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add sklearn tutorial
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‎sklearn-tutorial/.ipynb_checkpoints/sklearn-datasets-checkpoint.ipynb‎

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{
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},
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"outputs": [
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{
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"name": "stdout",
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" 22. 11.9]\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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"from sklearn.linear_model import LinearRegression\n",
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"\n",
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"\n",
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"loaded_data = datasets.load_boston()\n",
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"data_X = loaded_data.data\n",
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"data_y = loaded_data.target\n",
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"\n",
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"print(data_y)"
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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": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
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]
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},
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"execution_count": 5,
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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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"#线性回归\n",
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"\n",
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"model = LinearRegression()\n",
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"model.fit(data_X,data_y)\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": 6,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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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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"[ 30.00821269 25.0298606 30.5702317 28.60814055]\n",
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"[ 24. 21.6 34.7 33.4]\n"
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]
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}
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],
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"source": [
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"#对比前四个值与预测值\n",
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"\n",
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"print(model.predict(data_X[:4,:]))\n",
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"print(data_y[:4])"
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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": 7,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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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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"[[ 0.53484728]\n",
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" [ 0.09466198]\n",
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" [ 1.34507271]\n",
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" [-1.31088444]\n",
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" [ 0.58025056]\n",
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" [ 0.7857833 ]\n",
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" [-0.6465785 ]\n",
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" [ 0.92960333]\n",
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" [ 1.38840659]\n",
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" [ 0.19348591]\n",
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" [-1.1664881 ]\n",
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" [ 0.75207622]\n",
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" [ 1.78021531]\n",
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" [-1.45425265]\n",
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" [-1.32332361]\n",
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" [ 0.0621699 ]\n",
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" [-0.39986477]\n",
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" [-1.12802116]\n",
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" [ 1.7483509 ]\n",
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" [ 1.32292197]\n",
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" [-0.37384565]\n",
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" [ 0.28216756]\n",
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" [-1.47542569]\n",
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" [ 1.5479627 ]\n",
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" [ 0.06995328]\n",
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" [ 0.2952387 ]\n",
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" [ 0.42768088]\n",
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" [ 0.450825 ]\n",
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" [ 0.88578399]\n",
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" [ 0.85147996]\n",
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" [ 1.01815836]\n",
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" [ 0.57378986]\n",
191+
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" [-0.23085505]\n",
193+
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194+
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" [ 1.60697398]\n",
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" [-0.65285564]\n",
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" [ 0.0198095 ]\n",
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" [ 0.03472977]\n",
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" [ 0.4685963 ]\n",
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" [-0.85911355]\n",
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" [ 0.55899829]\n",
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" [ 1.21070012]\n",
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" [-1.07870773]\n",
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" [-0.35157916]\n",
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" [-0.06284252]\n",
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" [-1.10413217]] [ 35.04137923 6.67445206 89.1887115 -85.86451558 36.98686662\n",
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" -72.92821527 51.86682499 -34.69786395 -42.83551589 61.82103167\n",
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" 92.73673928 11.58049078 -76.09868009 49.93617116 43.02138599\n",
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" 88.35398414 -23.89284497 19.63198733 -97.40042846 104.40656187\n",
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242+
" 24.60768426 8.23752438 3.4575212 -7.53113032 59.87988435\n",
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" -62.87627739 60.71576618 -138.47992707 -128.29409295 67.91874014\n",
244+
" 44.8192337 37.42835412 -5.34690779 -16.25498474 39.48704617\n",
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" 88.2313693 82.23809065 -42.16653969 74.44990164 21.38691013\n",
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" 32.2923918 -114.88114137 -136.1756398 36.16201735 3.35286591\n",
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" 110.82367051 71.60760825 -48.4528432 94.7096711 -99.83200908\n",
248+
" 135.61594923 91.15560295 66.09513697 -59.51164066 -46.04753082\n",
249+
" 103.77034245 -24.94603219 -104.40981804 73.57428866 -30.2955937\n",
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" 109.16199455 -122.47711418 -43.37879225 3.82008495 3.20863493\n",
251+
" 32.99653935 -56.2763452 -20.74694624 36.67869052 79.67552968\n",
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" -71.31635282 -6.55670787 -23.22650451 -3.62246158 -73.72908692]\n"
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]
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}
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],
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"source": [
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"#创造数据\n",
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"\n",
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"X,y = datasets.make_regression(n_samples=100,n_features=1,n_targets=1,noise=1)\n",
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"print(X,y)\n",
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"\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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"#绘制散点图\n",
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"\n",
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"import matplotlib.pyplot as plot\n",
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"plot.scatter(X,y)\n",
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"plot.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": 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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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [Root]",
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"language": "python",
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"name": "Python [Root]"
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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": "3.5.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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