RNd297ejm9+85tM5HsYC3WlUkE8HsfExATeffddxhaFQiFUKhWcPn0aJ0+exB/+4R9ienoad+7cwQ9+8APMz8/X0c1HBTqdDsPDw+jv74fb7ca5c+dgMBgglUqxurqK999/H3fv3sW1a9cwODiIy5cvQ6PRHBobQ0bhn//5n7Osn62c0xMnTuD555+HVCplBnA0GsU//MM/PKG7IsPggw8+QCQSQXt7+65EgKurq/jJT36CBw8eIBwO13n9NpsN/+7f/TsMDAzg0qVLGB8fx/z8PFZXV5FKpTA5OQmxWAyn0wmJRAKJRIJ79+5heXkZXq+X0eyZTAZzc3PMMNXpdEeC+TsKoE2HjJNGGWlkmPObXalUYmxaKpXC3bt3mXaANioSAJNn/PWvfx1tbW11zwfpcCideTfrFm0aG2XYFItFBAIBLC0tYXR0FJ988gnGx8cRDocBAOfOncPAwAB++7d/G4uLi5iYmMAHH3yAlZWVuow+Yv1I9MyzPpS1tR0ImaTNzgWNQS6Xo7u7G6+//jqcTifMZjNcLhfUajX0ej1CoRB++ctfYmJigmWHXr58Ge3t7dDr9UzIu1kxuZ2uY+QokLFLWFhYgM/nw1/91V9hcXERY2NjTFNIzhTVt6I5Op1OtLW1obu7m2X/kGF57do1JBIJRCIR9pmVlRXmiDY1NaG9vR1yuXzbEQoKrabTaaysrGB2dhZjY2NIpVIQiUTMwfrqV78Kl8uFgYEBZshMT08jkUggkUigubkZKpUKCoUCarUay8vLCIfDePjwIZLJJPt59OgRenp6EIvFWIYTjUOY1n9goSXewiZs5AWS6pw8HfJ6YrEYkskko9zu378Pr9fLYoipVArAutVMNJrJZNoXyvZp86S8+kwmg0AggEePHmFsbAw+n49Zr1KpFB0dHejs7ITdbkc4HGaeAS1mR0XfQ+EMetB7enrgcrkwODgIvV7PHjqJRMIWfr1eD4fDcShp13SzLy8vY35+HrOzs+yBbiToJe9NJpPBaDRiaGgIw8PDdanVq6urkMlkbKGh7ymXyygWi0in08hms7seOy14xCbxcxKLxbBYLLBarTCbzejo6IBEIoHJZGIppS0tLey8t7a2olwuQ6lUMoOLxpxMJlmKp1qt3vW4n1XwaxMt1sQ+AKjzIIkZ4I1LYlvpfAYCAcTjcRYe8Hg8CIVCiEajLAxA65PNZoNWq60rlicsMS9kI7b6LAl1FHTMQqGASqWCdDqNTCbDmOHx8XEsLCwgEAhAJBJBqVTCbrejra0NTqcT2WwWy8vLAFC3uQJg96qQqWgUJnraHITXo9G5oBRpXtdiNBohFouhVCrhdDphsVhYRqFSqURLSwt7Pmq1GtRqNUwmE3uNdBx8PRyeFdru+af3Nup1RGEVCnkvLi5iZWUF6XQaItF6/R5hpppcLmfyg66uLgwODkIul0MsFiMejyMSiWBychLVahWxWIxdn0KhwLKdcrkcSqUS02/tZF2ma02GO7Ejra2tkMvlMBgMsFgssNvtkEql0Gq1aGlpQSqVQiwWQ3NzMzQaDTNkmpub0draivn5eeRyOQCPJSOZTIaFpIR6M/4abRe7YmRIKAeA0WGNUK1WsbS0xPL9C4UCcrkcfvCDH+Cjjz5COp1mdDlfcpvQ3NyMK1euYGRkBFqt9sDDGrRIfO9738Py8jImJibg9/sxOzvLLvrw8DBcLhd+7dd+jXnw2WwWfr+fCTd3epH2A0ajEb/9279dt4jwTeToZ2lpCbFYDADgdrtx+vTpQ9EmEV3+3e9+F++99x48Hg9KpdKG51MikeDcuXPo7+/Hb/7mb8JiscBms9V5s/Pz8/jOd77DhJD7Bb1ej+eeew5TU1PsNeHiTvNwu91wuVxPCDh5r+/FF19EKBTCzMwMC6lR5lUwGEQ0Gt11OOxZBjlKZKAmEgnk83kEg0GWeadQKCCXyxmzQFWfRSIRyzKZnJzE4uIi5ubmkEqlGEOhUqlQKBTQ0tLCiq2dO3cOIyMjjDkjQXy5XGbGs7CR5F6xmisrKwiFQvj000+xurqKR48eYWVlBZOTk+w7L168CLfbjddffx0ulwtGoxGLi4vI5XIsRMlvzqSxoQ1NGA6ic7WdOTQKidGxKpUK06u0tLRAp9Ph+eefZyFvuVz+REYVhZQp9G0wGJgjQAYs1b4i0B6zE3aMFwoLkc1mWe2q+/fvY3x8HKlUCk1NTZDJZFCr1UznQw5MX18furu7WZbk4OAgRKL1shBer5cljhBDS8UNKZMpkUiwufNC9O0YxmKxuO6H6tOsra0xDRUxYA6HA3a7HbVaDZcuXUK1WkU0GkVTUxMUCgU71yqVCiqVCmNjY+weKhaLiEQiLAGGr6vG3xc7dZB3Zcjk83mk02ncunWL5bo3Gki1WmU0eDabZR7P9PQ04vE4mpqamBeqVCphsVgQjUaxurrKvJ7+/n4MDg6yG/AgQLFJinPevHkToVCoTlTa1tYGm82GM2fOoK2tDe3t7UwcqtPp0NfXhxs3bhzIeLeDpqamuge8ESg9mypN7nfvmM2QTqcRjUaZJyxMbSXQQiORSOBwOOByueB0OqFSqSCRSOqoYI1GgxdffBETExN477339m3stDg38gILhQJmZmagVqtx6tSpJ+qXCEEx59nZWZbxRKwAX2fmV1Efw2+S5BCRkUrZj1Rqne5l8mhpIxeLxUilUuw5J1E23U9KpRJWq5WtT+l0GgBgt9tht9vZtSPngITEvBh3qxuo0EgXsjilUgmlUglzc3NYXFzE7du3EY1G4fP5kE6nIRaLYbfbYbFYcPnyZbY+GQwG9hzQ5sOH6smgEbIIvNe8HfAGQCPRbaONjETHzc3N7LnljZ9isYhMJsNYcjom/5w1YoA2O8+bXZON/kZMDDF1CwsLWF5eRrFYRK32uOgmn46v1WqhUqmYIeN2u2E0Ghl7T+8pFAqwWq1IJpNQKpVs7+TvbzLMduIgEzPZ2toKhUIBlUoFjUaDbDaLUqmETCaDWCwGr9cLpVKJSCQCpVLJCIu1tTUWHaFr1NzczD5LGa4A2HUkbYxwLRQ60NvFrgwZUi//6Z/+KWZnZ5/Y5HiLm0RLxODwg6UCOhcvXoTL5cKVK1cwOjqKDz74AA8ePEAymcTly5dx9uzZp26+e4lSqYRsNovvfe97+OijjzA3N1eXdlar1XDq1ClcvXoVFy9ehNVqhdvtZheK6jL89Kc/xfj4+IGNe6+QyWQwPj6+r2zFVhGJRDAzMwO/388Yoo0gFouhUCjQ29uL3t5eOJ3Ohl6UXq/Hv/7X/xq//OUv8f777+87WyZ8QEUiEdLpND766CNIpVK89NJLzLPZDKOjo/j444+RTCYBgG2Yer2eVf486J5RRwkUKiK6PJVKsUJwVOcpk8mw0EoymUQ2m2XiRtokaZOw2+3Q6XS4fPkyLBYLent7MT09jXv37sHv96NYLDKRL19/prW19YmEhJ14nkLWjhb7fD6PeDyOmzdvYnR0FO+99x7b1OVyOTQaDZ5//nlcvHgRzz33HCwWC9RqNRsTbWK0gfEl5JuamliIhsdOQzJCQ+Rp4tqWlpY6VpEK4ikUCohEIiSTSUSjUXi9XiQSCTafzRzdRoz4TsMxBEqxXlhYwJ07dzA6OoqZmRkUi0XmLBK7RfO12WxwOBx4/vnn0dnZicHBwbp9TSQSsd5vXV1dLNFibW2NhWp4Y2ajYoVbATFeOp0OJpOJGelUIb1Wq+H+/fuoVqssK4wMGZFIBJVK9cT5TCaTrA4ZMVAtLS1Qq9WQy+XM6OHny99nB561pFKpYLfb8a1vfYt5J4lEAouLi4hGo4hGo2ygVAmzu7ubxfptNhsMBgOsVitUKhXb+BUKBfL5PGZmZpDNZhntddCLM1GeVIiJb+blcrlw6tQpvPDCCzh9+jTa29tZfJBupng8jtnZWab1Ocb2kUwm4fF4cO3aNVy/fh0rKysbGhwulwsWiwVutxs2mw0vvvgibDbboWfwSKVS2Gw29Pf34+LFi5iZmWEsV7FYxPLyMm7duoW//du/xfDwMLq6ulisnwefvRCLxep0HVqtFqdPn2af/VVvOMl7d+QpEvOgVCrZsy2Xy1GtVlk/JTp3crmcec5OpxMajQadnZ2MpZidncXCwgLLwNBoNNBqtXWMgpB1oHEJx9kIQiaBZx/pM+T5BgIBeL1eJm6Vy+Uwm83o6upCb28vOjo6WPsB/pgSiQRarZZtLFRmAXhs5DRiYIVe89OYjKfNnWdStqq9oetHmg7y9HnPnt8wyYgRFiXcKWq1Gkv8mJmZwf3793H79m0Eg0G2eZOehb8mer0e58+fh9PpxNDQEMxmM1pbW1l2KGWO0hj5bCb+nNA9zQuxt2sA0DFbW1uZxqu9vZ3JCWj8JKFobW1FKBSC2+1GW1sbyw7jWbJCocBKQqRSKRSLRchkMpjNZgwPD6O7u5tpbYRMHz+mbRtk23q3AKSwfuWVV1iVRa/Xy24mskIBsEZq586dY/HpgYEBdHR0wGKxsDgoFZWjsEZTUxPzIg7akCHPjkRNwGM6zmq14vLlyxgZGUFXVxe0Wi3EYjGzlMnb9nq9zIp+VtCIjt3tg7+TMdRq6+mHlLb8zjvvMBaCxgQ8Dnk5HA709/fjzJkzcDqdOH369KbF4Wgx4g2C/QAtFB0dHRgeHmZVPUmku7q6yjbIWq3GYtP0GqFSqbAwCYU26Ryo1WoMDAywmg+/6uCNGKEho1AoGFtDHj5R5lQjiUSxVqsVLpcLSqUSGo0GhUIBKysrAACfz8cWc6VSyUoWAI+L321kdO+EkRF+jjbyWCyGcDjMmkOSQLO9vR12ux1ms5lpdPh7hjq/kzaFzgltLPT6RsLk7WgxGv1bGGbazOgTbnA0FwrhkNHFMz18iJXO4U42/EagtSOVSmF+fh7T09N49OgRYrFYXTFO0kiRQdze3o5Tp06hra0NnZ2dUKlUEIvFLAQq3Ofo3AivARl/JFrfTeFOsVgMrVYLk8kEu93OtGMkAVlZWWGZapQIoVQqWbo1fTdlQEUiEQSDQZYNqtFooNfr0dPTg7a2NhiNxicYqN3uLbuyDOhEOp1OVCoVdpFeeOEF1pKch1gshk6nYydfoVCwmD49+IVCAV6vlxX+GRoaQmdn5773vtloflKplPWQIC+4XC5jenoa3/nOd/D2229Do9Ewj+yVV15h9DP1p9jvjXKvUa1WWWVi8laJelSpVAeikymVSlhcXMSdO3fw//1//x98Pl9dSW8AzIM+d+4cvvGNbzCPx2QyMQpzM0SjUfzFX/wFpqam9jWsRPqVs2fPwmw2Y3FxkTWwpO8Nh8OsvcIPfvADvPnmm+jt7cWJEyeY0HF8fBwffPAB3nvvPYyNjSGfz0Mmk2FgYAAXLlzAm2++CbPZvG/zeFZABiE5FkqlEk1NTejo6GCsVrlcrisASUkEGo0GUqkUKpWKrU1UvKupqQnZbJb1Icvn86wjscFggEwmqys5T4szJS9sJhbdCMKNn/+sWCyGXC5n4spQKMSys1ZXVwGss8K3b9+GVCpl94rFYsHIyAiWlpbw8OFDhEIhlrXEb/z8s8ZvNtudw3bnSeF7npGkDZOq2/r9fgQCAaYfcTqdrGwEjU0YvtjLZ7xSqWB1dRUzMzO4du0axsbGmANOoPB2f38/rl69ylLIjUYjC+cI+3kRqGXAgwcPMDU1hdXV1Tq2TCwW14mgdyO5kEgkjMGr1dbTu1UqFcbHx1mdIersTkUVM5kMnE4nOjs7IZfLoVQqsbCwgImJCdy5cwcTExPI5/OQSCQYGBjAyMgIXnjhBbS3tz8RWtoL7NiQ4W9soddLxYp2gkqlglgshlwuh7W1NZhMJqZEP2iQ4E2r1cJgMDAvuFarMdpsZWWFxZl1Oh1sNhuSySRkMhlrcLadcvlHAVTELxqNMvU6dcc9KCEpMWGrq6uYnJxkokwera2tcDqdGBgYwJUrVyCXy1l58q2wd8ViEZOTk6z8P/D4vqbFYi+KLtICTJ6Iw+FggnYSbBYKBbZoeL1enDx5sk4YqNVqsbKygvHxcfj9fqYLUCgUcLlc6OjoQEdHx4FqyI4ahBstMTGU0aHX6wE8NnTIkydhKd+5lz4j1FBQOIFYVsrmkMlkTOjIj4UY2u3WXtnKxtvS0sLYIGKtianI5XKMpQmHw6wbMa1dBoOBVcPOZrNPsBVCXY5Q47Ib8PPiDTz+e4XvpWcSWF8bEokEW1vJoaH1aaPj7gXoOFSQkjRX0WgUuVyu7jslEgmcTid6e3tx9uxZWCwWJmAmXdtGGzpl162uriISibCUZeCxFojqSonF4l05l+RoUfXetrY25PN5LC0tsewvSvWWSqWoVCpwOp0oFouQSCRQKBRsfZqbm8Pq6iqSySQztO12O8t4UqvVbM684czjwMW++4F0Oo3x8XEEg0GIxWJcvHgRr732GmvJfpCgRe7KlStQKBT4q7/6K2ZxU0iCWKdoNAqPx4PZ2VkoFApYrVYmxuI3ymcB2WwW3//+9zExMYFSqQSbzYYrV67AbrcfWDVlis/mcjlW/loIh8OB//yf/zPcbjecTue2Ve/Uz0WoYaIu352dnU8UN9sNyHv6N//m3+ALX/gC3nrrLXg8Hty9e5d5v5Ri+X//7/+FXC5Hf38/TCYTBgcHMTk5iTt37rDiZsB6SInSN49KH6/DBhky5MUrlUr2TJJhw78XqA9v0GskMCXmWS6Xo1gsYnV1FZVKBSaTCZcuXcLVq1eh1WrZPdvS0gKFQsFCH3xX553cSzQmYknI4FAoFBCLxRgZGUGtVkM8Hkc0GmUNU5PJJPx+fx0TdPv2bWi1WvT397M+S3K5HGq1Glqtlm00QvaoUdr0bsCLVWljJvDXRxgOq1aryGazuHfvHh49eoRyuQyLxYLnnnuOFcuj1Hee0aHv26tnuVwus27WExMTLORNWWoAYLFY8MUvfhEnTpzAyZMn2XmlVP+NnHOqlzUzM4OHDx+y1GuCSqWCwWBg7I5SqdyVA0NGldPphMlkQrVaRXd3N0QiEXw+Hx49eoRisYhisQiPxwO/34/V1VVoNBp0d3ezYnwejwczMzMIBAJoampCV1cXXC4XvvzlL6OzsxNut7suDEVp8MLQ4k5w5AwZYjlSqRTEYjHr7nsY7QjoQSYLta+vDxqNBvF4vK6xIoHKMlOHaLK8SfD1rKBSqbB+MrVaDSqVCu3t7U+0Y99P8B7hRumFLS0t0Ov1LNy11bGtra0hHA4jEAiwYmI8KLRABRj3as70wFI9m97eXrS0tGB5eRmZTAaZTIbNOZVKIZfLYWlpiXX35dP+W1tbWSEtPuX/2JBZB78JU9dq2mSETfpIC8czMDyjQ9eNet7QdaBCYCaTiW3EPKshNIy2e30asQk8Y0FzsdlsSKVS6OjoYBlrlJpLY6eigOl0GtVqFcvLy4jFYixNWzh2+j+Ngz83e8nKNDreRseneRQKBUQiEaYzUygUsNlsdYLmjb6vUebSVr5beCxqb5LL5VhqNX8MyliiLEIS9FKLAp6VIJaf18lRJ2q+Gjx9TqlUQqfTQa1WM63KTq6J8DxQDRmr1QoA6OrqgkQiQTweZzpRXpeVz+cZM6RQKBAMBlkFYhL42u12ZiAJWxKQEbsXxvGRM2QSiQRu3LiBfD4PpVIJh8OBjo6OQ12gL1y4gKGhITQ3N2NxcRHXrl1jNzCBSnovLCygXC4jEonUHeMoNLjcKsrlMmZmZhi16HA4cOXKlc+M/qJSqeCjjz7C+Pg4MpnMEy0OdDodHA4Hzp8/D5fLtef3Hokwa7UaFhYWkM1mWTlzHuVyGYuLixCJRHj48CFbhEnL8fu///sYGRnBmTNnfqVDShuBjBB69jai34lJIaqbQktSqZR59eSkrKys4P3332dMrMFgQFtbG9sEqFkhfR9fQ2anGw7weMPnnSfaLE+dOgWXywW5XI5AIICJiQm2yRKrHAwGWRPJQqGA+fl5FtakwqbCNN791MLxhR43M2Ro86Zzl0gkEIvFWGuYWq0Gk8mEkZERxtrvt9NIhi9VUqa+aDQvyvCRSCRMawWA6UYprZ2M41QqBYlEwrJeK5UKAoEAfD4fM0CBx5lkTqcTPT09aG9vh8Vi2ZPWF8Dj+4mqvIvFYqaXWVpawtjYGHPU4/E4a51ADCT1iaMyECMjI+jv768rosr3LeMLE+4WR8aQoRYAlH9OFqqwr8hhgES/Q0NDsNlssFqtTOFPIENmfn4e8XgcCwsLzOt/VlCr1VhWDFWMpAwa0hA8q6BFmh625eVlVh1YmNp6/vx5nDlzBiqVal9EjSLRelql3W5Ha2srXn/9dYyPj2NtbY0VgaT38qwUgRrT3bx5E6lUCg6HAxqN5le6LQEPXl8iFKbSokliX1pIqXAZv4HTPdPc3Iy1tTUWuqGy7KTHIoGpMHxB4xB6nULtzVYWcmFWCh9iIkby5MmTcLvdsNvtzECRyWQsbTaTybBqvtT8cmFhgZW8FxaY3EsGho7HH5fmxd/nvKCZN6zodyaTQTweZ73ItFotC4nxzNJG54+OtVlm1NPAh91obxBmQDZik2l8fEo13Ue0z4XDYWaoLS0t1a1PxBTb7XZWnVmj0Wxr7Py53AjEnFA5lHg8zjL9qK8gFeej1jsA6sLjhUKBGZ2RSATVapVlU5LBRM/GXqyxR8aQqVQqWFlZgc/nQzKZZHVnDtuIoZtWKpXi7NmzDd9DNywZMouLi/jxj3+Mhw8fsj4nRx38QpFMJpFIJFAoFNgiodfrn2lDhhYaKqQ1MzPDvFJafOhav/TSS3jttdf21TCgWkQkVv7kk0+QzWZx8+ZNlurfiHIlrzyfz+NnP/sZZmZmcPHiRbS1tUGtVu+ZhuFZx0YbFN3npVKJiRepuq1wrSH6m855OBxm9LlWq4XRaIRcLmfrlNDgFKYX02t8g7ytrG/C8BQJMGnMKpUKarWaOVgUcgHW9V4ymQzhcBipVApTU1OIxWJYWlrCjRs3sLCwgHw+j+bm5jq2Zyuakp2GM4RGHM2Jz+6i/wtBvfm8Xi8ymQysVisMBgN0Oh1bn4RGC51nocG0k3kIjRjqR0QhF76XoNCQocq2PIgxpPf4fD4sLCxgfHycVQim8yCTyWAwGOB2u9Hd3Q2bzbbtNUpoGDaaH2lmOjs7YTKZAKwzj2Tw015HRgyFxwikIw2FQlAqlfD7/ahWqywMJixcyF8TukbbvbeOjCFD9C5dOLPZjJGRkWemZwyJCx0OB6RSKSsOdPPmTWZJ863Ljxpoo//Rj36EBw8eIJfLQalUsh5SvIjtWQR1k/3ud7+LTz/9FFNTU0xvQqB+TFSg8SCMaApl9Pb24rd+67cwPDyMubk5/PCHP2T1SoSgRT6VSsHn8+GXv/wl+vv72X12GKUKnhXw4lG+9IPwWtM5ph5AQL2I2Gg0ore3lxXB49mEWm29JD1f54M/Ln3/dvUBdK/QHPiQAx/OomqrxGoDYCEOiUSCVCoFvV4Pn8/H1iQS/FLJeepV1dLSwtLYaQy7NZQ3YqL4/zdiUCqVCsbHx1npAaVSiXPnzqGnp4c1MqRaOvQ54cbIb567mYswBCmsFUYZj43uLdK8EZPW3NzMwjS//OUvcffuXYyNjSGRSCCTybA1SqvVor29HV1dXejs7Ny2Y9nIeOGNRjLc+bVeKpWis7OTacGsViva29sxOjqKUCjEhMj8/UGan/HxcYRCIZhMJrjdbpRKJVgsloYSBV47tJNrcmR2VbpRycpTq9Xo6up6JhZlOvHU7EwsFqO/vx+3bt0CAGa1k0L7KILO/+joKG7evIlisciugclkOhSx9V6CBGqffPIJ3nrrrYbv0el06OjogE6nq6tHsZ8gL8disUCj0UCn06GtrQ0fffRRnSEj9MjX1taQz+eRSCTw8OFDAMDw8HCdJuRXmZnZyOPkDQkqPtbIiGkUGuBFtmq1mhUP40NZTU1NrJAY8CTrQsek159G8wtBQl6e0uePIxKJGmbDkN5HpVKxzZFqekkkkrqaJPzmJmSadlN4baPPCcM8fNiJ/k7n1OPxMCaVOkdbrVaWJUZp7mSM0fGE53knISXh58lQbdQ7iO+uLkShUEC5XGb3TlNTEzKZDHw+H8bGxnD79m1W24dni6kPocVigclkqmspsdHcGqHRZ/j9ly9yR13IKcW6XC6jubkZgUAA5XKZ1RrjGw+TBsbv9yOTybDsV3pehAkUPEO2U0b5yBgyQhgMBgwODj6TVUorlQoymQyj2CwWC9rb2+u67R41kIBrZmYG09PTKJfLcDqd+L3f+z2mYn+W8ejRI7z11luYn5/f8D19fX34yle+Utf876DAV2SlbrYEiimTl0dZccD6dbt27RqWl5cRjUbxpS99Ca+++uqBNlc9yhCGLID64mq0yVGvJRLr0jknQ4eKRCaTSUgkElit1jqBKW2afEVf2kz5f/P/B3a2iTY3N9el2guFk+QVU1NRmhcZJfl8ntX6qFarMBgMdbVwgMcpucK6MvS3vbi36Hj8OWl0XJFIhFgshmAwiOvXr2N0dBTVahUmkwkvvvgi3G43ex//ed4I4I2KvWCVyLijatvEmlDIiQzO1dXVJ6IKBoMBtVqNMUiRSARjY2N4//33MT4+jkAgUFdjheqU9fX14fLly+js7GTVmvl5buW6bGT4kFHG1zviGT8KwVIWViKRQDAYRKlUQnNzM9Pr6PV6zM/PIxKJsHttdHQU8XiciZ71ev0TKeOUwbRTHBlDhiZNsV+K+cpkssMe2pZBVm02m4XH42H9dIiC5FM5jxqo+FwymUQul4NYLIZarYbL5TqyGVdUPlsikcBisdTR+LTQFItFxONxLC4uYmpqqq5mDC1odL9RhhyVrT9I8OGiSCRSJySXSCSw2+3Mc15aWmKGDBUHa21tZT1SeCHoMZ7sU8QbMcDjc98oDCE0dgqFAns2rFZrXVYT8NiQ2chbpuPuZDPl38/f5/RbKCjmNS70d2qIGQwGWVIFMcZCFoM/T7sxvITjF3rg/N+F7Al9hrqXU9q1QqGATqeD1Wqt04lsNL7dGo8bHVPI2tGxqYZQOBxmBRTJGSGha7lcRjabZb2yFhcXEY/HWUiPDAzq02Sz2eB0Opmwmb6f7t2tzq8RA8bf+/Q6byDRb6ocnc/nWeaeVCqFxWKBTqeD0WhEJBJBOp1m1aIpbBYIBFhDSqlUylj+3bIxwBEyZEqlEivz7Ha7MTw8jMuXLz9TAlOqiHv37l38j//xPxAMBgGA1T6gug9HEaOjo/joo48QCoXQ0tICl8vFmnsdVV2P1+vFf/2v/xWvvfYa/uAP/gA6na5OH5DJZDA9PY1vf/vbmJiYwL17956o90Dphl/+8pfxwgsv4OzZs4dSRTqfzyMWi+Ef/uEf8OMf/xiLi4vsb+3t7fj93/996HQ6aLVa/PEf/zHefffdus+XSiW2aFI2x1G9bgcFfpMh/QoZGQTSmtDGzf+NGBTgcQG0WCwGrVYLt9uNs2fPsk2UdALE9rS2trLPCw0jqo69F8wA8Nh44lk4qpXDO0+UdTI9PY233nqLMa9UHZiyl2h8/P3DN1vcC/DhO+F559kN+s6lpSV8+umniEQiEIlEuHjxIs6dO4f29vYnmqsS+Lol++GY0PVrJOaORqMol8t4//332fPY1dXFqt5Tra6lpSX8+Mc/xuTkJO7fv88qdgPr51yr1aKjowOf//zncfXqVTz//PN16dyka6Frv1vnhZ8H9UpUqVSsF9TMzAxu374Nv9+PdDoNqVQKp9OJr33ta6zwJD0Hi4uLLHtJJBLh/v37MJlMcDqdrAo7PZ+7dbyOzEpHjAwvMNuPngz7Cao1Qbn25FVTx11hE8CjAKJHA4EApqenmedw8uRJ9Pf376r2xW5ANTmo+FMul3uidxeVX5+bm8ONGzfQ1tbGhGQUCpiZmcHU1BQCgQBrMEcgI+bEiRM4ffo0nE7nod1zPH3bKOuCDHo+bs6/p1QqIR6Ps3uPDLpfdfAGA7/h8B4oCXiFmxG9j17L5XIoFous3D/p3uj80/s201/sJrwhFArzrwu9aZ5R4dOZKWuP0peBx+sThZ6E7MXT5rQTCI/X6NxTJhXpLWZnZ5HL5dDc3Iy2tjZWaVyY7r4RM8GLf3c7D+H6VCwWGcNF4y8UCggEAlhcXMT4+DiKxSLS6TSrFTM9PY3l5WVMTk7C5/OxlGYaa0tLC+tI3d/fD6vVytYn+p6t9vETMmBCjQqv2+K/n14jBonqE9F9SOnvxOjRHs4LgIk1okwnCvXy4xAyf9vFkTJk+Iq4R6F+zHZBVnY4HK4bP/VjOSgB6XZAlvbExATef/99FAoFmEwm/It/8S/Q3d39RBXUg0JzczMMBgPsdjt6enrg9Xrh8/nq3kN9uT755BP4fD6cO3cOfX19ANYZiunpaXi9Xly/fv2JGhnAenn/L3/5yzh9+jS+8Y1vHGrIj8SYDocDLpcLHo+HLYykuaLaPrzXRhtPJpNBLpeD3+9HOByGTqc7siHBgwbPLAivMa01PFNK6aV8J+JarYZEIoFcLgeZTAaFQsESEcjIqdVqLOunEXYb2qBqsrx+hcJBQh2QUBcCrBv3gUCACWbT6TQAQKPRwGKxHChbvNmmxYdKisUiotEoHjx4gPfeew/xeBwqlQpnz57FwMAAY494/U6j7+I9/71iwQwGA2w2G7q7u1EqlRAMBuvCZrlcjmVHxmIx1v2ZhMm3bt2C3+/H3bt369K2gfV7SiKR4OTJkzh9+jS+8IUvsKrN9Hde3MzPdaug95IzK+yiTfc3vVYqlZDP51kfxJaWFrjdbjgcDojFYqRSKUSjUQSDQZaZxaNRhXbeydgNjowhQ6EluhmAZzfrolwuP9FYjoqfHbU5xeNxzM3NIRQKsZbrZrMZDoeDqcsPy5DR6/Xo7e3F17/+dVy7do15NKQP4ReMQCCAO3fuYGlpCcD6YhiNRllfk0Z6BZlMhjNnzrC+Iod5bSgDaWlpCXNzc8jn82w8iUQC165dY0YnhSyBx8+I0WjEiRMncOLECdhstg3p9l8lNLqe/OLdSGBKizqJGAGwvkXz8/MIBALIZDKsczl9lnQPjXQgG41lp+A9aJ5pIs+XN9qFIlcSovLOolqtZp3tpVIp21Bpk9zrsFKj4/AbMx8ii8fjGB8fx8rKCtLpNBwOBxwOB3p7e+FwOJ5gjuhYGzFhe4WWlhYYDAa4XC6MjIygXC4jk8kgEokw5phCZvl8HouLi0gmk5iZmYFKpUKtVsPS0hJSqRSbN2+MtbW1wel04uzZs+jp6akTx/L1jShraquOfyOGjUD3T6PMNJof6V2oJ5zX60U2m0VzczPri0cF8+gepfo3VDHYbDZDIpHs6TU5coZMMplEZ2fnYQ9nVyiXy0x0SfTbQTZc3ArooYnH4xgbG0MoFEKlUoFWq4XFYoHD4YDRaDy08VEqe29vL5RKJbLZLGZnZ1nzMh4kPFtdXd3weI2obLlcjlOnTsFisezbPLYKYlOWl5cxNzdXl76bTCZx/fp1RuvSIkYQiUQwmUx44YUXmCHzq66PeRr7QRu1MCxBqc18enM+n0cqlWLVusViMQtT0r0kfLY3EvruFfixk7CYz6QBGtP1wvomZMhQ80GZTMbmzmet7CU2Ojf0vXybh3g8jomJCZbKSyHv7u7uJ9Yn3pjhX6PvbBRq4t+zHVCmTrVaxfDwMBPpZ7NZ5oSQHop3UgCwVgTZbJaV7OfR1NQEl8uFnp4enD59Gna7vY5d5Xs2CXVQm7GBjUS+9Jv+zhvI/LGIFaZqveVymTVEps7fxNgQcwyAtS8wGo2MkTKZTHuufT1yq91+LwCHAb4x2FFBtVpFLpfD+Pg4vvvd78Lj8UAsFrNQy1EJS8hkMjgcDnR2dqKvrw/JZJJR4tsBpUVeuHAB3d3deO6559DW1gaLxXIo4l4heCZA2DahUqkwqpYPIVDV5ZGREQwPD+PXfu3XWFrmUWP+jhpooRZu1qRRorAShe0SiQQzXqjrNL/oH0TtHtJl8OPn/82LJvkNnMIGpEPMZDKIRqNMb0UZQKQ34ZtO7td8eLEv/Z+Mb5FIxMTrExMTeOedd+D1eiESiXDy5EnWPoTGKdzE6ZpuZMzvhfiXroXZbMbw8DBLwY5Go0in03Usi3Dd5w1Fvv2AQqFAZ2cnbDYbXn31VbS3t6O3txdyubzhOaOw/3Z6LW3EUvLnSshWUuiUDBih3IBCntTeplwuQyQSwel0Qq/X48yZM2hvb8elS5fgdruh0Wj23Kk/dEOGHjShOIgW9mctjZS/OeliH0XhcrVaRTKZhN/vx8TEBKrVKlpbW9HX14fh4eEjwx61tLRAqVTCZDLB5XKxEutUO2OroBTTnp4enD17Fq+99hor9HdUNv2NBHy0UPDCuObmZphMJlYBe2hoiHXTPkr32VHBRk6EMBwh9HBrtRqr0s33XqLXJRIJC9fwxkOj4wO7T18Wbs6Nwlc8g0LrEV8bhEKUZKjQs8Gn2W4mmt0pGl0DfmPmwyNkFPj9fiwsLCCTyUAikTANGbVnIPZJuBELIWRrdmvMEAtHqdF2ux3t7e3QarWQyWQs46dRUT6hgUPhF71ez6r2Dg8Pw263s6KFNGZheryQPdlsTpuxNcLQnDB1n9Ku+YKDBBICkxNGmjOqlH7ixAm0tbXB7XbDaDTuS9j7SBgyMzMzmJmZYcZMqVRi/UDMZvMz0wyPCiBR52uKX164cAFf+cpXjlSV4ng8jh/+8Ie4efMmcrkcjEYjzGYzTpw4gYGBgSNjyBAuX76MwcFBKJVK3L59G59++ilbLLaCgYEBXL16FV/72tcwMjLCyoMfFSNmKyADxmg0wmAw4A/+4A/Q09ODrq4uyGSyI2WUHVXwmwGAOm+WFmza6OVyOVpbW5HL5VhacnNzM5LJJBYWFvDpp5+ir6+vrgNxIwgzQvYafCYbVepdW1tj6ynPdpCeAnjsYFFrAt4AIlZqPwp4CpkFob6DGnS+++67uHXrFrxeL+tLdv78eYyMjLD0dqEoG9h8w95rVpxCJ729vVCr1QiFQjAajbhx4wYymQwr98AbuaVSCSLRugCZahKdPHkSzz//PK5cuYKenh4YjUZWe0zI6PPZaPuh7WtUlZg3coV/K5VKiEajrKhnV1cXbDYbXn/9dbS1tWF4eJg1Ht6vgrCHbsgAj08cAFZ63e/34+HDh3W1Go46yLKm7CSy2ukiHgUQA5ZOpzEzM4NAIIBarQaTyYSuri7mURw1qFQqyGQy9Pb2spgzpZGSpoFfpJqamqBUKiGVSqHX6zEwMIATJ07A4XBAr9cf4kwag+LpJAynOhRyuRxisRgqlYoV03K5XLBarejr64Pb7Wblyo/xJIReuDAzZCslEWjx5sW/8XgcXq8XFosFarV6y0UU9zKRQTi3jebBe+sUOpJIJBCLxVAoFIxV4jfFvczw2QjC0Biwvv5nMhnEYjF4PB6Ew2FUKhVoNBq0tbWxqrC8pmMjXYzwtf0QXxODR6xxZ2cnSxePx+NIJBIsbZm/9s3NzVAoFFAoFHA4HOjp6UF/fz9cLhcLd/Np1o3qEW2VidkIwvuHfoRlCoDHhi2lnFO9GHLWqS6MQqFAX18fnE4nenp6YLPZmFG2n8kuh27IiEQi9PT0sIkWCgWsrKzgRz/6ET7++GP80R/9Edrb2w97mFuCRCLB2bNnWYl5SpM8SpsMeTuLi4v44Q9/iFQqBbFYjKtXr+IrX/kKK9h01EBG4Ze//GW89NJLeP311zEzM4M///M/RyQSQTQarXs/1cLp6enBG2+8gba2NnR2dh7ZbB6ZTAar1YoXXngBKpUKf//3f4/V1VUMDAzAbrfjwoULUCqVUKlUuHDhAtrb2xm9fszCPB3EtpCYl1+0eQOAvE3a8AGw0uvEAsRiMUxPTzMDhgpI8iJVociUNtK9vFY0Bwo58RsFrT1CrUtraysUCgXsdjuUSiX0ej0LX/Bjo812P7AZi0CNIaempnD9+nWEw2FIJBIMDAzgpZdegsViqXuGG2lQNhv3fj0r1Kz19ddfx+XLl9Hb24uVlRXcuHGD1cChe4BqEA0NDcHlcuHFF19Ef38/Tp48yVg/mptQVN6IEdkN+HA2hSFpz+K/gxzCtrY2JBIJNDU1IZfLQaPRQKvVoq+vDyaTCXa7nXXN5g2y/caRMGSIDu3q6kJTUxNWVlagUqnQ3t5+pMIxT0NTUxO0Wi3MZjPcbjeamppYsamjgrW1NQSDQayuriKbzQJY76pKhZeO6kZPiwAVIaON/Jvf/CYymcwTAmCxWIzOzk5YrVZ0dnayRpBHyajkQd5xd3c3S01MJpNoa2tjzfGoRYHJZGICwGNsD428WF4bINTqkUEjk8ngdDrZZkK9ZSikJ1yseYNir7QmjdglEn8LN3Nea8WHBVQqFUwmE3p7eyGVSqHRaJj4UmjI7DU2OyYZmqVSiWX+UHNO6m3FN6/ls32edlx6z0GwSxSypuxBrVaLWCwGv9/PrpNMJmN6H4PBgP7+fthsNuaYAPVVpeme249wEp/txhu9pHOh8VAbghMnTkCtVrPaOTyrpFarodVqYTKZoFar64wy/vuE52wvcOiGDEEqleLSpUtQqVTw+/1wOp148cUXj0Rq7FZBC5zL5cKZM2fQ3NzMcuiPCqrVKmZnZzE3N4dcLgeFQgGbzYauri709/cf9vA2hUgkYoXjqDP3lStXDntYewKxWAyNRoOzZ8/i7NmzeOONNw57SJ858GLeRkzJ2toastksM15oEZZIJFCpVBgaGoJer2c6ALfbDYPBAIVC0dBL3sgw2AuDhnQT/GbOMz7UNLK1tZUxNSQQL5VKeO6551iIyWw2H4linRSCSaVSyOfzLCSvVCrR1dXF+o2RwckLUhthL8N4W4FItF5ETqlUsgrjAJiBRvcbhfGERhZvWBNr2MiI2Wvw9xP9UGgeeGwAq1QqtLW11VXnJX0Tr6k8DJb4yBgycrkcX/nKV3D58mV88YtfhMViYf1+nhXQxbPZbPjWt77FFsSjFBpraWnByMgI7HY7q+apUqlw5syZZyJE8SyMcSf4rM7rqICn5hu9zqe0kl6J3ktdyUdGRtDW1oaOjg6YzWam2eCr7PJoFEraa20G0LheTHNzM+vezbNDWq0WtVoNIyMjbH2yWCysRcFhgph5p9PJmGGJRAKtVov+/n60tbWxrt9808TNxn3QzxXdS0KWjBgv/kdoaAk1fvwx90vYyxtKPCvHz4MfIxkuPFskfK72m/1qOI/aXsu4j3GMYxzjiIEPFzXaMMgrLZVKrNcbvxgXi0VMTEwgm80ik8kwtoAKyfHpy/u9iPMb5UbiTL7gHz/HUqnEKmHTBqTT6ZhY87BCrzwr4Pf7Wd0SuVwOnU4Hg8EAjUazpeM0wkFurBQmE4p7NxuH8L4UhmD2Kjwp/E4AT2hkyEghY/5ZqEt1bMgc4xjH+Mxjq8vcRpVNq9UqKydPBeYoi0xo+ByEIcP/u1HYihcC86/RpkXVZ0UiEZvDYQrHeUOzUCiwNHg6z/Sz1eMIcdCGDP97K2HFrd6f+2HI0L+F4+YN82ND5hjHOMYxnnHwQmChtkGoXziqiz7vgZPXTeM+7LASgWeahAbAUT2vxzh8HBsyxzjGMY5xjGMc45nF0TDDj3GMYxzjGMc4xjF2gGND5hjHOMYxjnGMYzyzODLp18c4xjGOcdQhzH5qpN3YrH7JYeo8eH0P36aBxtVI59NIeXDQwtntCGE3uhbbEd7uNYRC2v3KQtottis4Fr6/UbG7ja5Ho+PtBseGzDGOcYxjPAWNFm2+GaQwW4iwHSPnIEDj5n9oDnzmldAA4Me8X9VZNxrv084pj0Z/287n9xL8OeR7QjVK09/MiDiMjCvh/zczvITzfNr8Nqqfs5t5HhsyxzjGMY6xBdBCXS6XWc8bYcoyX+zsKHnbBD5LiTZXPvOqEcjY4efEb0L7Oc+tMhdbGcdhGTN8jZZarcZS3fmO3ZRuTu/hGw8fFIRGo7Do3UaZYyKRiBnFjTLO6NhUhZqvfbRX2XKHasjQ5CuVSl1vECogRGW/6f9HcWE4xtEGbTx8HxGRSFRXkv34vjoGsHGnbCpuRhtNqVSCWCyGVCplrQyEi7yweisdcy+90KfNYSPw9z1vyAg/34gNEX5uv9HoPPJ/o3Hyv/m/HTbo/PEMGG3ivGFcLBaRy+WY8UD1c/gChfsVltnomvMp+puNQXisRsaM0JCh4+7VfXSohozP58Pq6io++ugjeDwe3Lt3D9VqFUqlEkNDQ7h48SIsFgu0Wi16enqgUCgOc7jHeAbh9/vxk5/8BA8ePMDNmzdhMplgMpnwn/7Tf0JnZ2edV3SMYwBANptFIpGAx+PB6uoqxsbGEAqFsLCwgFptvXGpy+VCT08Penp6YLFYYLPZIJVKIZPJNmQ3NtPV7CX4wnf0ncQCbMUD5uvlCI0EqvJ60E4A7/EDaFgcjxxivgu4cIwHXY+GjBbeICEjJpfLYWVlBT/72c8wMzODhw8for29HRaLBb/+678Op9MJu93OPsszaHs9Dz78JayTRN/DXwN6ndZPMkr49wlDlULGZi/Hf6CrOFlluVwOyWQSc3Nz8Hg8ePjwIZaWlnD37l1Uq1WoVCpUKhXIZDLY7Xbo9XqIRCKo1WrWDVWhUBwZq/tZRKVSYc3ZMplM3SIhEq33MpHJZLBarYda8XO3yOVymJmZwfj4OO7evQuz2Qyr1YpsNrtlcdsxfjVQq62X8E+n01hdXcXS0hKWlpbw4MEDBINBzM3NoVarQalUIh6PI5/Po1QqIRaLoVAoQKVSwWAwQCKRQC6XM1aZjs1/D7C3zIZQhCz8Eb5X6Dk3YjW2Osa9ZGfoGlQqFeTzecZWFAoFlEolFuZSq9WQSqXQarVoaWl5wiFpNA/+N/+evRx7IzQyqmq19caQmUwGCwsLmJycxOjoKDKZDFKpFFKpFEql0hPG5NraWsM2Ettho5627vHXnDc4+M/RdRBWwhaJRHUNJel4GzGWO52DEAdqyNDm+emnn+J73/se5ufn4fP5EI/H2c0LAMlkEp988gnu3LkDhUIBiUQCvV4Ps9mMN954A319fXjppZcOcuifOUSjUbz11lu4f/8+3nnnHWSzWVa2XCwWw+Fw4MKFC/if//N/QiqVorW19bCHvCMkEglcv34dPp8PIpEI0WgU5XIZ+Xwe1Wp1S2XPj/GrgWKxiOXlZczPz+POnTt4+PAhc7ay2SxboGOxGPx+P+7evQuFQgGZTIa+vj5YLBZcunQJDocDw8PD0Gq10Gq17PjktVKIc68dBOqYTGwJf3w+NE/hMQKFMCiUsNkzQe/ZL1SrVSwvLyMUCuHOnTtYWVnB1NQUvF4vgsEgKpUKJBIJzpw5g4GBAfz2b/82zGYza4C7GQ7CGWvEOFD3a9JU0QZfLpeRzWbh8/kQiUSQz+exurrKwpj89eNDU3slsxAaNE/rs8X/ndZPmhfPRJZKJWZ0Njc3szA+NQJtBKEeZ7s4UEOmXC5jdXUVHo8HMzMzCAaDzJtZW1urE0FVKhXkcjmUy2W0tLSgUCggn8/j3r17SCaTaG1thd1uh91uZ3HqY2wM8gBKpRJbGG7duoW5uTmsrq4yj4enQVdWVuDz+WAymWA0Gg95BttDpVJBJBJBIBBAJBJBNpsFgDrK/ahibW0Nd+/eRSwWQ7VahVwuh9PpZE0Kj7FzNPL6aPMpFosIhULwer2YmZnBysoKwuEwcrkc20BpIyJtQyqVQi6Xw9LSElKpFORyOeLxOCQSCdra2thCTpuskCXZrTHDe+tCz1jI0vAQdivmj0daBnofr59p5EXv1aaazWaRy+WYg/vgwQOsrq5ieXkZ4XAY8XichSwCgQB0Oh3C4TBkMhnMZvMT8+Azsvh57sezT+eY1panMUSVSgXRaBThcBiJRAL5fL4hq8bPhZ+D8N4lPI05E46XINTB0H5Bji0dt1AooFgsYmFhAclkErFYjHUsN5lMsNlsTN+zUSizkbaJ1y9udG9uhgM1ZHK5HB48eIB79+7h7t277HV6GFpbWyGRSKBWq5FOpxGLxZh1l8/nmSdkNBrx0Ucf4etf/zq+9rWvQa1WHxsyW0Aul0M0GsVf/MVfYHp6Gnfu3KkTYPEeRDAYxOLiIm7fvo0TJ048cxtoqVTC3bt3MTo6Co/Hw9i+ZwGVSgV//ud/jk8++QS5XA4ulwu/+Zu/iTNnzuDq1auHPbxnHo02X6L5p6amcO/ePXzwwQdIJpPMAG5uboZer4dEIoFMJkM2m0UymUQul0Mul0MqlYJYLMbi4iLa2toQDAZx7tw5AIDT6axjC2jRBvaGJaCwcKM1kBcsE5qamhoyrLSBkUYGAGuMSdgsHLBbBINBrK6u4r333sP8/Dzee+89tnHyoPVJpVJhdnYWUqkU7e3tjPEglEol5hjvd3iczjGxYrwhIzQ0yGienJzE1NQUVlZWkEgkADwW+fKaEmD9/hT29OK/m47N/94IvGHU6P3UGDWXy7Hu6IREIoFwOIx//Md/xPT0NG7dugWtVovPf/7zeOmll/C1r32NMS/Ce4MPQwnHQ0YTsZbbZZ0OxJChCaVSKVy7dg3T09N1rwOAVCrF5z73OVgsFnR3d8Pv92N+fh4PHz6Ez+djJ79UKiEej2N2dhb37t2DTqfDpUuXYLfbARwNtXqxWEQikUA8HkckEoHP50MymUSpVEK5XEYymURLSwsUCgXcbjc6OjrQ0dGxpTb1OwEZg59++ilmZ2cxMTGBQCDAUgIp5iyXy1lstlKpIB6P4+OPP2ZeA9HnwgXjKKJYLOL27dsYHx+ve9Ap2+QwdD+0UTx8+BCxWAxWqxUqlQoOh+OJ80n3ezabZYJAuVx+bMhsE0Kvln54XUWtVkM6nUY4HMb09DS8Xi97DgDAZDJBo9HgzJkzUKlU0Ol0SCaTiEajWFpaQiQSQTgcRqVSQTqdxsrKCj755BP2bJFAVi6XA9g+M9BIX8MfhwfvjPAhLP5vpVIJkUgE0WgUq6urbH3is/toQ3U6nXA6nWhvb4dGo6nL9tsrI4aMwbm5OSwtLeHevXvw+/0oFApoamqCRqOBQqGAVCpFNBplOia/34/bt2+z9xmNRiiVShgMBtYtmw/hCZmO/RD+CkW9AJgxRetmKpVCPB7HzMwM5ubmEI1GkcvlAICtTxTu440T3pDZiG3j/9ZID0TGERm2/DpIBszy8jKSySQKhQIUCgWGhoYYw0LHymazSKVSSKfTqNVqWFpaQjgcRqFQYO8l55EMM96YpmdCOIed3lMHxsjUajWkUincuHEDwWCQvU43k0KhwEsvvYS+vj4899xzmJ6eZvR6IBBg7y+Xy4zSvX//PlpbW9Hd3Q2bzXZoRoxwoSkWiwgGg1hYWMDU1BTu3r2L5eVlpkPxeDyQSCQwmUz43Oc+hxdffBF6vX5fDZlMJoNPP/0Ut27dwuTkJDKZDIDH55/CFuRRkCFz7do1FgKUy+XQarWQyWR7LpTbK9C4isUi7t69ywSa5IXTQnFYDB4ZMrOzszh16hRsNhtsNtsThgwtSPl8ni3aJ06cOJQxP+vgqXLynPmMi7W1NaRSKUQiEczOzsLr9bLno6mpCRaLBXa7HVeuXIHBYIDFYkE8Hsfq6iru3LmDxcVFZLNZpNNpZLNZZDIZ+P1+Zgy4XC4mTt1Nl2xhiKHR5/kNi8LytGnQPUUh/unpaTx48AB3796Fx+NBsVhkzk1raysUCgXOnDmD06dPQyKRMHZmJ9T/RvMBHjPF8/PzmJqawujoKOLxOABAqVRCq9XCYrFAo9GgWq0ikUgglUqhWq3izp07KJVKaGlpQXd3NywWC9RqNcRiccOMxO2EYjYa72Z/41Px+fAhH57JZDKIRCKYmZnBwsICYrFYHQMmkUjQ2trKxs8bMULxuNAwE7KNjcJJfPYRfy1JYL2wsAC/349KpQKDwYDe3t4nwkSUJEJhVwr/kVHZ1NTEwmxChom+92mp5dvBgRgypVIJN27cwMOHD+H3+9kiAaxbZv/lv/wXnDlzBj09PVAqlVAqlcz7HxkZQTgcxgcffICVlRXcvXuXKbtnZmYQiUTQ1taGRCKBc+fOHVqKdjAYRDKZxPj4OJaWlvDuu+8ilUqxh65QKLCL2dzczDQct2/fRiQSQX9/P1wu176MzefzYWpqCjdv3sTo6CiKxSJaWlogk8kwNDSEL37xi+ju7obVasWf/dmfYWJiAh6PB6VSCX6/H8lkEjMzM5icnITL5cKbb76JtrY2mEymIxnSo4csm82y807o6upCd3c3tFrtgQt9qVbEtWvXcPPmTaRSKQwMDLAQxDH2B/Tc0cIqNAIqlQomJiYwOTmJ+fl5RCIRAIBer4der8err76Knp4evPjii1AqlZBIJCyTpr29HX6/H+Pj48wQIqZmeXkZlUoFHR0dAICzZ89CJpPVMUFb3Uh5T5rXsQH1ImJeG0KaHmBd7xaPx3Hv3j0Eg0E8fPgQwWAQPp8PmUwGpVIJOp0OLS0t8Pv9LDyi0WigVCpx+vTpPdeWVKtV5PN5rKysYG5uDteuXcPk5CRyuRykUimcTicGBgZw9epV6HQ6yOVyvPvuu1hcXMQnn3yCfD6PpaUlZDIZLC4uwmazwWw241vf+hba2tpgt9tZFg2xIfwavBMjhjcmhWJqfvOmgoOFQgHNzc2QSCTs8xSWjEajSKVSdYatyWRCe3s7DAYD1Gr1U41eYS2Wjd7Hs1G8Y8efh0gkAo/Hg48++giLi4tob29nhgjdsy0tLSxrmDKHKRM5l8shm82itbX1iZAkGXONDCj6G43/yIl9+XDQ7OwsZmdnkclkWMyTrP6rV6/ihRdeqPssLSKdnZ0olUooFAqYnp6G3+9HOBxm9FwikcDc3Bz0ej1Onjy5n9NpCAoXBQIBhEIh3L9/H3Nzc7hx4wa7WYhWlEgkqNXq0wvD4TBEIhGjFvcS9PDFYjEsLCxgZWUFwWAQItF6QTibzYb+/n688MIL6Onpgdlsxg9+8AOsrKygqakJ5XIZmUwGmUwGq6urSKVSWFxcxMsvvwy9Xn9kdTNkyFB6LA9aKGQy2YEbYYVCgdUnmZ+fx/DwMKNmt4JqtcqM0KNoQD4L4Bd8YmkKhQJ8Ph9WVlYQi8XYs6hSqWAymdDb24uBgQG43e66DYmum8FgQEtLC4LBIMs28Xg8jDXw+Xyw2WwolUpPhGa2umgLN6BGGxdvsNE9Ui6XmUMSCARw584dBAIBjI+PM/ZIKpVCIpEwxoOqzOZyOaTTaaRSqTqNWaP7dScMLd3P8XgcPp8PHo8HXq8Xtdp6rZ62tjb09fXhwoULzICcn59HPp9nCSCpVIodIxwOw2g04uWXX4ZOp2PF5/iy+TuBkP0QhkGEmy//d9J+0PtqtRoKhQJj8PL5PPtbU1MTS+GnbF2eZeFZHh5Chmwrgl9eFE7IZDJMG7m4uAidTodisVjHxpDx09rayur10J5GCTkqlYq9jx8vGX7CzDfhe3dizOw7IxMKhRAIBPD3f//3mJ+fR7lcZn+7cOECTp8+DavVuukxxGIxXn/9dVy9ehXnzp3D9evX8Sd/8ifsppqYmMDa2hpeffXVunTHg8Ddu3cxNjaGH//4x/B4PAgGg2hpaUFHRwcGBgZw9uxZmM1mqNVqGI1GRKNR/NEf/RF8Ph+WlpZgs9kwPDy8L2GltbU1FItFPHjwAP/wD//AtEYA0Nvbi7/+67+G0WiEVqtl1nNnZycSiQRmZmZYFhN9JhKJoFgsPmEcHCWsra3h3r17ePToEZaWlhCLxer+fvLkSbzyyiv7FsbbDPfv38eHH34Ir9cLqVSKN954AwMDA1suyuf1evHBBx9gaGgIDodjn0f72QEtxPxiTAvlzMwMPB4PfvKTn2BpaQnRaBQAIJfL0dPTg1OnTuHSpUvo6upCa2vrE6yIy+WC3W5Hd3c3kskkurq6cO/ePSwvL7OQyfj4OGq1GoaHh9lmRePYKni9xUb6Lqpbw2sS7ty5g/Hxcfz0pz+Fx+OBx+NBS0sLLBYLTp48ieHhYXR0dECn0yGVSiEUCuHv/u7vWPaWXq+H2+2GWq1+otAcGU471ZuVSiVEo1HMzMzg1q1b8Hq9SKfTcDqd6Onpwb//9/8eLpcL3d3dLONSq9UyPR8ZoeRMUtiMxkTj4sdG52g7RiShkb5ECD6Ewjux/N89Hg+mpqawsLCAaDTKQnlSqRRdXV04efIkW5OJXeOPIRR37+Q+asRI+f1+3Lt3D7OzswgGg3j55ZdhNBo31GLx2pp0Os1qLclkMqjVajbffD5fx0rRHGgc9Js31LeLfTNkSMG9uLiI+fl5+P1+FgtsaWlBa2srHA4H+vr6mAhuI5CGQyqVorOzEx6PB1qtFrlcjnm5wWCQWbhSqXTftBt8XJc2/AcPHmB+fh6hUAgymQwGgwEnT55Ef38/hoaGoFQq2U1J54XSx91uN/r6+qBUKvd8rKQ8J51RoVCASCSCRqOByWRCd3d3XSiuUqnAaDTCZrNBLpczPRKBKEWZTLav53inoEWEKPNCocDGT4saeTwHyWhQTDqRSMDr9bJUy1wuxzwyIWQyGZRKZd1imc1m4ff70dXVdVBDf+bBCwr5c8kzFYuLi/D7/YhEIqhUKpBKpVCr1bBYLGhra2O6MAobVCoVpj0hGp3+7XQ6EQgEYDAYsLa2hkwmg3g8jlAoxFhC4XXdzjyERoNQM0MbbS6XQzweZzoY0jBIpVLodDoMDg6ir68Pw8PDMJvNkMvl7F4kppzqs1gsFlbgT8hK0LndyVpAoaVkMolQKMSeC71eD6vVCrfbDZPJBLlczpwnlUoFjUYDtVqNSqXCDBcKF7W2tj6xPgnZIp4x2UgcK4SQlRG+l3+dF+QKrxVpDyORCHK5HItOUCaZWq1mYmUK2/AhIOH5364Rw7ND9G++lEA4HEYmk2F7BR+Oo8+QvoWvOFwsFpFOpxGJRFg5FZ71rNVqGxoywjkcKUaGVM3f/va3ce3aNaa5AAC1Wg2Hw4FLly7hi1/8IgwGw5aOKRaL0d7ejoGBASYInpubg8/nQ7lcxszMDGq1Gnp6evZ1o1pbW8PMzAx++tOf4he/+AVu3boFANBoNPjWt76FgYEBfPnLX2aF5Obm5rCysoLvfOc7mJubY6Wof+u3fgvPP/88Ll26tC9MUj6fh9frZT9ra2uQSCQ4d+4cTp069cQ5ampqwvDwMBQKBa5duwa/3w+fz8f+3tfXh97eXvT09ByquHoj0EIxOTmJ+/fv1zFHtMgplcoDT9evVqvIZrNYXV3FwsICE33/zd/8DU6fPo2hoaEnsh26urqQSCSwtLTE5hGJRDA2Nobh4eEDG/tnFVRj6P3338f4+DhmZmZYmI82+gsXLjB9Rq1WY6EiCn3wGgCxWAyFQoGuri7kcjlcvHgRo6OjTHdQqVSwsrICiUSyo5Bso8Wdskz4QmvVahWpVAoTExN499138c477+Du3bvQaDTQ6/X46le/it7eXrz66qtQKBRQKpVM3Pn+++8zVkSv1+PixYs4f/48zp07B5vNBplMhkql8kT6+E7DNuVyGfF4HCsrK5iZmUEmk0FLSwtOnDiB4eFhFgIG1rUZUqkUHR0dEIlEmJqags/nw/T0NDNoHA4HywClTEByHIVjFGbtbGUtExoR/DHJSRVuzLzBVyqVWHbW1NQU0uk0e7ap7IjD4WAhTF5jQ+eBjsmzPtsBGSb82IvFIsLhMBYXF/Ho0SPEYjHGvNMPZV6RwU73Dp3jdDoNn8+H8fFx9Pb2oq2tDS0tLSzzkpxg3pkH6rOXdoN9M2RisRgWFxcRCAQQjUaZVQYARqMRFy5cgNvtZiWmtwKRaL3WjNFoxMmTJ5k+hkSUvIW7X0ilUhgdHcXY2Bhu3bqFaDQKuVyOwcFBOBwOnDt3Di6Xi7VZyGQyGB0dxcOHDzEzM4NkMsli7pcuXUJnZydUKtW+bKzknfGdV4F12px/MHio1Wom+hM+kIVCAZlM5okH4aiAKk0GAgGsrKzUxfVlMhm0Wi10Oh00Gs2B9liqVqsoFAqIx+OMGaO4MpUh5yESiWC32xEOh5+oR8Ffx2NsHTwNXiqVEAwGMTs7i6WlJaysrLAwqkKhgNVqxeDgINrb22EymZh3TKElYpX5woq0GcrlchgMBnR3d8Pj8aCpqYm1ZEkkEuz52cnYCcLnmtYO0oyMj49jbGyMZX1KpVKcOHECbW1tOHfuHCuuWCwWsbq6yjIZHz16hGw2C4vFAofDgbNnz6K7uxsmk4npNfix8EbMTjYjYgJKpRKKxSILCfHhH/78EmvR2trKjDgyrPj30jGfVpSOf60RgyK8BsIfft60QQsLCNJrwLrBkMlkkEgkkEgk6kTDlE0pk8kgl8u3lBm2kcG0UShIyOLwzwQ5V3zmWjQaRSQSQTqdBrCeQSYWiyGXy1nmntFoRFNTE9LpNAv1CZ8Lynij/9M1bnQ9dmrU7Ntq7vV68cknn2B5eZml0hHcbje+8Y1vYGBgAHq9fsvHpMXE4XDglVdewcrKCq5fv85SVIm63c+FPhwO46//+q8xNTWF27dvs4f+13/91zE0NIRLly6xgkDUs+VHP/oRfvKTnyCfz0Ov1+PLX/4yzp49izfeeOPAWA3+xpLL5ZDL5Q0pPYPBgHw+zyx3foGgjfioamRIQD03N8dqFRFUKhXTM5jN5gMfVzqdRiAQwOzsLIDNy8A3NTWhp6fnuIXCPqBSqbA+b59++inr81YsFpmT1NPTgxdeeAEnTpyo0yLx4V9i/+j5IE9cpVLBbrfj7NmzmJ6eRktLCzKZDEQiEYLBIIxG467XJ/puYZPEdDoNr9eLt956C2NjY/joo4+g0+lgsVjw6quvYmhoCJ/73OeYExMOh/Ho0SN873vfw4cffsjWpytXrmBoaAivvfYa1Gp13bz55oDbYTM2mgcZMbQB0iYnEolQLpdZ6I4Xmba0tCCfzyObzaJYLLLxkDFBFYJpnpvVvRGGmTYDH1oRvpcE0kLHgwxkYJ0hj8fjCAaDCIVCdWEjiUQClUrFfp52bvdy36DaZnQ+6Tx6PB4oFApEo1E0NTVBqVSyKENPTw+amprQ3t4OsViMTCZT1x+Lv0f4UCo/r52GJBthzw2ZVCrFDIy3334bwWCQTUqr1eLSpUu4cuUKBgYGoNPpdiRUIgqXlOkbWaZ7AVqoSqUS81xGR0eRSCSgUCgY9Xr+/Pm6Cp61Wg3Ly8u4fv06C+u8/PLL6Orqwpe+9CU4HI5dLQLbBd04a2trrBgWf66mpqZYgzyv14vFxUUkk8m6z5Kga3JyEmKxGF1dXUeqe3Q0GsXKysoTKdfA46JUh1EIr6WlBWq1Gm1tbThx4gQ8Hg9yuRwCgQAkEgm+973voaOjA4ODg2yxMJvNyOVyaGtrQ1NTE8LhMAst+f1+pFIpKBSK4+ylbSKTyWB+fh6Tk5N4+PAhK7BGDQhHRkYwMjKCEydOMCeLLzsvvHeEnYmBdfbP6XTCbDZDo9Gw9OZkMskygDYT7W4VdD9TdtLo6CimpqZw7do1ZDIZWK1WnDt3DoODg3juuefQ1taG1tZWxhCGw2EsLy+jUChAJpNhZGQEbrcbr7/+OqxWKzQaTZ3AtxETu5vxU/sG0oWQ3iWZTCKZTCKfz7PQBLUooNTghYUFJBKJuuc8HA4DABYXFxkDy5fLF4p+t5olw2+6QkaGPx5dDwCs1xD/jAaDQRYSI5EvsH5/tba2QqvVQqVS1WVU8oXoCNthw4W6oEag4qxGoxFOpxPBYJAV4mxpacGdO3fgcDjQ1dUFvV4PlUoFuVzOxkohVsrCDQaDCAaDsFqtrO6Q8Bxu1rpgJ9jzXSibzWJhYQEPHz7Ep59+Wvc3lUqFy5cv4/Tp03C5XDseuEwmg8PhqPMUSFTEVw/cK5Ahc/fuXYyPj2N2dpbVWBgaGsLnP/959Pf3M50LGT+hUIjFyQHg/PnzOHnyJC5durRhaGcvQTcM/dBiS9RmuVxmfWPm5+dx8+ZN/OIXv8DKygpCoRCjrelz2WwWa2trWFhYgFqthtvt3pFyfq9BD2ksFoPH40GhUHjiPUTfHkZIrKWlBUqlElarFd3d3YhGo8hkMsyY/OUvf4nTp0/DZDJBJFov2keiZJPJhGw2i0gkgkQiwTIdqNbGsSGzNdA9QnVLlpaWMDs7i2QyiUqlAoVCAY1Gg56eHnR3d6Ojo4NtdnzZed6DBx5nEfEhbdLBaLVaKJVKZLNZFmYmz5Xf9HYKPiOmXC4z4+zhw4fs+Tx58iRjWKjUfLlcRqFQQDKZxOrqKjPkhoeHmf6QT8DY6oa/XTQ1NTHthFarRTQaZeHrVCrFGIJKpYL5+Xl4vV7cvHkTy8vLbNw8c0zX0ufzwWAwsIxAobPFMwLC15+Gjc4BOUp03HK5zNhvYP0cRiIRLC4uIhwOI5lM1jHeYrEYGo0Gcrm8TqgsrIBL37UTCOdM/yYNjlarhdVqhVQqZftXU1MTJiYmkMlk2PpJlYcpbZ/OL4XPY7EYotEojEYjM4T5725UnZgf406w54bMysoK/vZv/xbj4+PstaamJhgMBnR0dODzn/88ayewW/DWcbFYxNtvvw2fz4dz587tKVOQz+cRiUTwox/9CHNzc2hqasLFixfxO7/zOxgaGkJnZ2fDrCOz2YwzZ84glUpBq9Wit7cXLperrsrjfkKhUKCnpwdDQ0M4ffo05ubmkEwmMTs7i3w+j7/4i79g7R58Ph/C4TDrb9Xc3AyDwYChoSHWRI8W9Tt37qBQKODcuXNQqVSHHv6gbtY///nP8U//9E/wer1PvMftdjMm7KBBnieJdD0eD1ZXV5FOp1EoFJBOp3H37l384Ac/wNmzZ9Hb28sEdKFQCNlsFrVajdG26XS6jlI/xtOxtraGXC7H2hBQFg/VfbHZbOju7sYLL7yAjo4OiMViltYLPA6pkACTRI+8zgAA029Q5gcZQSKRiB1vtzonYaVY8oJ/8YtfYH5+Hs3Nzejr68M3v/lNXLhwgTF9BGIIqWOxxWJBS0sLnnvuOfT09LDCZRS6ooyTvTaaJRIJzGYz3G43BgYGWGbX1NQUY5QymQx8Ph8WFhbg8/nY+qTT6aBQKNDW1oZwOAy/3498Po9cLoeJiQnUajUMDg6yDKe9wGZrtjAU1KiS7+joKD7++GPWDJbui5aWFthsNpw+fRoGg+GJTX+/QTXFBgYGUC6XmTg9Go3C4/Hg7/7u76DX6+FwODAyMoLOzk5kMhkkk0mWLVyr1ZDJZFitslgsxu59kiLQXIVzEmaC7WRv3BdGZnFxkTXBAtYvBtVRsdvtdU2odgKKxfHxx7W1Nfj9flit1j1f4EmkRd1wDQYD2tvbcf78eVgslicyjij2y5dolkql7OegQkrUb4QWC7/fzyoNB4NB3L9/H6urqxgfH2ep7OTBGAwGJnpcW1uD1+tli7Df74dGo0E6nWbfcZigglhLS0t49OhRXXFBWvQNBsOGBud+g+LgRqORlVGn80cidarBQMwXzYvPbCBdBC3YwsJSx9gYtKBSWjJfmJMYML1ez9YnPhTbKOOFz04RrjcknqT0fzoGL9bezRpF9xN9N6W9UshCo9HAbrdjYGAATqcTOp2OZYrwVYWpdUmtVkNrayvb9GnufJLAfhjNFNLQ6XQwm82QyWQQiURIJBJoaWnB1NQUEokElpeX4fF4EAqFWGNhk8kEg8GAnp4eSCQS5HI5xjSFQiHodDokEgmIxeK6miaNRK+7QSO2ijRT9G96vgOBAOshJWRDdDodHA4HY3CEdVb47xN+l/BvjdCIjaH3Njc3Qy6Xs/vfbDaz/nqFQgFerxexWIzV5OL1MOSMAWBFFPP5PEvBFo5xo/O3VdH1RthzQ6ZcLrMTQBCLxTh79ixOnjwJk8nExLA7hd/vx4cffoipqandDndL8Hg8LDvKZDLhd37nd3D+/Hl0dXU19FIymQxmZmbwox/9CH/913+NdDoNkUgEr9cLnU6H/v7+Axk34dSpU5DJZFhZWYHf74dIJEIymcTbb7/NMgdIa2S1WmGxWPDmm2+is7MTly9fxs9//nNIJBKMjY0hEAhgdHQU4XAYH374Ifr6+nDx4sUDnY8QS0tL+PnPf84oUP7BUSqVrMjUuXPnDrxgIg+r1QqDwYA333wTQ0ND+Pa3v80KsBFGR0cxPj7OHmiKtfM09NLSEh4+fMgW/2M8HWtra0in04jFYlhdXWVtUiitt729HZ2dnXC5XMzYJaEpn5nT1NRUl1VCjRb5cEAikcD9+/dZGLBSqTDdw16zGrVaDTMzMxgfH0c8HodSqcTXv/51XLhwAS+++CLTL5CHbDabWT+da9eu4e///u9Ziu/i4iIUCgU6OztZuIzv1bTXkEgksFqt6OvrQ6VSwdjYGJLJJOLxOFKpFPx+f52wWSQSYXh4GE6nE6+88grMZjM6OzsxPj6OTz75BO+//z5mZ2cxNjaGeDyOoaEh9PX1MeeWDEngSQNhu6BjbeSUUip8JpPB3Nwcfv7zn+POnTusMjEAJk/o7+/HuXPncOXKFZhMJgBgzgs931sxBraCRiE1et1ms0GhUGB1dZX1fwuHw/D5fMzRjUQi+Pjjj9mcKcsJeFypmXe0RCJR3RrVSCRN12Y34vE9N2R4r4OHRCJh9ORO6TKKPVIhOn4TaG5uhtPpZP019gq1Wo114i6VSlCpVKxJJVGwpPqmmy8ajWJiYgKLi4uIRqNobm5mvSmoP8VBQqPRwOVyob+/n2VSEWVOYjhqWkk1YgYGBuBwOFhlz9OnTyMUCrEaA1TO+qAzgHhQF9/FxUXW68sz8toAAFpVSURBVEbIUrS2tsJms7HOuIfJHpGmweVyoVwu4+LFiwgGg4hEIiiXy+y8UpbLRvcJlXQ/qtljRxHEMPBF1IDH3YYpO4fWJmIj1tbWGhozQo+YX9jpGUun03WpqMIGjtsZeyNQpgxt/mRkORwOmEwmyGQyxlIQs0eMx8TEBJaWlhCPxyGRSFhdHL5jNN2vfDE2Hjtdx/hz1tzcDLVaDbvdjo6ODmQyGdbAslQqsdpPKpUKCoUCJ0+ehMPhQG9vL3Q6HaxWK+LxODo6OvDgwQPI5XJWrDQcDj9RNX63zNJGc+bDI3y4MRQKwefzsb2AsoKAxxohg8HA1l9an562RzbSumw2vs3mQP9vbW2FSqVCW1sb1tbWsLq6inA4DKVSiUKhwNhjEmED9QYhlb9IJpOIRCLIZrN1rRZorHyCDs/2NWL+tnqPHZ2Uky2AdAMLCwv42c9+Br/fz/4mkUjw+uuvY3BwcE/1MbVaDdevX8f777+PTCYDh8OBq1evsqJW1GOCmj8C6zqhn/zkJ/D5fCgWi2hra4PD4WAewkELNM1mM4xGI/7Vv/pXeOGFF/A3f/M3T5Tuf/nll3HhwgVcvnwZJpOpzjo+deoUOjs7EYvFEAqFEAqFUCwWMTU1Ba1Wu6dpdNtBIpHAL37xC3z88cf43ve+11DorVarcfbsWXR1dbHS8IeNkydPsi7vgUAAb7/9NiKRCFZXVzE5OYmlpaWG55Oux9zcHADgq1/96kEP/ZkGX3uEFmIKbdhsNhgMBiYgpfBwpVJh2TsEYbVTelaq1SoikQi8Xi/GxsawurpaV3mXHJm9ypzL5/OMuQgEAlAoFKxit8ViAbBu9FKnbqqztby8zLRk6XQadrsd7e3tcLvdsNvtaG1tZanOQvCb8F7BZrOxUNDAwABu3rzJqt8qlUqYzWaMjIygu7ubleywWq11TJFMJsP09DQT/IdCIczMzECv19cZGHTtt9s0UohGzMLa2lrdmDKZDG7fvo2bN2/io48+Qjwer9OL0D3R3d3NQoAEvgpuo+8jCDf+3azFRDaQrqqnpwfJZBJer5eVjpienobX60U2m2VMJLFmVAl+bm4Ora2tOHnyJNOf8dl9fLh2r0KW+2bI8AMUi8W4cOECTpw4satNvFQqYXFxEcvLywgGgyyMQBeOL0a0VyBqjHL7w+EwvvOd77BS/XTxFhcXGV2dTCaxsrKCarUKq9WKF198EUNDQ7DZbIdS2p8WW6fTCZlMhl/7tV97okklCZHVavUTrAVpCGjOTU1NKJVKWFhY2LeO3VsBNevkhWVC8BvNYWZW8aCURNLvvPDCC6wSdn9/PzNkarUaisUiYrEYHj58iHK5jEqlgkQigUAggGAwCJ1OB51OdyQLFB418B2QeZZEKpXWleKnlgOUQcbrHQA8Ua69VntcLHJ6ehrT09NYWFhg9bOUSiW7TlT8civ3In8/C1sR0L95toR0P2+99RZMJhMcDgeSySSrsUWF4uLxODweD0QiEXp7e3HlyhX09/ejvb0der3+CUOLD2tS7ZPd3m+8QUR1VHp6eqDT6WA0GpHJZBAOhyGXy6HT6dDW1gaLxcLaKfCiUblcDpPJBLvdDofDgUAgwNgQYgaoKBufQSMs3cFjI9aikSiVD1vRv8vlMrLZLLxeL1ZXV1lGFRVS5K8fdUTfTP8iHNNe6ZZ4Bpu+m8Kg7e3tKBQKMJvNiEajaGtrY1pLqvtDMgWqjJ1Op9n6FIvFGGtG9w4/bqHWi77/SISWgCcvQGtrK15++WX09fXt6njEAszMzMDv99d54PslSAPWvXq9Xo/m5mb4fD78r//1v+oeCD72ysNqtaK9vR1f+cpX8PnPfx5qtfrQ6q6IRCK43W643W6cPXt2W58lypk8yubmZhSLRTx69AidnZ0AducJbBf8/eD1elka87OQxSMSiVh1UhLYud1u9vfJyUksLy8DAOvPNDU1hfn5eZbGS/P1er3QarXQaDTHhsxTQBkTQkeKQktOpxMOh4M5LACeYCToOSePnjciEokEy0wZHx/H5OQk00JoNBqYzWYYDAZW24T3SJ/23PCbJF/ugMLC9BrVX5mfn0dLSwskEgnLlKLv49ep3t5enDp1Cl/60pdw9uxZ2Gw2Nmfhs0SMAxWt2+06xhtEdC4HBgYAAFeuXGFhY1p3aA0Snhdg3ZAxm81wuVzw+/0YGxtDJpOB3+9HMBhEKpViDhpvfAqPtdVNdCNDhg+RlEolpFIpLC4uwufzIZlM1iVG8M0gyXgmEXmjOW6VkdnKuPnPCkOfTU1NrFcVn+2Vz+eRSCRYwguFaakIY6lUgs/nQzqdRjQaRUtLC+tGTnMVMubkzG3kgG4H+7KrbmTN7hTlchnf+973MDc3h1u3brGQEh13ZGQEHR0dOHHiBFwu154u6iKRCBcvXoTBYGDFjCjdjP7OGzSE5uZmnDt3Dm+++SZOnTrFKOVnGS+99BIMBgP+9E//FB6Ph8WzP/jgA3R2dtZtyPuJtbU1BAIBzM/PY3R0tC7E+KzDZrPVZVlQqunDhw8xNzeHyclJVoo+nU6z1OxjbA6RaL1UOl/Xgl5vbm6GSqWCUqlkCy6JeKk/Gb/g8vVCSDdAVczfffdd+P1+5HI5plnp7e1FZ2cn2tvbYTabtxXW4FkgGi99ljb5np4eJvqlYmTA+kZB6w6xeYVCARKJBBaLBWfPnsVrr72G/v5+GI1GxgrwNXF4Xc9u2hFsNDeeneCPLxaLGdPIG6BCx5E+J5fL4Xa7kclk8NFHH7G1aX5+HtPT0yxjkW990OicbgWNigKScSoSiVjRUeoFFQgEADw2mvm0ZBIx07kQ1lih4zfCRszNTsAb6ATSVInFYlawj5od03iTySQ0Gg3LFl1eXkalUmH1fzYyXvZ6L9w3emC3Nzt/s+XzeVy/fh0PHz7E1NQUe9DoBu/o6MDQ0BAcDgfLw99LdHZ2QqFQwOl0MpGsUGyVy+Xq4q9yuRzd3d14+eWXoVQqn4h5Povo7++HRqPBX/7lXzI6PRaLYWpqihXgOgjUausN/EiPQGngPP39rG7uWq32icyqcrmMrq4uxONxRlsXi0UmiHxW53qQ4MNE/PpAGyGVRgAe9wCihZiqtlJmBc+m8F2mZ2dnMTk5iVQqhVKpxBhMagRoMpmgVqu3vYhvxDbS+mez2VAul9HR0YHW1lZmhAFgIZVkMsnuGYlEApvNxrL5KFuF5s6HaYnJ4sP3e7m+8l2SeYOCjJNG54I2XRofGTsmkwltbW0sq4w0fX6/n2kaea3MToSljVgNGi//GvVUoto4FD6TSqVMEkH9shoJXxthI+Nmu2Lfpx2bQPcQPTcymawuA2ltbQ0ajQalUgkmkwkej4d9jncEaFw8A8djL+6nIyv2pTSuqakpeDwels7I18/o6elBf38/fud3foe1o28kUNstdDodlEol/viP/5hZmnThycv5kz/5E4yNjWFlZQVutxu/+7u/i4sXL36maH+DwcAoa2D9BqQO2zsNG+4ETU1NMJvN0Ov1KBQKsNvtOH36NGKxGAvFpFKpAxvPQaARff2shNOOAviQHq+vIFErsTU8pFIpMxaEG0cul0M6nca9e/cwNzeHH//4x6wYWK223h37xIkTrL9RR0cHXC5Xwx5nWx0/gCcaEwLr66DL5UJHRwfy+XzdxhmNRhGLxfD//t//w+LiIpaWluB0Olm/N2Jzcrkcy86Sy+UsjEQhpD33oLnQFG3oVHuExsELRGkTJKOl0eZtt9vR0tKCzs5OFAoFLCwsIJ1OY3JyEhaLBX19fXXhJf7Yu4EwPFirrRevrFarMJvN0Gq16O7uZplhn376Kcu4JaeXquQKw1ZP+67N3rsV0HVolE0sDB/SPUHXiNf4WCwWlkxBhloymazTse5XGj9wBA2ZcrnMrNloNIrJyUmWukZK6ZaWFshkMrhcLgwPD6OzsxMOh4OJKPcS5MmJxeKGmzVlBVC5eIlEAp1Oh+HhYfZgfRZAG4FMJoNGo6nrIROLxZge4KAglUqhUqngcDjgdDoxMjKC5eVl+P1+LCwssPc1NzdDqVTui4F7UGhqamKZJATSZRBLc4ynY7NYPG2m9BqxMPzzSyxNPp9HMplENBrF/Px8XWVsKvev1+vhdDrR09ODtrY22Gw2yGSyul5sW4Uw9CHU5FETWApbUPNCiUQCj8eDlZUV5nxIJBJoNBq0t7fDYrFAJpOhWCwyphmoLyG/HyJ54TF5VmYjg10YbuFBf6e1iXoWiUQilp5OBfOE2iah4bDdudK14JkqfoN3Op2o1WrMSKlWq3WdxEnnxBvXm81zL8HLIhoxbUKWqZHzROMnHRPwuMN8JpNhezZ/DfnyLPyatiuDbMef3CeEw2H84he/wPj4OG7fvo3l5eUn8u+NRiPOnz+Pb37zm3jjjTfq+j0cNO7du4dbt27hwYMH8Pl8cDgc6Ovrw/PPP8/o2s8SmpubcfXqVajVavz85z9HMpnEw4cPceHChQMbA+kdenp68N//+3+HxWLB4OAg3n33Xdy8eRNjY2MsFV6j0eDixYtoa2s7sPHtNahg2+zsbB01/vbbb2N5eRkvvfTSZyJ0uZ+gEDW1DeAb9hWLRYRCISiVSsY68usJLcw+nw+RSASPHj3CysoKq1/k8/kQj8dZfx2bzYaXX34Zzz33HC5evAi73c70GdsFr4eheZAGgRe7knMnZCpSqRS8Xi8ePXqEubk5tLW1ob29HWfPnq0Lt9AGz+tU9tOD5kHfTXMUGm2NDCqqBUQd4snJWltbg9PpRDQaxfj4OMrlMuLxOKtozjNswu/ZyVwp1MvfM01NTXA4HNBqtTCZTKw+y/T0NObn53Hjxg1EIhGWDcsbAQShuJsP0ewHGh230f0qbI4pl8uhVqthNpsZI0Oh2cXFRdZrjGrmlEolpg2q1WpQq9W7ToUHDij9ulwu4+7du4jH4zAYDE9YeqlUinVjDYfDuHv3LpaWllhp5Gw2y5gR6ony/PPPo6ur61BKzvOgNEEqO01t2Cml7rMGnqIHHj/IB10un9T1HR0drNkaGbT8Q0F9b3gB47MGuVyOrq4uPHjwoC6enslk6oTnx9gYJGIlXRGxL1SLZWFhAZVKpW5Dp5oztD6trKwgGo3i0aNHrO4P9ZoRi8Use6arqwuDg4NwuVwwGAx1zT2Fz8lmC3gjPQavM+A3eeBxvyee3YhGo6zux9raGkwmE4xGI1QqVV2IuNE4hDpA4dj2Uo+xEevEv86zNHx4iP9NBh21O6BNtVQqsSq0G53Xncxho1APlamw2+0Qi8WQyWRYWFioq9TNZ+00ChMfhBHJfw9/znlsZEzSv6l3l1arhcFgQCaTQbVaRSwWQzgcRjabZeHb/WD4gANKv85ms/izP/szVkxOSJFT6fsPP/yQhSiEan1gvXjVlStX8Nxzz+Hf/tt/eyS0J5SSRmW+qQeIMBTwWcRBPWiNQBkZg4ODm44ll8thdnYWKpUKvb29BznEPYNer8crr7yCiYkJ9lqttt6T6ThraWtYW1tDKpVCKpViYQZg/fktFot45513YLFYMDMzwwz1VCrFsl+oU3QymcTS0tIToSiqpvviiy+ip6cHL7/8MoxGI/R6fd0Y+A1rJ1lAxJS0tLTUFRcjXQZ58cRWUKZnKpVCa2sr+vv70dvbC61WywyZRusoHZs2HmG9kd2ubU/LGKrV1hvUknFC46FzJ0xjJsOGal5RaGltbQ3FYpFpmvjrsVvwtXD4cygSiaBUKqFUKhnrBTyu9Ev6GWIiiKHgjYhGxtF+r7e8gUXfxafcb3TNSTze0dGB/v5+VnV/aWkJYrGYpdET+9La2loXYtopG8bjQNKvq9UqfD4fa80uZGRCoRAroS1clK1WK3Q6HU6dOgWr1YqLFy+ivb19z9Xz2wU1zFpaWsL09DTLUrh48SKGh4cP5MY7DFDqs7COz0Fio/O6vLyMO3fu1Al9qesxrwF41kDzNRgMGBgYQCAQYCxBKpWCx+NhmQOfxXtuL8AbEPyzSWL92dlZ+P1+LC8vQywWQyKRMPYmHA4zES2J/YF1mp0yJU+cOAGLxYJLly6xlhh8nxxiLun7d+KENdIw8HOhDaK5uRm5XA7JZBLhcBirq6uscW97eztsNludF04/wo1sszHuNlOG3yj5cfCvbaTbEI6dWBcqSkil9YVNCzca526eGTLq+PmQEUbsdalUQjabRSQSYR3XyfCk9H1iizZq4SM0cDbTez0NGzE/ZBALWZeNzjmwzsZQU16LxQKn08lCsJlMhpWJoExMmptEImH36l6sWfuafs1fXKr1MT09/dTP8f+22+1wu934jd/4DbjdbnR2dh4JPQB1WV1aWsLs7Cyampqg1Wpx4cIFdHV1PXMbylbT/qrVap0hc5QyZ7xeL+7evYt0Os1eW1tbY12In3Xo9XoMDg6yTYrCIh6Ph7GBx9gYjQwZYlao7QOAusw8yugRGu2k6XC73ejq6sLly5dht9tZzSi+mBixCbwBRGPYyjqx2fPFf57v00M1PsiQoZ5G7e3tsFqtLPzEH58vUscLQYXfLzQKtwshy8P3teKZGAoPAY/PIV/Dh95L55bE76urq4hEIg2Nrd2uy42uBS+OpnFSq4vW1lYUi0WWvEJ91Wq19fIVZMxsVkZBaEQIx9Mo3LPTOTUyooTEA89G0nPAGzJjY2MAwKqV8x2yeUZxL7Hnhgy1WKeW3nxNg83Aay9UKhUzYF599VUMDQ2x+gAUezxseDwe/OQnP8Hc3BwqlQpOnTrF0sEtFsszZ8hMT0+zirJCLC8vMwO0XC5jdHQUoVBo37Iadgq5XA6DwYBsNst6mojFYhiNxs+E8LqjowNf+tKXEI1GEQ6HUSwWkUwm4fF4Dl0rdtTR3NzMeioNDg7WMXW08VD9C1qz+JBFc3MzS5GlJqzd3d04f/48Ojo64Ha7oVQqodFo6vQwVAFVqIvYqSaDD8nQxkkOBWkQarUaVlZW8LOf/QyPHj1CLBbDmTNn0NXVhfPnz8NsNtfpFTZKZ6Z/N3rG9+KZb+Tt87+bm5tRLpeRz+exvLzMWg/UajW0tLQw/RJpYPx+PyKRCG7cuIFQKIRKpcKK/5GObq/D/cLzQIwKPy/a7KmXEf2NDGaNRgOFQlEnot2sp9VehWOEx9wKY0X3MY2Vdwio6fDk5CTS6TTy+Tzy+Tzm5ubQ0tICt9tdp6/cS+y5ISOVSlmBJqoFwzdqI2sUeFzQiX4ojdBkMjHB3Pnz5zE8PLzXw9wx6CaieiXUfNFms8HtdsNgMByZ5oRbAcWPFxcX8ejRo4bvmZycxJ07dwCs38jBYBD5fJ7Fr6nE9mFDoVDAYDAgEomwuK5SqYRWq60r5PSsQqfTobe3F3q9HmKxGMViEYVCAcFgEDab7bCHd6RBHr5Go4Hdbmc0P7F11IuIWBMqbEnZFny/MapJcvLkSZw8eRJtbW3smvDg627wjfK2w8bwEHrkdGwaMx27Wq0ikUhgdnaWzdFkMrGwkkajecJwedomth/OymbfS55/Pp9neouFhQVWa4YyYKgRY6lUYmJsn8+HTCbD+mip1Wq2Ru3VPJ628fOMA2+0UJq8VCqFXC5neh6pVFpntGxkyPD3AD+GvZzXRnPi38OHhOgzcrkcFosFer0earUahUIBxWIRwWAQRqOR7RnPhCEzPDyMP/7jP8bMzAxmZ2cRjUaRyWQwNTWFUCiEO3fusAevp6cHbrcb3d3d0Gq1MJvNMJlMOHPmDLNej5pRQJ6cz+fDvXv3mJDp8uXLOH/+PKsO+qzg2rVr+N//+39jcXERfr//iUJRIpGIUaB04xaLRYhEIqhUKvT39+PNN9/EyMjIYU4DAPDcc89Bo9Hggw8+QDQahd1uR19fH1599dWGVUKfNZhMJmg0GpbaWSgUkEwm8f3vfx+JRAJf+9rXjgw7dtRAjExPTw+kUikGBwdx6dIl1gdnaWkJyWQSy8vL7N6nRbmnpwcGg4GJZNva2liGBtWG4T19Mi6ELAwt/jvRBQh1LGQcra2tscQCakUQj8exsrLCCkNKpVKcPHkSZ8+eRWtrK+uRI5FIngjfEA6baS0UClheXsbExATee+89PHjwAPPz8+x8qtVqVCoVJJNJdk6ISaPqxd3d3ejr68PAwACr47NXjAwxX8JzRNedv+ZU3n9oaIiNPZVKwWw2o729HRcvXmQiZL5qsRD7GcYXGir0W3gf8K0YALCQvVKphNvthtlsxr1797C4uAiv14t8Po+PP/4YqVQKHR0daG9v35dGw3tuyBCrQvn9iUQC2WwWMpkMoVCIeQzA44qUnZ2d0Gg0MBgMMBgM6OjoOBLho0aoVqtIp9NIpVKIx+NobW2F1WqFw+GA1Wp95jKV8vk8QqEQEokEcrkc685N3U2pNoZIJGI0PDUVo2vX3d29p9kAO4XRaER3dzei0SiSySSsVitcLlcd3f8sgwpn0cZF1yQUCiEWi7F6Fp+Fue41yCuWyWQwmUzMq9dqtUilUtBoNEilUjAYDGyzNJlM0Ol06OzshF6vR2dnJ1QqFSsk9zTjWOgt89qT7XrSwvfzYS8+a6ZSqSAcDiMWiyGVSrHQqs1mY+sTaUsoG6hRf5/tjG27EIaQGmmXiJEpFotIp9PI5XLMmWpqamIMWiqVYq8R66FSqSCXy1khQp1Ox8JKu50TP75Gx2pkAFI2ldFohNvtRrFYRDabhdFohMVigU6na8gYP22sO53LZp/byFDa6BrxhjoxLXSPkUFPhfGohtNu9FUbYd/Evi6Xi1U15D0JXi/DP4T8A36UvcpCoYD5+XksLS1hdXUVV69exZkzZ3D+/Hl0d3cfWQNsIyiVSrS3t0OlUiGTycDpdEIsFsPj8SCVSmFlZYXRoKurq8hms+jp6YHT6cQ/+2f/DD09PU+k1B8W2tvb0dbWhnPnzrGFhha4zyoorZg6HyuVys8E+7RfkEqlsFqtsFgsGBoaYusTCRj5kCnpAGgDFKal8osyn+kjEokahlp3ozPhDZfNkE6nWV+6SCTCGlY+99xzGBgYQFNTE6tlwvf6IQeTnpv9fp75btp0DYjZonNORmNvby+amppgMBgAPGbFqQEm6U+MRiPUajVsNhtUKhU6OjpgNBrhcrmg0+kOpMAfrTXCsE9zczN6e3vR3d2NF154oc4A4MfFG6ebYb9Dfo2Ozc+J9nOhfqZcLiObzSKdTtddWz7Mutf6HmAfDZmtXIxnEalUCh9//DGmpqZQq9WgUChgMpkgkUiOxGa+XdhsNrz00kvIZrMoFovQ6/VoaWlBJBJBPp9HLBZjlTOp8ZzT6YRer8fQ0BDMZvOBVQB9GviF4bMMWpwjkQjLBkgkErh//z66u7vR2dl5yCM8uuAdJWFHZdpMhQ6WELQJ8VT/VmvC7OQ52cpn1tbWEIvF4Pf7MT8/j9XVVRQKBbS0tEClUtXVtWpqWm+SSXPlz8dBCfgbMVPCsSiVSlgsFgwMDMBkMiGZTLI5UIG7RCLBPk9FMdVqNaRSKcxmM9RqNaucK2SedjPHjdgYoH7DF2qRiDFtxHAIj/M0w3c/jRghNspc47VfBL1ej7a2NqZnIm3WfmopP9sr/j4gEongO9/5DsLhMID1Evg2m+1IpITvBL29vbsuFHcUjJhfJXR0dODUqVN1zTFXV1fx9ttv40tf+tKxIbMD0IbS6DluJIw9avd8tVrF8vIyZmZmMDo6Cp/Ph3w+zwqR8cY9GTeN9A889nOOQoapkSFDIRdqBkziXRpntVplzAz9LpVKLKGEmBqlUrmn+pitQhiBEM6Xfx//ns2wmQG13xCyLxvpedrb25FKpVitmJGREbhcLuj1+n3TkB4bMttES0tLXRy9u7sbp0+fPnKi5K3iqC3Ix3g6qDjk4OAgstksgHWD2u12w+12H+7gnlE08tI3EnQK33eYEHrGSqUSfX19sFqt6OjowLlz5zA4OPhE+YGjYJBtFiqj0B0VlqP3C1kcvnZOa2srS3cHHvcFkkqldbqyg5gXQVjxdyuMx0bvOUgdU6PvJlDWkrCAHmnQent7oVAo4HK5UKvV4HA4oNfrmWG6H2M+NmS2iZaWFlZ2urm5Ge3t7ejp6Xmmuysf49mBSCRCT08P0yYdY2+wWR2Vow4yZqiERUdHB2MmBgYG0NHRUScmPWwDhsZAv4VGJBlmlFwgZGv4Y1B4jETwfGE9Cg+S9kaYcXMQc9zvujWHgY3mRWEzyuojjSwxYmq1Gq2trfsTEqvtVz7XZxTZbBbz8/OsP4bL5YLFYjn0lgnHOMYxdoat1M44quAL5VGF2GQyyYwbEn9Th+yjgK1sOXyCCBklQoOGz3oS/gD1DI6QyTlq2E546SijVquxNh58UVJKQ98v7eyxIXOMYxzjGM84+E2d12Vs1LvnqIPflrYjQD6s0Msx1sHXONooo28/cGzIHOMYxzhGAzRaGo83xmNsF0KjjPCshTCFOEpG49HgGo9xjGMc4xjH+AyDD5UBW68NdIyn49iQOcYxjvErjc2qmfIhGz5LY6MiX3xBN+DZ9LSfRRy2zmkzjQuF+6iXF5X1p0rdwqq+wvvuKOh7GumRaDz8+IRapf0OKRGODZljfGZwHAo4xlbR6F4RhgCEiza/MFOab7FYZBsT1aGhApFk+GyEvbo3t7qJ7/b5OEqhhEbgN0/g8Dd9AqWSFwoF1rdubW2NNZDkDRmhwUBZWweVOt5o/Dz4+jG1Wn0XbF54zleJPoiM3mND5hifGYyNjWFhYQEmk4k1tDxOiz/GZuA3Dv413oAh8CwLsS70mxZ0PjODX9h5z5p+9qJUe6Nx0xj5343m9lnLtOQNmaMyr1qtxsr2x+Nx1jeqVqvBaDRibW2N9akj5mZtbQ3FYpHVBuIF2/s9r6dJZoXPhjCLbG1tDaVSCdVqFRKJ5MDCZseGzDGeedBD5fF4cPfuXfT09MBsNqOnp+eQR7Y3oAWOaGlaPKRS6XF8fQ+xUTrvRim8/A/V0OBLsAtDT3xHZDrGToyZjdikjYyUzZimp21cm41NaDTtZvxP+97tsEkHYcRsdA0ajZvYmHQ6jXg8zppfKpXKJyrd0rNeKpUY43EYxstmBnEjg7FcLrMf6uUl7Aq/0XcRdjPPY0PmGJ8JrK2tYX5+HtevX4fb7WYl2D8LSKVSmJqawvvvv48f/vCHrNP6H/7hHx5X8t0lNmMuqKgc0fv8pkLhAjJUqMkkfXZtbQ2rq6sIBoOsMaNer2e9gKh/2W7YA6GBwotJN2JiaCyNQhV8OOAgRKhCw5HG0+h7qcovVfqVy+Wbsk0H/exvZAxQ92e/34/l5WUEg0EolUqoVCoYjcYnKsJTcT+1Wg3gYAXBjdgWIchgJ4hEIoRCIXz44YeYnJzE6OgorFYrDAYD3njjDTgcDlgsFvYZMu4bdYLfDQ7VkOGpVyGofwad2HK5jObmZuj1epTLZSQSCRSLRRSLRXahNRoNJBIJtFrtZ2YTO8bTQYt4NptFLBZDS0sLFArFZ4atyOVymJ+fx6NHj3D37l2k02kUCgUUi8XDHtozi6etD/zfaX2iTbRWqyGfzyOXyyGdTqNUKrG+Rq2trSiXy8jn8/B4PPB6vSz0VKvVmBdOVWl3qlHZTK8i9J7pt5BdetoxN3ptp2j0XY3Eoxt9VhgG5DUkfLiOf/9mG+VO9ojNzpfw++jvlUqFdYROpVKQy+WscSeFvoV1VzZi1DYbz3bntZVwqvC99CzQ2lqr1ZDL5bC4uMgMmZ6eHrY30/u3e723i0M3ZKgiJV/ECVjXO9y8eZN5RT6fDxqNBr/3e7+H5eVlfP/738fExASmpqYgFouhUCjw67/+6xgcHMQ//+f//Fgb8SuEarWKYrGIXC6HXC4Hp9OJrq6uI1PJdLfw+/349re/jaWlJVQqFajValgsln3rJHuMdVD5exL0EjtTKBTg8/kwPz+PqakphMNhGAwGKBQKtLW1IZFIYHp6GsvLy/B6vbDZbNDr9XjxxRdhtVqZx72Tcu20MTRikhrd77RB0ud4VonEyuRl8ywN/5m9Fs8K2SPCZoJWsVgMsVhctwFS08iWlhYWxqBzsLa2hnw+j+bmZha+4b93t2sDf34IwrGT0HVtbQ3JZBKpVArpdJqFvvnQUqlUQqFQ2LBDNG+w7TU2MoaExjyFvCqVCpRKJZqbm1GpVBCNRnHr1i3Mzs5iZWUFnZ2dUCgUMBqNMBgMTC8m1IrtpUZrX1d6omeJfq1UKkilUigUCshkMowq9Pv9yOVydZ9dWlrC5OQke086nYZWq8WDBw/g8XgwNTUFn8+HZDIJtVoNuVwOmUzGvJ3DBIm7wuEwAoEAgPWHtLu7m5UMP+wxNkKtVkM2m0U+n8fS0hKy2SwSiQR0Oh3MZjOsVivUavWREwkWi0VEIhG2KLe2tu5bT4+DRK22Xu6bhIKlUgmtra3o6urC6dOnn2gEeIytgzIvyEjJZrMsRZb0SCTMDIVCKBQK7DPVahWRSAR+vx8ejweJRAIWiwVKpRLVahWJRAKLi4sIBAIIh8Os10ylUqnTr2zXcxZ6so3SXvm/0etCKp+el1gshlAoxJgBu90OuVzOGG3+exuNdaeMhtCAER6nUqmwTY+MEzr3iUQC+Xwe4XAYuVwO0WgUKpWK9fbRarWbGvhb0QVt9LntzIF/rVKpMCeLjCsShRMopCQ0JOl7SPjb6LueNo6nYTtaLT7Mtba2hlQqhUQigXg8jnK5DJVKBYfDwYwZYiL5zwtDZc9EaCmVSiGXy7GFYWJiAn6/H3Nzc4yevX79OsLh8BOf5ak2o9EIjUaD7373uwiFQrh58ybLwXc4HLDZbBgYGEBnZ+ehhxTy+TwWFhbw4Ycf4q233gIASKVS/If/8B/Q09ODtra2A28pvxXUajUEg0GsrKzgL//yL7G4uIh79+7h3Llz+MIXvoAvf/nLGBoa2jYlvt9IJBKYmppCJpOBTCaDRCL5TDBy5MklEgkkEglUq1VoNBq88sor+MY3vvFE/YljbB0knA4Gg0ilUlhYWEA2m0UqlUI+n0cymcTi4iJbq5LJJLLZLESi9RRr2lzK5TJqtRpcLhfUajXC4TDS6TTm5+eRyWSQzWZht9vrNmW6P7fTPmAz/Qs/JwB1GzkZZ1KplK05iUQCN27cwPj4OG7fvg21Wg2VSoXXXnsNLpcLp0+fZllYZEDs1XrVKLTA998hZ4quj0wmg0ajQblcRrFYxMTEBHw+H959912EQiHMzc3B5XKhr68Pb7zxBk6dOgW1Wt1wvEKjb6eGmJDlepphWiwWEY1GEYvFkEgk2L4lZNN4Q6ZarbJrR2hubn5CACwcx07Bs26NrpFQ1C4SiVCpVODz+eD1euHz+dDU1AS3243nn38eL7zwAgwGAzNC+b18P/aOfTNkKB749ttvY35+nsX0Q6EQMpkMotEou1kzmUxDnQxNuLm5GefPn4fNZoPVakU2m4XL5YJMJoNcLofZbIZWq0V/fz+MRuOhb7KBQADf/e53MTs7i6WlJQCARCLB+Pg41tbWYLPZDtyQIeFZLpfD+++/j1KpBJvNhkwmg1AohGKxiFKpBI/Hg1gshocPHzIre3l5Ge+//z7a2togl8vhdruP1Caaz+exurpax+od9j2wFyiVSix8mkgkYLVa0dnZCZvN9plgnA4DtPBnMhkkk0k8ePAAKysruHXrFtO7EAscj8eRSqWQSqVYJgYApk0idkUsFmNwcBA2mw29vb3IZrNwu93sWF1dXTCZTBgcHITBYIBard5SamojFoD3zIWvk5C30XyBdWMonU4jEAhgdHQU09PTmJ2dRWtrK3uua7UaRkZGGrIae3G/8RumMLxAjMX4+DjL8CFxdCKRQDqdxvj4OMLhMHNcYrEYRCIR1tbWMDg4CKVSiRMnTrAmmTyLIdygd6vTELILvGHLZxQWCgVm4BaLRUilUqhUKmYoEpPMn/NGgmyhdoZARtlmuqmNxi8Ef46EmioaD71eqVSwvLzMWEmbzYauri44HA6YzWYWCiyVSswI4r9X+D1bHXcj7Jshk8lksLq6ih/96Ee4du0ao243wmaCLLFYjPPnz6Ozs7MudKRSqaBSqVis0eVyHYonLlxwAoEA/uZv/gbZbBaFQgHAuiEzMTGB5uZmXL169cDHCKyHvNLpNH74wx8im83i9OnTWF1dxcTEBNLpNLLZLAvzUfofAHi9XqysrOD06dOwWCyw2WxH1pD5LGzudN5LpRIePXqEmZkZJJNJ9Pf348KFC7BYLJ8JxukwwBsyoVAIDx48wNTUFH76058ilUoBQJ04t1EYgZgPYJ39kMlkGBwcRHd3N0ZGRpDP59HZ2QlgffOx2WzQaDTM+dpuRp1wHMINiy9SxjMb/GfpfYlEAn6/H/fv38fCwgLm5+fZPAYHByGVSln2UqNxbIStzofexxsYtDlSCPX+/fsIhUJYWVlh+sdgMIhoNMqehXQ6XfecZDIZzMzMQKPRoKuri4U1hILZjTbPrWCjTZ5nrorFIqrVap2jwRsypVIJMpkMSqWSGWCFQoGF94QsSKM58Odxu0bZVq4hP0dhCIh3wMmQ8Xq9SCQSaG9vR2dnJxwOB4xGIwCwujK8fkm4Xwq/fyfYN0NGoVDAZDJBq9VCqVQin89vasi0tLSgra0NEokEUqkU0WgUgUAAzc3NkMlkOHfuHIaGhupOJNFxZHkfprizWq0ik8ngnXfewd27d5kGiFCpVHDv3j0Ui0X87u/+7qGMsVAoIJlM4tGjR/D5fJiYmECxWEQqlWKppIVCYcPr9N5778Hj8aC3txdarfZgB78JPB4PfvSjH0Gr1aK3txdyufywh7RrVKtV5p3Ozs7uKK5/jCdBizF5iolEApFIhN3zJEwkSp82LLFYDLfbzSr3JhIJhMNhqFQqaDQa9PX1oa+vDz09PahWq2hra2Nrk1QqZeGEvWJiGwlLJRIJe53YgebmZvY8ZDIZ/PKXv8T4+Dju37+PdDrNPl+r1bC6ugqDwYBkMgmRSASZTMa0jY0Knwl1OjsdOyESiWBlZQU3b96Ez+fD8vIygPV1nhjjTCaDarUKpVLJrlO5XGaC03g8jpMnT0KhUEAikTDRL7UD4FkFIbu1VdCz2OhaFgoFlMtlqNVqZtiEQiFMT09DpVKxMB4Zs01NTZDL5eyeJH0QzyYJv3sz9mIzA20j47TRvSQ0kITGW7lcRi6Xw9LSEnw+H6rVKqRSKYxGIyQSSd2xSRPKny9hqv9uHdB92/nJktZoNNDpdMhkMuxGIrU8TaSpqQkSiQRtbW1QKpVQKBSQyWQsfUsul8Nms6GtrW2/hrtrUNjm4cOHmJ6eZhkOhFqthng8jlgs1jCMdlBjJKGfz+eDz+d74iaiFFI+759CgB6Ph+majgLoIUulUpifn8fp06dhtVrZg/QsgzyZQCCASCRy2MP5zEC4cFL8nrJH+PWJ/kasi8vlYmXlA4EA21C1Wi3MZjMLcQOAUqlkzxLQOFWXxrPVcfPjp3/zx+U3Cgo1kSdMovHZ2VnMzc0hFAoxY4SOQeXzSfAsHGuj8e9kDsK589+fSqUQCASwsrICr9fLWAEypCiUp9FoUKutp8FTqQ6/34/m5mZks1mUy2WmZapUKk9UZd4pGoVcePDl+Wu1GgqFAmOaKCFFIpGwsAsZyfz9WKlU6tgLPnTU6Frs1AgQ6mCE17TRPctreEj7k0gkmPFFRhm9H8ATrNJehpQI+2bI0MX6yle+goGBAdy+fRtra2tob2/Hw4cP8fbbbwNYH7zT6YTL5cJ/+2//DWazGVKplGUFXL9+HX6/nxUIOoqo1WoIh8NYXFzEP/7jP8Lv9z9hrLS0tOC5557D8PDwkQoLkMdI6ZenT5+G2WyGw+FArVZDKpXC2NgYPv3008MeakMQpQsAvb29eOmll44UW7RTlEolJo73+XzHjMweQ61Ww+l04tKlS3A4HDh16hRjUL1eL8bGxhAOh5FKpTA4OIiOjg783u/9HoxGI7RaLWZnZ/Hw4UMUi0U0NTVhYGAANpsNTU1NzBASbpjkAPBtDLYCfvNtlKLMGzC12uOidyQordVqmJ6exuLiIm7dusWEmZTSTBoGChurVCpm2NH7Gm3YPLu10zANfw6kUikUCgWrDgsAcrkcOp0OWq0WarUaZrMZKpUK7e3tKJfL8Pv9mJmZwf3795HNZhEKhZDP5+vYcB7C0NtOxtzos3RcmUzG2JRisYjl5WXmiDidTgwPD7MieFSXiA9jUmNJYpMoXM6nngsrRO+G5eOvgzDsJyx8xxtuyWQSoVAIjx49YgYnzYVvUyASiZ6oXkzH3ksZwL4ZMnRinE4nmpubkc/nUavV0N7ejlwuB5PJhEwmg0KhAKvVCrfbjfb2dphMJrS2tjLKNplMQq/XHylNBg9iKxYXFzE9Pc0WPwJdXKlUira2NrS1tR16VhWNq6mpCQaDAR0dHZBKpZBIJBgZGYHRaITNZmMP4uLiIoDH3gKxMkLl/UGDHnTyntVqNRwOx2eCkSHqmzxOYH0DpKqwx9gcG21UdL8SY2y329Ha2gqLxcJEvs3NzVhZWUEmk0Eul4Ner4fFYoHL5WJ9vKjsPBXtVKlUdZQ6/Ram0NLrW9lIeS92I2Fmo9coy0gkWk+1LhQK8Hg8WFhYQCQSQSqVqhujRCKBRCJhrBLVz6ExCDexvTCqGx2DnCkq1yGRSGAwGNDZ2QmTyQSdTscMLbvdjlwuh7W1NQQCASY+LZVKLAzFMyfC88n/f7tjFYZdCLy8QSRar3ND7BDtaXq9nhkutNETYyFkn+g4QgZOeI23iq1obTbam4T3IN8Ik8TvYrGYiawbfZ6MX+G89gL7LioZGBhAX18fzp49C2BdO2M2mxGPx3Hr1i3Mzc3hxRdfxNmzZ6HT6RhbodFomNdE9O5RRCqVQiwWw1/+5V/i3r17dUYMQaPRwGg04vnnn8eJEyeOxFyosuTVq1fxH//jf4TFYoFer2cPh0gkgtfrxT/90z8xi5rSUhcWFqBWq9Hb23uouqRCoYD5+XkEAgEUCgXYbDacOnXqSBiK+wGLxYIzZ86wJnPHeDr4DYP3MInmP3/+PCqVCsRiMctUMplMLJMvn8/D5XKhq6sLFosFarUaIpEIdrsdSqWS1YbR6XQsLFWr1ZjYk4qIUTrt0zpib3duPKtB380bSn6/HysrK/jhD3+IyclJzM/Ps7A+hQc6Oztht9vxwgsvoK+vry7jRnjeCMLNdrvjJv1OI6+f9JQ2mw0XL17Eb/zGb7Cy9waDgWmOfD4fq3pNLEChUGCpzpTBulGa+3aZJL6OEJ0DnhkB1lkl2rCpplClUmFGmMvlglQqZWGn5uZmZgATi8ajUa0oki3shAnjQed7u4b1Rp9Vq9Vob2+HRqNhFa7pb/QckFZpr7GvuxB/E5Hgp7W1FRKJhFUwrNXWs3y8Xm8dHUgn6iiFYRphdnYWY2NjWFxcRCQSqfO6JBIJEyr39fXB5XJBo9Ec2kZL1UhLpRIkEgk6Ozvhdrtht9uhVqufeGiIaYpGowBQt/jxWU2HBaoqSaLFjRZdAj9e3is7TFZpO5BKpTAYDJ8Jxmk/8TShIn/NqSIshVnW1tag0WhYuxNgnUaPRCJIJBJobm5m+hcqgAc8psr5miL0b7oneZ3HTjxpfn7CujL8nHgxs9/vx8TEBJaWlhAIBNgaS3oGpVKJoaEhdHd3o729HUajsS6duJGx0ug8bheN9BnhcBherxfZbBZNTU3o6upCd3c3urq6oNFomH6ypaWFsQFUB4iv7UKsDLEWFOrZzXolXDtoLQQeX3uhEVmpVBCLxZBMJlkhPIVCwe4HXodEn6eMUTq3JBgnw3iz8NZWsRm7x1dzFl4fEkeLRKI6o65Wq7Ex8owMb+xsdE55Y+xIamSE4BdfeogonjY2NoZisYg333zzmWv2d+PGDXz/+9/H1NQU21DpAslkMlgsFnzjG9/AK6+8cqhps7VaDRMTE7h37x7y+TyUSiUuXLiA4eFh2Gy2hp9JJpO4desW/H4/gMdCNqKrDxu0UMfj8S1/RrgQHMXChBtBoVDAZrN9JrKy9hsbhXeE2Y38okv92vR6PYxGI/OcfT4fmpub4fV6sba2xgyZRszqRoXYeLEp/++dzo02EqG+gY5P/Z8mJyfx4YcfYmxsDKFQiI1RKpXCYrGgvb0dX/jCF3D69GmcOHECSqUSwOPQwUa1S3brjAk341qthvn5eTx48ADxeBwymQwXLlzAuXPnMDw8XPdZCilHo1FMT0+zxpx0PvL5PJMykL5E+N27GTd9T6MChDxjUyqV4PP5EAwGkUgkIBKJoFarWWiJL3ZHSCQSSCaTjOkh5pDfN/jv2CtQCjmdM+EayYuQ6b0820hGjEajYedDqEcS3kf8Wrzb++lADBnhQ+B0OvHaa6+xonmxWAxzc3OYmZlBsVg80tlJBOrtk8lkkM/n68S9VJxvcHAQ586dw7lz56DT6Q6kJXsjUDbCw4cP8emnnyKfz6OtrQ1f+tKX0NPTA+DJJnn5fB6ZTIYVcQLWGQG1Wg232w23233oIZxcLscKZOn1+oaiMkIgEMDy8jLGxsbg8/mQyWSgVqvx1a9+FSaTCXa7/cgY0LXaeoVlSmvkcVTGeNQh9CiFoAWU0l15XYher8fw8DACgQDz9uPxOObn51GtVmG1WusMGdqUarUaY6B5b7QRU7jb60jH3YjdIbYiEAjA7/ez5roqlQoKhQJOpxMDAwM4d+4czp49C7fbzfoC0VomzIJqpDfZ7pgJvNg5k8mwpIK7d+8in89Dr9djYGAADocDAJ4QwcZiMdZmgUTUBoMBVqsVZrMZOp2u4cbJn7udzIH/HH98vo4PvV4oFLCwsMAMSJlMBq1Wy/YBPh3f4/FgdnYWjx49QjAYZAw5MeZ8AgPfu4jGJBzjTrCZsUrPCs2PyhaQsJrq4Aj1Vf9/e9fW01Z2hT9jYxvj+x1sfOESICRAMmGmuXaYtqpmWk3V6UVTKVKn6kNf+tj3PvUXVOpb+1apVas+ZFSN2pdOJ7RSQqa5EAgJBowBY3w3YPAV9yFaO9uHY0LAYHt0PsniYvucvc/ZZ++11/rWt+gneW3oPTI86f2WMGSEsNlsMBgMePr0KQKBAB49eoRcLofl5WW0t7fD6XSKlvlupkm8WCxia2uLid7xDym5a2/duoUPPviAaRo0CpQBEwgEMDc3h3w+D61Wi6tXr4pm+NCuhuqD0M6BDJnu7m44nc4z7sVBEEcmlUpVhQJ40AMfjUZZttzMzAzi8TicTieGhoawv7+P7u7uBvSgNqgODvE7WikE1mgI3dli8wcfHqDUUVrg9Ho9BgYG8OLFC4TDYUQiEWxvbyMUCkGlUjGiOz9HET9FGN6h857GvTusSnI+n0cqlUI8HkcsFkOxWGQhfrPZDK/Xi4sXL+LmzZvweDwwmUxsMaYFizfU6Ge9+iJU9E0mk1hcXMSLFy9YSQW/3w+bzQbglXQEhf+odAfV7gNechGJS0M14QjCcOOb9kPMWOCPwae703nJY5xOpxknUafTMeOFwkVEWL5//z6+/PJLrK2tMWOM7tvo6GjVOOaNGT7d+bjGGQ+xa0TPCj03W1tbSKVSVQKASqXygJoyf2yhjgwZx/XYEDfEkCHX5je+8Q34fD785je/QSAQwO9//3s4nU6Mj4/DbDbDbrfj8uXL8Hg8jWjmoaD6PlSPhbwWwCsirUajqRq4jUIikWBcl93dXSiVSkboExtE5XIZ4XAY4XAYiUSC9W1kZAQjIyMsfbDRKJfL2Nraglwuh8fjEW1XPB7HvXv38MUXX+DTTz+Fy+XC2NgYYrEYFAoF7t69i2w2i/Hx8aYxFPb39zE3N4fHjx+jUCiwnRllZDXaE9YqEBIShRMzeWL4v+VyOXQ6HXp7e/HOO+9Ao9Hgn//8JzY3N1mI5vnz50zXanh4mFUi57kE/O71NOrLCL0+fKhDoVBgb28PGxsbTOeDDC0i+lqtVlYlnjyZ2WwWlUoFnZ2dB8JV9eTDEfeDjk9p05SF5PV60d/fD5/PB4PBwPpL39nf30c2m2UefTJk7HY7K+EhxvU5qRFGx6OfQg9DPp+vyuQkzzZlhBmNxio+TbFYRDqdxosXLzA1NYXPPvuMXR8q27O8vAytVotsNstqdAnJsvW+N2LXiNeI2d/fRyAQwOzsLIrFIrRaLZMosNlskMlkjNgr9PQL+VX1ejYaYsjQze/u7oZKpYLb7UYsFmPpgaVSCXa7Hd3d3ejq6oLRaGRuuNOYFI4D3iMjFIgjV7KwfkajQLFMUhc1mUywWCw10/foASTiGekdGAwGNmk3A2gyUCgUBzwyNLlns1m8ePECS0tLWF9fx7lz5+D3+wG83DHF43FkMplGdeEAqPJyJBLB2toayuUyOjo64Ha7WVaZZMi8HkIvRa2FjOYTCg3Rs6vVauF0OrGzswOTyYRMJlMlMx+NRrG9vQ2DwcCeKaGI2UlJmYf1Tawv/I6cZBKoureQzKlWq5lUPn2XLz4o9GbUY87l7wl/fDL8KCWe0sCp9Ax9l5/7C4UCy1IiA444GhqNpkqI8KSeJKEhxxs0vLHE0wv4shFUL4ovW0BzFxUYXVtbQzKZhNPphE6nY6KEVH+K568IPYv8eY/Sx1pjUsxg5Z8fhULB1pJYLMa4ScQFNRqNUKvVVXXJxAj3dLx6epoblzuLl67Azs5O/PznP8f8/Dx++9vfYn19HaFQiFngoVAIExMTuHXrFtMSaAZDppXgcDig0WjwrW99C36/H9euXUNPT08VMesoIOOs2a6/TqeD3++vEk0kIyYYDOJvf/sbtFotPvzwQ/zoRz/C+fPn8bvf/Q4bGxswGo1NRZ4ll+3U1BTu37+PXC4Hv9+Pn/zkJ02Tut9q4DkNh4HKQpCSr8/ng81mw+bmJrq7uzE1NYVkMonZ2VmWpfHRRx/hypUrmJycZBozfK2m01LxFhoalUqliiNG5xbj59DcyhM8yRPTiBAmlbO5ceMGvF4vPB4Pq0vV1tbG0tZ5rxcp5qbTaeaR6ezsZF4P4FVNrHoaYjxZvJanneaeXC7HjDGDwQCFQsE8FeVyGZubm5ifn8edO3eg0WgwOTmJr3/96/B4PPjrX/+KRCLBvOd8tpMQwkyj4xgzYkYxb9jSZ2KxGCKRCO7du4eHDx8in8/D7Xbj6tWrbIMoPD55A8lYpUyseqKhhgx5BCh0dOnSJYTDYSwtLTGyaSAQYLyZ3d1dRipqxgWV0N7eDqvVKqoB0AgolUp0dnZicHAQer0eg4ODsFqthwoqUe0lHhqNhpHVmgnk1uXbWy6XEYvF2C66u7sb4+PjjA9AYmYul6upxBYpGyOTyWBnZweVyksNJYvFwhYaCceHGPmT/k9hC/o/7aB7e3uhUCiY2ng6na7idahUKjidTmQyGfT19bF6ccArYiYtxmLtOCrEFh+xPgCvnglhqRTyEnd0dBwwisnjwWdF8TopYjyTekClUsFgMKC3txc6nQ5dXV2w2Ww1s154fhOfAk3hfOH36vnM1OJtCg1AvmYScaj4zKpyuYxEIoFoNIpYLIbh4WGMj4+zSumVSoUVmOTDyULStVj211H6LcYhE3pPhP0GXmmJUWV4meylRApFTYTn4MOffDi03mj4iiSTyTAyMoKBgQF4PB7Mzc3hD3/4A4LBIAKBAO7evYv79+8jmUxiaGgIt2/fhtlshslkanTTa8JgMODtt9+G1+ttdFMAgBGxPvzwwyq3bq0Btb+/z1RN+QHtcrmYYFYzIZvNIhQKVRXBy+fzmJ6extzcHAqFAoaHh/Gzn/0MWq2W6eOEw2HcvHkTFoulaQyEzc1NPH/+HKlUiu00FQqF6EQh4WioFT7l3fViGyPaOU5OTiKfz2NkZAQLCwvQaDQsw+S///0vHj58iFAohP7+fvz4xz9mKsA0aZMxQec4i9Ag1cHZ2dlhmioymYwR9qkiN6+vwoelSKeFQlCnCaPRCIPBAIvFgmKxyGT+iY9B3CVhKQbeQFSpVDCZTLDZbEyfjA8V1ssjQ5lpYiFKXqGXsqyUSiUTxsvlcsyoLBaLePr0Kebm5rCxsYH3338fv/jFL9DW1obd3V3E43FEo1GW8UqeqXK5zK4NgfecHDWUyRsrQsK4MK2fN9aoFhZVI1epVNBqtXA4HIyjKPRE0r3jdYnqjaYwZGSyl7n+drsd5XIZ3/3ud7G0tITnz59jZmaGueB2dnZgt9sxODiI9957r6HtjkQi+Pe//421tbUD7/G1JpoBNHiOSjoulUqsqqkwG4sktpsJKpUKZrOZFYkDXvaBZOYvXLjACv5RXJrcnAMDA3C73Q3uQTWEO19CsxhbrQihy50nZPKLE+1+aUe9v7/PNgLd3d1ob29HPp/Hs2fP4HA4sLi4iEQigUAggN3dXSbgZjKZGDmTwNfWEdvVH7Ufwj7xfSiVSqxIIc/pIe8KZcHp9Xq2IaEFkudw0JzMZ1/VE2IEXLVazdLaea8K780iAysWiyGVSrH709nZCb1eX0Wo5VXK6Vj8Ik39fJPrX+uzPIdHyJHc399nxiTfj3A4jO3tbVaCQS6Xsyy5nZ0dyGQydHV1wWKxsGtGishi4+BNQmhkyIiFKGvxVyg8ROOJvs+HKoXXlL/2vHdG7JqKeYmOiqZZkeRyOZPJ9/l8WFxcxOzsLHZ2dhAOhzEzM4PFxUXs7Ozg3XffxeTkJIDGTe6hUAh37txBOp0+8N7+/j4r595KoIFULBYxPz+PYDAI4NU1pqJtzWbIENmM57oUi0UsLS2hVCrhnXfeYdkZiUQCqVSKVccdHR2F3W5vYOtrQzJc6gOxSR94VQ8HeEXc7OjoQLlcRrFYRD6fZ7WX2tvb4Xa74XK5cOHCBczMzMDn8+HPf/4zotEonj17hvX1dVZDbmhoiEm103nJkOFJoscNMfGLFxlcpHhLqfsUBqOsQ/IUyGQymEwm9rwUi0VGmucLEfLEciFp+jgQ3gc6Hh/KE/sOeSGAlyRfqnQdi8XYe1qtFiaTqWqjJRQe5K8V7605CsQWW2E/eE8NGTFk+OZyOWaQUahpZWUF2WwWIyMjTJR0eXkZz549w9bWFtra2uDxeOBwOACAjUulUlkV7uM9K3xbD4MYCZo3bsSOITRk+H6T0Uv/48OAhFoeo1rP55uguVYkgKkZ+nw+GI1G3Llzh5HSCoUCAoEAurq6MD8/D7vdDqvV2pB2ejwefO9738PU1BSePHlS9Z5Op2Mp5K2GSuVliubS0hLW1tZQqVSg1WqZ4ulhpLNGIR6P43//+x/eeustVCoVrK2tIRgMYnp6GmazGR988AGsVitKpRL+8Y9/4MGDBzAajbDZbNDr9U0r+S+Xy2Gz2dgCSqmoEt4ctIgT54PPViINED5dmiZmMhCAVy58uVwOo9GI3t5eeL1eRCIRrK6uIp/P48WLF9Dr9Sy9n8i/J+XH8P0AqhctXgNHLpdDr9fDYrHA5XIhFAohGo2ydF7g5SJrMplqhoyE9Y+o73y6+knAexx5bRIe1BeSsqD+5nI5bG9vM0OGSM4mkwl6vb5K7oK8T2ILcz0MslrGDZ2bzk+fyeVyyGQyLOuHpBXeeustVn/w0aNHmJ6ehtvthsPhQG9vL6NRyOVyVjmd74eY5trrIOZ1IUND2C9e74V/KRQKmM1mWCyWqvFE901MA4vutdg4Ponnr2nyOGlQk7XMy7HTRSiXy0zgKRaLIZvNNqy9ZrMZY2NjTLCJB1W6bmYeTy1Qel00GkUymWQTBRkxarW6aTwFbW1tUCqVyOVyWFlZYXoZiUQCGxsbCAaDSCaTrKL69vY2Hj9+jLt378JgMLACbkK9g0aCr1/S1tYGo9EIk8l06MIj4XDQxMkT2HmuCr9Tp/do0VCpVFW8B0qLbW9vh06ng16vh8FgYGGdWCzGuCnk/eC9G/UwaGhhEEsDJq+STqeDyWSCTqerIsACYOnlvAEvDAcIF0Xek1EPCA0k/kVGAM9fIuOEFNWTySQr0Et1rzQaDZufhPe0nuAXYuHCzH8GqL43ZITR+lUsFtHe3s70oZLJJOOG2mw2+P1+WK1WliLP83DE7tebcK9q3WvhmOTDccK+keYSqUWTR402C7UMVf6a1Ws8NY1HhjgN0WgUjx49QjweRyQSwYMHD9jkQ+7d/v5+jI6ONnRip4en2cIsJ0GlUmFZY+FwGMlkEsCrYoU6na6pBNlsNhs++eQT/Oc//8Gf/vQnzM7OYnp6Gul0Gtvb2+zBisVi+OKLL3D//n0Eg0Hs7e3hBz/4AS5cuNB0xOVIJIJnz56xwnl6vZ7xGb5KY+0sIZfLUS6XkcvlGIGXJm+lUlll5JAByXslqG7R3NwcdnZ2kMlkEAqFEAgE8ODBA4RCIRQKBWZYU9aS8H7R3yd5fsR23aQTQ9wSImBaLBZmxNBCZzQaYTabYTabWWiJvx58CExMF+ekEO7EydsuVMXl+8t/NxqNIhQKYXV1lXlktFotXC7XAY+xMFOM+nrc8AWBN7J4zxHf3s7OTpjNZgwMDLDyKEtLS9jf38f6+jqSySRWVlawtbWFR48eIZ1OIxqNMs7llStXcO7cuSrBRjFxxeN6ZMTGIE+sprCrMPyUTCYRCoWwt7eHtrY2+Hw+JoRH5SN4wUBqO28oCVWv+b4cFw2dGWny2N7eZsJlGxsbePLkCWKxGDY2NthiCry8+FqtllmBjQxx8DsF/iZptVpmQbciSMckl8uhUCgAAMsIUKlUTRVWUqvV6O/vRzAYhFarRTwex9OnT1EsFpFMJtnitbS0xGp5dXR0wOVywev1oru7u6n6A7ysOxOPx1kYxGw2MwJjsxiQrYRaO0x+URAubLQDpYmdpPADgQDS6TRisRjC4TCCwSBisRgTxFQqlTAYDNDpdKKGTL3CS0LwYR+eaCoMQ9D8SdomtEgKd/O8AVAPbowQQkIpv0gLvWR0bjKkstksMpkMtra22HVXqVQsq48nLAtRy+twnPa/7rqQ+KjD4UA+n8fm5iay2SzC4TBWVlaYR0kmkzEF5nA4DIVCAavVCqfTyVRyeT6L8L4Ir+VRITQQ6Sdf8oDvH723s7ODRCLBDHcKK9FY48NQYuEiseetHuOqoYZMKpVCMpnEZ599hsXFRUxNTbGJgq/7AYDFrc1mM3Q6XcMndblczrIS2tvbUSqV0NHRgcnJSUxMTDS8fcdBpVJBMBjEwsJCFVHZ4XDg8uXLTcf50Wg0GB8fR7lcRiaTwdTUFP7yl78wvZV4PI54PI5f//rXsNvtcLlc+PjjjzExMYH+/v6m88YAQDQaxcLCArLZLNRqNSYnJ3HhwoWmCX21KijkQiEinpwIVFcw3t3dRSaTYard9+7dw9raGj7//HPE43GEQiEW/qBdq06ng81mw/Xr1zE6Ogq3230qHmOxnSxfUJHAk0/pO+3t7ejv70dvby+rwgygyuAiLiIROHnyqvC8x4FYqEcYcqN+8u2gMEUkEkEoFMLGxgarem80GtHf3w+bzVZFRTgtrTGxlHXheYiHdOvWLczPz2Nvbw+pVArRaBRzc3NIJBLY3NyERqNBuVxGZ2cndDodrl+/jsHBQYyNjTHxV6JcCDczvHfrpLwrGsf0N40f6huRrIPBIGZmZpDNZqFUKjE2NoahoSG2BuZyuQPVus8CZ2rI0EOSzWaRTCaxtLSE1dVVPHz4EGtra4hEIkySmS4mxT29Xi+sVivefvttDA8P13WHcBxQ7QkyZmiXZ7FYRAsxtgIqlVfFwHgxLKvVisHBwaYjm9IEaLfbcenSJWSzWcjlcqRSKezu7rJdUV9fH3w+H86dO4fBwUF0d3cfunNrBEhuPZ1OI5FIoFKpQKVSoaenBw6Ho6na2orgJ2Y+44fP1KA02VQqhdXVVUSjUSQSCTx+/JjVHqOyJLTAkvfF7XbD6XRieHgYHo+HeS+Psnt/E4gV2hOGwoBXAnE0P9F3yGPE959fCMnT/LoQT633xCDGrxC+Twv1YdyTSqWC3d1d7OzssPAXqebabLYqUrCwX2IS/yflKAmPwRsEpGBrMBhgt9vh8/nQ0dGBZDIJvV6PXC4HpVKJjo4O2O12dHV1wePx4Pz580wqgtdeEXo4+Ovypt4N+ozYfeT7xo/bUqnErj2lhhN3Uq/XH/Cw8UTf0/DACHGmhkypVEImk0EgEMC9e/cwNTWFx48fY3NzE3t7e6LfoZt8+/ZtDAwM4Pr1602RZULaJUajkdXGUCgUcLlcsNvtLbnwVCoVloFBRe/UajUGBgbw/vvvN8V1F4PP54PX68Xly5extraGTz/9FIFAAJlMBj6fD7/85S8xNDSEixcvVqUuNhN2d3dZLD0YDEIul8NgMGB0dBQ9PT0tOZ6aDWIZN5S1pFKpUCqVEI/HsbS0hOnpaTx9+hSBQAALCwvIZDJVNYtIWM7n88Fut+Pq1avo6enBzZs3GcFWSDg9KceJ56/w5GHaSPFjRKlUMn6VTqdDLBaDXC5nBRVlMllVjSN+980XPqzVDsKbjEuxRRh4JcB32LNJRk06nUYymWT6MZTV19/fX1WiBKgO5Yllph0XYm2k+0yidxTeo5p2Go0G6+vrbK3r7OzE7u4u7HY7rl27hkuXLuHatWsHSNhAtRFO5xIaavS540IofgdUZ30VCgWkUin2am9vh8FgqNpoUTtJ74b3iJ2Ul/Q6nKohQylnxWKRCTR9/vnnWF1dxfz8PEKhENP04CcIq9UKl8sFv9+P4eFhuFwujI6OwmKxNFWGiXBXVy8GdiMRj8exubkpmsHRrOEyeogsFgsUCgVGRkagVqsRDofR29vLtGKoD80yfnjQw08vCoFkMhmYTKaW5l01A4T3nBaDZDLJCLykEL24uIgHDx6wFN+dnR2mV6LRaOByuVj1697eXjgcDgwNDcFsNjPtGJoPhJLy9QC/4wVwwPNAiw8tiHzYgDwF/HHEDCzeeyGWpUPnPQ5qeQQIYt4TCm1sb29je3ubGQt85hgfggJelcCpdV6xMN1R2y4EXQs6J1/9nKpfq9VqOBwO9kwDL8P24+Pj8Hq96OzsZGRkvg/8uU/iiREDP8/z14cfY1QjKp1OM34SfTefz6NQKDDDikJKQq8hf9x6tZ1H3Q0ZoStwb28P2WwWKysrWFhYwB//+EdEo1FEIpEDbGbaDbjdbkxMTODdd9/F5cuX2Y60GRcgoPYD2UqgQUb1P/g0VXKjNytoXNBCMjw8DLVajefPn8Pv9+PixYtNn/FDu2N6yWQytgEwm81SeOkNcJTnkXbQiUQCsVgMCwsLSKVSWFhYwNLSEr788ssqwjtN0iaTiZVUoZ8OhwMOh6OKF8BvbupJ1BYL09Cx+UWJ2kscC5pr+YJ9ZGiJhXDoOK8L9bzpmDzKDl0s5FUsFpkhw9cgI0NGr9ezOaqWESAMA9XDW8BfDwrx8bwWhUIBlUqFjo4O2Gw2lMtlpFIpGAwGlEolOJ1OjI2NwWAwQK1Ws74TR1Ho1RBex+O0W+weCo1Wmo9obBUKBWQyGWQyGWxvb0OtVjMPFAkuioU4+fPUGjvHMSiFqPvsThVkp6enMT09jbW1NWQyGfYzGAyyjgMvG+92u2G1WnHz5k24XC6MjY0x5rbBYGjKCZwqLrtcLthsNmSz2aZs51FBHoFSqVTlIWs1yGQy2O12lEol6PX6ltVeoZj07OwsAKC/v7/BLWot0I6SMi3y+Tzi8TjT8tjb28Pe3h6CwSDi8ThmZmaQTqcRDocZyZc4DIODgzCbzYwHMzExAYfDAZfLhc7OTqhUqqpFlBYFsXTZk0AmkzGvA3CwAjbp1pBkf3d3N6xWKyP20ns8cZdCAWQ8C/sg1vbD3ntd+8XUZ4mTQ4sgn5rNGzJ7e3vMkBFmlvFaJ2JzF88xEXKj6DvHNQqIT8jfH3oVCgWWQcZ/RyaTwWq1VkUZhDpFYkZBPUM0taII9H/SUAJecUKpaHM+n0c6ncbc3BwqlQq8Xi/UavWhCRSv88SdBCc2ZGjCoGqr+XweW1tbmJubw9TUFEKhENLpNCKRCLMyqUNUw8TlcqGnp4fFmcfGxpi8c7OC2k5CTPQw8pNaK4GE8AqFAssUa0XIZDImBmaxWA7EzZsVNJHzxfKKxSLC4XDTllBoZvC7SvIKRyIRbG1tIRqNsgVxbW0NyWQS8/Pz2NraqpJ7UKlU0Ov16O3tRXd3N/r6+uB0OnHx4kUYDAYWHjgs7FLvzY1YxgyB58mQqjAlJJBXiA8t8YYFr//B/04Qnus44ZhapE+hUSTmbeDLRuTz+QO7+9ctjod5f07q1eAVn/lrSuui8FpSxiupEZORyYsB8qF84fg67egEnY8PzZGHj150H+LxOBKJBNNoqnUfTpsrc2JDZn9/H0tLS9jY2MDf//53bG5uYnl5GZFIBJubm8zC5BdHeqAmJiZw4cIFfP/734fX62UKrM2kHnsUyGQv9WP0ej1u3rwJr9fbtHySWiABwmAwyMi+zUiKPQosFgsMBgN+9atfHagU26zQarXw+Xzo6+vDuXPnsLKywip4KxSKA/oOEl4PWhh2d3eRTqexvLyM5eVl3L17F4lEAslkks1PtDhSjRyNRoPLly9jaGgI3/nOd9DT01M1P/FKs3wNJb7g4WlA7NhCrwDwykihsLDJZGLZVaRPUktpWIz4yYce+M/WE7zxyYfl2tvbkcvlsLOzg62tLWxvb1dxMkiAkO7JWS6o1GbaCL5Oa6utrQ3nz59HX18fALBwJV1fXtuHbyuftl5PD99hoSChp0mn06G7uxt+vx8LCwssNJtIJLC1tQWFQoHOzs4zM7h41MUjE4/Hsbq6itnZWUSjUayurla5Z5VKJcxmM7toWq0WVqsV4+PjOH/+PNvx8K6sVgE/WfBWdquB96jxQnhOp5OVZ28F8DwAp9PZ6OYcGZQu6/f7We2VYrEIv98v8WOOAT78wXu7iIi5t7eHTCbDPkskXVJR1mq1GBoawvDwMHp6etDV1cXSTIWLJD9h15vEKDyPEEICJb/4EceBPMek7/E6TZhawn21PConaf9hn+H7Qxol5DUmI4c84m/qwTgJx+RN+iUkHANgpV7ofaEBKgzB1CLJnrS9tYw5sTZQYU6fz4dLly5Bp9OhXC7D4/GwRAphIUt6Vk7zmSCc2JApl8t49OgRnjx5gqmpKezu7lZdeJvNBrPZjK997WvQarVoa2vDyMgIvvnNb7KH67SEi84CNEmQXkOrGjJisNvt+OijjzA2NtbopnzlQQvOxx9/jB/+8IdVMXyq1SPh6CCDViaTMfd9b28v2tvbsb29jefPnwMAI41eu3YNXV1dGB8fZ95VSlXW6XRsp8wLxhH3odFzF/FD+EWTyJmkFEtepmYeR7SAisk8ZLNZtvOnNaazsxNDQ0Nwu91Vc26pVKoSkKsV6q/HAkveCrHvk3yF8D0i9eZyuarsIDHuEe+dOisPB803vCiqTqeDVqvFJ598gtu3byOTyaBcLsNkMh14DnjC8Fm1+cSGTFtbGwYHB5miIt95ACx1lJRUZbKX5F6TydT0PJijoK+vDzdu3MDTp08hl8uRTqdZNdZWglqthtFoxMDAAHZ3dxGPx+FyuXDp0iW43e5GN+8rDyFvTMLxQTtB2qHTvEMFXrPZLAwGA9xuN8sqoWr1RFrs6OiAXq+vWvzFuBhn6T4nCHfqYoYMufm7urrQ39+PbDbLOD9UE0csO+iwvtSTZMr/LsyeomvKb3CpfpTX68X+/j4cDge6urrg9/thsViqFlEieQvPUauPJ+nX64whYSYZ7+3itXvEjJijHP8kOOx4/BjjDRKan4jTQ5pDQiPmtLgwtXBiQ0ahUOC9994DAPz0pz89cYNaDaQ0/K9//QvpdBrr6+soFoswGo0N36m9CfR6PZRKJW7cuAGr1Yr79+9jaGgI3/72t5tSyl+ChMNAz55cLmeu/Pb2dlgsFjidTpY2qtPpmOTD6zZVwiJ6jX6+iRgq1AIhr4bJZMLQ0BB0Oh1yuRzkcjnsdju0Wi1LUSYDSCwEctrgOTH0ouvLp4kDL+kIdrsdV65cgcfjQaFQgMViwaVLl+ByuaqOyxcA5b2atcIop9k/6hOAA/wp0hs6rB1C4+ssxp0wJZ/4krw3TywblM8gO2uc2JBp9MPcaBDJbGJiArlcDmaz+bXKmM0IhUIBtVqNGzduYGRkBNevX4fVaq0i0UmQ0ErgY/SUKULeB1rsiF8httAJvS8koV/vtOrjQijCJuTqKBQKVpW4UChAJpPBbDYfKJ/QyH6Qp4LP3BHT3dFqtZDJZBgZGYHH40G5XIZOp4PH4zkQyudLxhx2r+rliRGCHzeHZZkJ/3cYX+WsuCb8sfl5n8/C470uYoU9z9LoYm2unEZStwQJEiQ0EXiZCD6DUqVSNb1YohC82x84fMEQakPRxoQWWFp4Titd/DAIw0rkRaH28cYleWxisRjTIVOpVLDZbAdE/ngvCBmxZ7mo0rkpm4nacZhg3FGOR8c5TQjHlvC8ZLAIvS9inqOjhizrAcmQkSBBwlceYhkgwMkrBzcCYlN2rT4IxeL4/ooZQmdtyAh/F5Ja6SctmIVCgf3OqxcLORo8zrpvYv2i8x+nDY0wZGqBDBn6bK221ZOHdBRIhowECRIkfEUhnN5rGUGtZsxJaDyEBlsjx5FkyEiQIEGCBAkSWhatJT8rQYIECRIkSJDAQTJkJEiQIEGCBAktC8mQkSBBggQJEiS0LCRDRoIECRIkSJDQspAMGQkSJEiQIEFCy0IyZCRIkCBBggQJLQvJkJEgQYIECRIktCwkQ0aCBAkSJEiQ0LKQDBkJEiRIkCBBQsvi/93pLYx8Iuo2AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 4))\n",
+    "for idx, X in enumerate((X_train[::2100], X_recovered[::2100])):\n",
+    "    plt.subplot(1, 2, idx + 1)\n",
+    "    plt.title([\"Original\", \"Compressed\"][idx])\n",
+    "    for row in range(5):\n",
+    "        for col in range(5):\n",
+    "            plt.imshow(X[row * 5 + col].reshape(28, 28), cmap=\"binary\",\n",
+    "                       vmin=0, vmax=255, extent=(row, row + 1, col, col + 1))\n",
+    "            plt.axis([0, 5, 0, 5])\n",
+    "            plt.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Randomized PCA \n",
+    "\n",
+    "*Randomized PCA* quickly find an approximation of the first first $d$ principal components.\n",
+    "\n",
+    "It is much quicker than the full SVD approach.\n",
+    "\n",
+    "Its computational complexity is ${\\mathcal O}( m\\times d^2) + {\\mathcal O}( d^3)$, instead of ${\\mathcal O}( m \\times n^2) + {\\mathcal O}( n^3)$, so it is dramatically faster than the previous algorithms when $d$ is much smaller than $n$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:21:55.866516Z",
+     "iopub.status.busy": "2025-02-27T23:21:55.866345Z",
+     "iopub.status.idle": "2025-02-27T23:22:02.100911Z",
+     "shell.execute_reply": "2025-02-27T23:22:02.100391Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "rnd_pca = PCA(n_components=154, svd_solver=\"randomized\", random_state=42)\n",
+    "X_reduced = rnd_pca.fit_transform(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:02.103482Z",
+     "iopub.status.busy": "2025-02-27T23:22:02.103301Z",
+     "iopub.status.idle": "2025-02-27T23:22:02.108558Z",
+     "shell.execute_reply": "2025-02-27T23:22:02.108081Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "154"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rnd_pca.n_components_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Incremental PCA \n",
+    "\n",
+    "One problem with PCA is that it requires the entire training set to be read into memory at once.  Thus, cannot apply to very large training data-sets. \n",
+    "\n",
+    "Instead *Incremental PCA (IPCA)* splits the data into batches, and allows for combination. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:02.110486Z",
+     "iopub.status.busy": "2025-02-27T23:22:02.110313Z",
+     "iopub.status.idle": "2025-02-27T23:22:20.395636Z",
+     "shell.execute_reply": "2025-02-27T23:22:20.395095Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import IncrementalPCA\n",
+    "\n",
+    "n_batches = 100\n",
+    "inc_pca = IncrementalPCA(n_components=154)\n",
+    "for X_batch in np.array_split(X_train, n_batches):\n",
+    "    inc_pca.partial_fit(X_batch)\n",
+    "\n",
+    "X_reduced = inc_pca.transform(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:20.398654Z",
+     "iopub.status.busy": "2025-02-27T23:22:20.397666Z",
+     "iopub.status.idle": "2025-02-27T23:22:20.403297Z",
+     "shell.execute_reply": "2025-02-27T23:22:20.402792Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "154"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inc_pca.n_components_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "**Exercises:** *You can now complete Exercise 1 in the exercises associated with this lecture.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Random projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Even with randomized or incremental PCA, PCA can be too computationally expensive for very large data-sets.\n",
+    "\n",
+    "An alternative it to consider *random* projections to lower dimensional spaces through linear projections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Random projections can actually preserve distances reasonably well.  Hence, two similar data instances remain similar after projection.  Similarly, two very different data instances remain very different after projection.\n",
+    "\n",
+    "The more dimensions that are dropped, the more information is lost, and hence the more distances get distorted."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Selecting the reduced dimension size\n",
+    "\n",
+    "There is some deep mathematical theory that specifies how much dimensions can be compressed in order to ensure distances are distorted by less than some tolerance $\\epsilon$.  \n",
+    "\n",
+    "This is called the *Johnson-Lindenstrauss* minimum dimension:\n",
+    "\n",
+    "$$d \\geq \\frac{4 \\log(m)}{\\epsilon^2/2 - \\epsilon^3/3}.$$\n",
+    "\n",
+    "(Doesn't depend on number of features $n$.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:20.405853Z",
+     "iopub.status.busy": "2025-02-27T23:22:20.405675Z",
+     "iopub.status.idle": "2025-02-27T23:22:20.409905Z",
+     "shell.execute_reply": "2025-02-27T23:22:20.409455Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5920"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m, ε = 1_000, 0.1\n",
+    "\n",
+    "d = int(4 * np.log(m) / (ε ** 2 / 2 - ε ** 3 / 3))\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Or using Scikit-Learn:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:20.413424Z",
+     "iopub.status.busy": "2025-02-27T23:22:20.412448Z",
+     "iopub.status.idle": "2025-02-27T23:22:20.419642Z",
+     "shell.execute_reply": "2025-02-27T23:22:20.419208Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(5920)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.random_projection import johnson_lindenstrauss_min_dim\n",
+    "\n",
+    "\n",
+    "d = johnson_lindenstrauss_min_dim(m, eps=ε)\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "Then can compute a random projection down to dimension $d$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:20.421721Z",
+     "iopub.status.busy": "2025-02-27T23:22:20.421549Z",
+     "iopub.status.idle": "2025-02-27T23:22:23.802948Z",
+     "shell.execute_reply": "2025-02-27T23:22:23.802408Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 5920)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n = 8_000\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Radom projection matrix\n",
+    "P = np.random.randn(d, n) / np.sqrt(d)  # std dev = square root of variance\n",
+    "\n",
+    "X = np.random.randn(m, n)  # generate a fake dataset\n",
+    "X_reduced = X @ P.T\n",
+    "X_reduced.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Or use Scikit-Learn built in functionality:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:23.805852Z",
+     "iopub.status.busy": "2025-02-27T23:22:23.804842Z",
+     "iopub.status.idle": "2025-02-27T23:22:27.060605Z",
+     "shell.execute_reply": "2025-02-27T23:22:27.060048Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 5920)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.random_projection import GaussianRandomProjection\n",
+    "\n",
+    "gaussian_rnd_proj = GaussianRandomProjection(eps=ε, random_state=42)\n",
+    "X_reduced = gaussian_rnd_proj.fit_transform(X)  \n",
+    "X_reduced.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "slide"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Locally linear embedding (LLE)\n",
+    "\n",
+    "Locally linear embedding (LLE) is a powerful non-linear dimensionality reduction tool.\n",
+    "\n",
+    "It is a form of *manifold learning* and doesn't rely on projections.\n",
+    "\n",
+    "LLE measures how each instance relates to closest neighbors, then looks for low-D representation where local relations are best preserved."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### First Step: linearly modelling local relationships\n",
+    "\n",
+    "- First, for each training instance $x^{(i)}$, the algorithm identifies its $k$ closest neighbours.\n",
+    "- Then tries to reconstruct $x^{(i)}$ as a linear weighted sum of these neighbors.\n",
+    "\n",
+    "More specifically, it finds the weights $w_{ij}$ such that the squared distance between $x^{(i)}$ and $\\sum_{j=1}^m w_{ij}x^{(j)}$ is as small as possible, assuming $w_{ij}=0$ if $x^{(j)}$ is not one of the $k$-nearest neighbours of $x^{(i)}$.\n",
+    "\n",
+    "That is, solve the minimisation problem\n",
+    "\n",
+    "$$\n",
+    "\\text{argmin}_{W} \\sum_{i=1}^m \\left( x^{(i)} - \\sum_{j=1}^m w_{ij}x^{(j)} \\right)^2 ,\n",
+    "$$\n",
+    "\n",
+    "where $W$ is the matrix of weights.  Note that weights are constrained to be normalised such that $\\sum_j w_{ij}=1$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Second step: reducing dimensionality while preserving relationships\n",
+    "\n",
+    "The second step is to map the training instances into a $d$-dimensional space (where $d < n$) while preserving these local relationships as much as possible.\n",
+    "\n",
+    "If $z^{(i)}$ is the d-space equivalent of $x^{(i)}$ then we want the squared distance between $z^{(i)}$ and $\\sum_{i=1}^m w_{ij}z^{(j)}$ to be as small as possible, given **the fixed set of weights** from step 1. \n",
+    "\n",
+    "That is, solve the minimisation problem\n",
+    "\n",
+    "$$\n",
+    "\\text{argmin}_{Z} \\sum_{i=1}^m \\left( z^{(i)} - \\sum_{j=1}^m \\hat{w}_{ij}z^{(j)} \\right)^2 ,\n",
+    "$$\n",
+    "\n",
+    "where $Z$ is the matrix of lower dimensional positions and $\\hat{w}_{ij}$ are the weights determined in step 1.\n",
+    "\n",
+    "Note that step 2 optimises positions of $z$ for fixed $w$, while step 1 optimises the $w$ for fixed positions $x$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Plot swiss roll"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:27.063998Z",
+     "iopub.status.busy": "2025-02-27T23:22:27.062959Z",
+     "iopub.status.idle": "2025-02-27T23:22:27.067705Z",
+     "shell.execute_reply": "2025-02-27T23:22:27.067256Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_swiss_roll\n",
+    "\n",
+    "X_swiss, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:27.070034Z",
+     "iopub.status.busy": "2025-02-27T23:22:27.069836Z",
+     "iopub.status.idle": "2025-02-27T23:22:27.289425Z",
+     "shell.execute_reply": "2025-02-27T23:22:27.288912Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.colors import ListedColormap\n",
+    "\n",
+    "darker_hot = ListedColormap(plt.cm.hot(np.linspace(0, 0.8, 256)))\n",
+    "\n",
+    "axes = [-11.5, 14, -2, 23, -12, 15]\n",
+    "\n",
+    "fig = plt.figure(figsize=(8, 7))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "ax.scatter(X_swiss[:, 0], X_swiss[:, 1], X_swiss[:, 2], c=t, cmap=darker_hot)\n",
+    "ax.view_init(10, -70)\n",
+    "set_xyz_axes(ax, axes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### LLE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:27.291420Z",
+     "iopub.status.busy": "2025-02-27T23:22:27.291245Z",
+     "iopub.status.idle": "2025-02-27T23:22:27.383133Z",
+     "shell.execute_reply": "2025-02-27T23:22:27.382651Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.manifold import LocallyLinearEmbedding\n",
+    "\n",
+    "lle = LocallyLinearEmbedding(n_components=2, n_neighbors=10, random_state=42)\n",
+    "X_unrolled = lle.fit_transform(X_swiss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2025-02-27T23:22:27.385572Z",
+     "iopub.status.busy": "2025-02-27T23:22:27.385146Z",
+     "iopub.status.idle": "2025-02-27T23:22:27.570413Z",
+     "shell.execute_reply": "2025-02-27T23:22:27.569904Z"
+    },
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Unrolled swiss roll using LLE')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_unrolled[:, 0], X_unrolled[:, 1],\n",
+    "            c=t, cmap=darker_hot)\n",
+    "plt.xlabel(\"$z_1$\")\n",
+    "plt.ylabel(\"$z_2$\", rotation=0)\n",
+    "plt.axis([-0.055, 0.060, -0.070, 0.090])\n",
+    "plt.grid(True)\n",
+    "plt.title(\"Unrolled swiss roll using LLE\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Swiss roll is unrolled and distances between instances are well preserved locally.\n",
+    "\n",
+    "However, distances not preserved on a larger scale."
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}