mhjensen
97 lines
9.2 KiB
Plaintext
97 lines
9.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 10,
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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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"text/plain": [
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"<matplotlib.figure.Figure at 0x1a152aa1d0>"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1 0]\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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"array([[ 0.00598012, 0.99401988],\n",
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" [ 0.00598012, 0.99401988]])"
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]
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},
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"execution_count": 10,
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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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"import numpy as np\n",
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"from sklearn.svm import SVR\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Generate sample data\n",
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"X = np.sort(5*np.random.rand(40,1), axis=0)\n",
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"y = X**3\n",
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"y=y.ravel()\n",
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"\n",
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"# Add noise to targets\n",
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"X[::4] +=3*(0.5 - np.random.rand(1))\n",
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"y[::5] += 50 * (0.5 - np.random.rand(8))\n",
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"\n",
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"plt.plot(X,y, 'g^')\n",
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"plt.show()\n",
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"\n",
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"#neural network example from scikit-learn website\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"X1=[[0., 0.], [1., 1.]]\n",
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"y1=[0,1]\n",
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"clf=MLPClassifier(solver='lbfgs', alpha=0.001, hidden_layer_sizes=(5,2), random_state=1)\n",
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"clf.fit(X1,y1)\n",
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"print (clf.predict([[2., 2.], [-1., -2.]]))\n",
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"[coef.shape for coef in clf.coefs_]\n",
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"clf.predict_proba([[2., 2.], [1., 2.]])"
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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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"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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