166 lines
8.5 KiB
Text
166 lines
8.5 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 53,
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"id": "3cd9fe22",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'0.24.2'"
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]
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},
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"execution_count": 53,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.datasets import fetch_openml\n",
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"import sklearn\n",
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"sklearn.__version__"
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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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"id": "b35f064d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# mnist = fetch_openml('mnist_784')\n",
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"mnist = fetch_openml('mnist_784',as_frame=False)"
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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": 50,
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"id": "907bd199",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"100"
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]
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},
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"execution_count": 50,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# print(mnist)\n",
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"# print (mnist.data)\n",
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"# print (mnist.target)\n",
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"# len(mnist.data)\n",
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"# help(len)\n",
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"# print (mnist.data.shape)\n",
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"# print (mnist.target.shape)\n",
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"# mnist.data[0]\n",
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"# mnist.data[0][1]\n",
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"# mnist.data[:,1]\n",
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"# mnist.data[:100]"
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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": 61,
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"id": "d0e89d79",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Classe : 5\n"
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]
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}
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],
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"source": [
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"# from sklearn import datasets\n",
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"import matplotlib.pyplot as plt\n",
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"images = mnist.data.reshape((-1, 28, 28))\n",
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"plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation=\"nearest\")\n",
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"plt.show()\n",
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"print(\"Classe : \", mnist.target[0])"
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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": 82,
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"id": "2d870997",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['3' '0' '9' ... '3' '1' '5']\n",
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"[[0. 0. 0. ... 0. 0. 0. ]\n",
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" [1. 0. 0. ... 0. 0. 0. ]\n",
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" [0. 0. 0. ... 0.2 0. 0.8]\n",
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" ...\n",
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" [0. 0. 0. ... 0. 0. 0. ]\n",
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" [0. 1. 0. ... 0. 0. 0. ]\n",
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" [0. 0. 0. ... 0. 0. 0. ]]\n",
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"Classe image 4 : 1\n",
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"Classe prédite image 4 : 0\n",
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"Score échantillon de test : 0.9699523809523809\n"
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]
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}
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],
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"source": [
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"from sklearn import model_selection\n",
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"from sklearn import neighbors\n",
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"\n",
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"data = mnist.data\n",
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"target = mnist.target\n",
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"\n",
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"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.7)\n",
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"\n",
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"n_neighbors = 5\n",
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"clf = neighbors.KNeighborsClassifier(n_neighbors)\n",
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"clf.fit(xtrain, ytrain)\n",
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"clf.predict(xtest)\n",
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"clf.predict_proba(xtest)\n",
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"print(clf.score(xtest, ytest))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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