mhjensen
102 lines
6.3 KiB
Plaintext
102 lines
6.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 12,
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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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"[[ 0. 0. 5. ..., 0. 0. 0.]\n",
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" [ 0. 0. 0. ..., 10. 0. 0.]\n",
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" [ 0. 0. 0. ..., 16. 9. 0.]\n",
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" ..., \n",
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" [ 0. 0. 1. ..., 6. 0. 0.]\n",
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" [ 0. 0. 2. ..., 12. 0. 0.]\n",
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" [ 0. 0. 10. ..., 12. 1. 0.]]\n",
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"[[ 0. 0. 5. ..., 0. 0. 0.]\n",
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" [ 0. 0. 0. ..., 10. 0. 0.]\n",
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" [ 0. 0. 0. ..., 16. 9. 0.]\n",
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" ..., \n",
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" [ 0. 0. 1. ..., 6. 0. 0.]\n",
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" [ 0. 0. 2. ..., 12. 0. 0.]\n",
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" [ 0. 0. 10. ..., 12. 1. 0.]]\n",
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"(1796, 64)\n",
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"prediction: [0 1 2 ..., 8 9 8]\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x1a17854da0>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy\n",
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"from sklearn import datasets\n",
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"from sklearn import svm\n",
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"digits=datasets.load_digits()\n",
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"\n",
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"clf=svm.SVC(gamma=0.001, C=100)\n",
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"print(digits.data)\n",
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"x,y=digits.data[:-1], digits.target[:-1]\n",
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"clf.fit(x,y)\n",
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"\n",
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"print (digits.data)\n",
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"print (x.shape)\n",
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"\n",
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"print(\"prediction:\", clf.predict(digits.data))\n",
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"plt.imshow(digits.images[-2])\n",
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"plt.show()\n"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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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": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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