151 lines
13 KiB
Text
151 lines
13 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "3eb7a65b",
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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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7i37sEnO15HHj7bJAELyDDgiCsgNBUHYgCMoOBEHZgSAoOxAEZQeC+D+ypTV9clByEAAAAABJRU5ErkJggg==\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.datasets import fetch_openml\n",
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"import sklearn\n",
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"import matplotlib.pyplot as plt\n",
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"mnist = fetch_openml('mnist_784',as_frame=False)\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": null,
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"id": "6cb9b8da",
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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": null,
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"id": "40907bdc",
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"metadata": {},
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"outputs": [],
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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": 2,
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"id": "f4dff3a7",
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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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7i37sEnO15HHj7bJAELyDDgiCsgNBUHYgCMoOBEHZgSAoOxAEZQeC+D+ypTV9clByEAAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Classe : 5\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# from sklearn import datasets\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"images = mnist.data.reshape((-1, 28, 28))\n",
|
|
"plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation=\"nearest\")\n",
|
|
"plt.show()\n",
|
|
"print(\"Classe : \", mnist.target[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "38f52261",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn import model_selection\n",
|
|
"from sklearn import neighbors\n",
|
|
"\n",
|
|
"data = mnist.data\n",
|
|
"target = mnist.target\n",
|
|
"\n",
|
|
"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.7)\n",
|
|
"\n",
|
|
"n_neighbors = 5\n",
|
|
"clf = neighbors.KNeighborsClassifier(n_neighbors)\n",
|
|
"clf.fit(xtrain, ytrain)\n",
|
|
"clf.predict(xtest)\n",
|
|
"clf.predict_proba(xtest)\n",
|
|
"print(clf.score(xtest, ytest))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.8.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|