mhjensen
138 lines
17 KiB
Plaintext
138 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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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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HoxoQ00R/mImUVAXuo3fODXDO1XbO1QM6Awudc/cAi4AOnt26AzN9rlLy7NWV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\nAAAASUVORK5CYII=\n",
|
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"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10ed08240>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.943743942225\n",
|
|
"---------K-Nearest Neighbors-------\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
|
|
" metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
|
|
" weights='uniform')"
|
|
]
|
|
},
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import mglearn\n",
|
|
"import numpy as np\n",
|
|
"from sklearn import linear_model\n",
|
|
"from sklearn.linear_model import LinearRegression\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"from sklearn.preprocessing import PolynomialFeatures\n",
|
|
"from sklearn.pipeline import Pipeline\n",
|
|
"from sklearn.neighbors import KNeighborsClassifier\n",
|
|
"\n",
|
|
"# Generate sample data\n",
|
|
"X = np.sort(5*np.random.rand(40,1), axis=0)\n",
|
|
"y = X**3\n",
|
|
"y=y.ravel()\n",
|
|
"\n",
|
|
"# Add noise to targets\n",
|
|
"X[::4] +=(0.5 - np.random.rand(1))\n",
|
|
"y[::5] +=(0.5 - np.random.rand(8))\n",
|
|
"\n",
|
|
"a=np.array(X)\n",
|
|
"b=np.array(y)\n",
|
|
"\n",
|
|
"X_train=a[:19]\n",
|
|
"X_test=a[19:]\n",
|
|
"y_train=b[:19]\n",
|
|
"y_test=b[19:]\n",
|
|
"\n",
|
|
"model=Pipeline([('poly', PolynomialFeatures(degree=3)),('linear', LinearRegression(fit_intercept=False))])\n",
|
|
"model=model.fit(X_train, y_train)\n",
|
|
"pred=model.predict(X_test)\n",
|
|
"\n",
|
|
"\n",
|
|
"poly=PolynomialFeatures(degree=3)\n",
|
|
"poly.fit_transform(X_train, y_train)\n",
|
|
"plt.scatter(X_test, y_test)\n",
|
|
"plt.plot(X_test, pred, color='green')\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"print (model.score(X_test,y_test))\n",
|
|
"\n",
|
|
"print (\"---------K-Nearest Neighbors-------\")\n",
|
|
"\"\"\"neighbors_settings=range(1,11)\n",
|
|
"for n_neighbors in neighbors_settings:\n",
|
|
" clf=KNeighborsClassifier(n_neighbors=n_neighbors)\n",
|
|
" clf.fit(X_train, y_train)\n",
|
|
" training_accuracy.append(clf.score(X_train, y_train))\n",
|
|
" test_accuracy.append(clf.score(X_test, y_test))\n",
|
|
"\n",
|
|
"\n",
|
|
"print (mglearn.plots.plot_knn_regression(n_neighbors=3))\"\"\"\n",
|
|
"\n",
|
|
"from sklearn.neighbors import KNeighborsRegressor\n",
|
|
"\n",
|
|
"X, y=mglearn.datasets.make_wave(n_samples=40)\n",
|
|
"reg = KNeighborsRegressor(n_neighbors=3)\n",
|
|
"reg.fit(X_train, y_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
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