Split figure 3-9 in two, and compare models using PR curve rather than ROC curve
Aurélien Geron
parent
01d5df8e72
commit
97b371329c
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@ -766,8 +766,8 @@
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"outputs": [],
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"source": [
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"y_scores_forest = y_probas_forest[:, 1]\n",
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"fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,\n",
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" y_scores_forest)"
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"precisions_forest, recalls_forest, thresholds_forest = precision_recall_curve(\n",
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" y_train_5, y_scores_forest)"
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]
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},
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{
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@ -778,30 +778,21 @@
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"source": [
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"plt.figure(figsize=(6, 5)) # not in the book – not needed, just formatting\n",
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"\n",
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"plt.plot(fpr_forest, tpr_forest, \"b-\", linewidth=2, label=\"Random Forest\")\n",
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"plt.plot(fpr, tpr, \"--\", linewidth=2, label=\"SGD\")\n",
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"plt.plot([0, 1], [0, 1], 'k:', label=\"Random classifier\")\n",
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"plt.plot(recalls_forest, precisions_forest, \"b-\", linewidth=2,\n",
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" label=\"Random Forest\")\n",
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"plt.plot(recalls, precisions, \"--\", linewidth=2, label=\"SGD\")\n",
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"\n",
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"# not in the book – just beautifies and saves Figure 3–8\n",
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"plt.xlabel('False Positive Rate (Fall-Out)')\n",
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"plt.ylabel('True Positive Rate (Recall)')\n",
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"plt.grid()\n",
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"# not in the book – just beautifies and saves Figure 3–8\n",
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"plt.xlabel(\"Recall\")\n",
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"plt.ylabel(\"Precision\")\n",
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"plt.axis([0, 1, 0, 1])\n",
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"plt.legend(loc=\"lower right\")\n",
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"save_fig(\"roc_curve_comparison_plot\")\n",
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"plt.grid()\n",
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"plt.legend(loc=\"lower left\")\n",
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"save_fig(\"pr_curve_comparison_plot\")\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [],
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"source": [
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"roc_auc_score(y_train_5, y_scores_forest)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -809,14 +800,23 @@
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"We could use `cross_val_predict(forest_clf, X_train, y_train_5, cv=3)` to compute `y_train_pred_forest`, but since we already have the estimated probabilities, we can just use the default threshold of 50% probability to get the same predictions much faster:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_train_pred_forest = y_probas_forest[:, 1] >= 0.5 # positive proba ≥ 50%\n",
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"f1_score(y_train_5, y_train_pred_forest)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_train_pred_forest = y_probas_forest[:, 1] > 0.5\n",
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"precision_score(y_train_5, y_train_pred_forest)"
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"roc_auc_score(y_train_5, y_scores_forest)"
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]
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},
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{
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@ -824,6 +824,15 @@
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"execution_count": 57,
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"metadata": {},
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"outputs": [],
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"source": [
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"precision_score(y_train_5, y_train_pred_forest)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {},
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"outputs": [],
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"source": [
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"recall_score(y_train_5, y_train_pred_forest)"
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]
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@ -844,7 +853,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"execution_count": 59,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -856,7 +865,7 @@
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"cell_type": "code",
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"execution_count": 59,
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"execution_count": 60,
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@ -865,7 +874,7 @@
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"cell_type": "code",
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"execution_count": 60,
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"execution_count": 61,
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@ -875,7 +884,7 @@
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@ -894,7 +903,7 @@
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@ -922,7 +931,7 @@
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"cell_type": "code",
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"execution_count": 74,
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"metadata": {},
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@ -1054,32 +1063,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's put all plots in a single figure for the book:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 75,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – this code generates Figure 3–9\n",
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"fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(9, 8))\n",
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"ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[0, 0])\n",
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"axs[0, 0].set_title(\"Confusion matrix\")\n",
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"ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[0, 1],\n",
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" normalize=\"true\", values_format=\".0%\")\n",
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"axs[0, 1].set_title(\"CM normalized by row\")\n",
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"ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[1, 0],\n",
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" sample_weight=sample_weight,\n",
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" normalize=\"true\", values_format=\".0%\")\n",
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"axs[1, 0].set_title(\"Errors normalized by row\")\n",
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"ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[1, 1],\n",
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" sample_weight=sample_weight,\n",
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" normalize=\"pred\", values_format=\".0%\")\n",
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"axs[1, 1].set_title(\"Errors normalized by column\")\n",
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"save_fig(\"confusion_matrix_plot\")\n",
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"plt.show()"
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"Let's put all plots in a couple of figures for the book:"
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]
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},
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{
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@ -1087,6 +1071,43 @@
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"execution_count": 76,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – this code generates Figure 3–9\n",
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"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\n",
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"ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[0])\n",
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"axs[0].set_title(\"Confusion matrix\")\n",
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"ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[1],\n",
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" normalize=\"true\", values_format=\".0%\")\n",
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"axs[1].set_title(\"CM normalized by row\")\n",
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"save_fig(\"confusion_matrix_plot_1\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – this code generates Figure 3–10\n",
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"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\n",
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"ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[0],\n",
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" sample_weight=sample_weight,\n",
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" normalize=\"true\", values_format=\".0%\")\n",
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"axs[0].set_title(\"Errors normalized by row\")\n",
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"ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[1],\n",
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" sample_weight=sample_weight,\n",
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" normalize=\"pred\", values_format=\".0%\")\n",
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"axs[1].set_title(\"Errors normalized by column\")\n",
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"save_fig(\"confusion_matrix_plot_2\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 78,
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"metadata": {},
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"outputs": [],
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"source": [
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"cl_a, cl_b = '3', '5'\n",
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"X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"execution_count": 79,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – this code generates Figure 3–10\n",
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"# not in the book – this code generates Figure 3–11\n",
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"size = 5\n",
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"pad = 0.2\n",
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"plt.figure(figsize=(size, size))\n",
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},
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"cell_type": "code",
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"execution_count": 78,
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"execution_count": 80,
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"metadata": {
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"tags": []
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"execution_count": 87,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book – this code generates Figure 3–11\n",
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"# not in the book – this code generates Figure 3–12\n",
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"plt.subplot(121); plot_digit(X_test_mod[0])\n",
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"plt.subplot(122); plot_digit(y_test_mod[0])\n",
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"save_fig(\"noisy_digit_example_plot\")\n",
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},
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"cell_type": "code",
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"outputs": [],
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"source": [
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"knn_clf.fit(X_train_mod, y_train_mod)\n",
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"clean_digit = knn_clf.predict([X_test_mod[0]])\n",
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"plot_digit(clean_digit)\n",
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"save_fig(\"cleaned_digit_example_plot\") # not in the book – saves Figure 3–12\n",
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"save_fig(\"cleaned_digit_example_plot\") # not in the book – saves Figure 3–13\n",
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"plt.show()"
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]
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},
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -2011,7 +2032,7 @@
|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -2038,7 +2059,7 @@
|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -2082,7 +2103,7 @@
|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -2143,7 +2164,7 @@
|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -2333,7 +2354,7 @@
|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -2349,7 +2370,7 @@
|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
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|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -2588,7 +2609,7 @@
|
|||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2613,7 +2634,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 152,
|
||||
"execution_count": 154,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2646,7 +2667,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 153,
|
||||
"execution_count": 155,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2657,7 +2678,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 154,
|
||||
"execution_count": 156,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2673,7 +2694,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 155,
|
||||
"execution_count": 157,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2689,7 +2710,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 156,
|
||||
"execution_count": 158,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2705,7 +2726,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 157,
|
||||
"execution_count": 159,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -2728,7 +2749,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 158,
|
||||
"execution_count": 160,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
|
|||
Loading…
Reference in New Issue