mhjensen
73 lines
2.2 KiB
Plaintext
73 lines
2.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<!-- dom:TITLE: Data Analysis and Machine Learning: Support Vector Machines -->\n",
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"# Data Analysis and Machine Learning: Support Vector Machines\n",
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"<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
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"<!-- Author: --> \n",
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"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
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"\n",
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"Date: **May 30, 2018**\n",
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"\n",
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"Copyright 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"## Support Vector Machines, overarching aims"
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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": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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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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"\n",
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"#SVR Fit\n",
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"svr_poly = SVR(kernel='poly', C=1e3, degree=3)\n",
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"y_poly = svr_poly.fit(X, y).predict(X)\n",
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"\n",
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"# Plots\n",
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"z = np.arange(0, 5, 0.1)\n",
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"t = z**3\n",
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"fig = plt.figure()\n",
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"ax = fig.add_subplot(111)\n",
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"plt.plot(z,z**3, 'r--', label='Cubic Function with No Noise')\n",
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"lw = 2\n",
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"plt.scatter(X, y, color='darkorange', label='Gaussian Cubic Noise')\n",
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"plt.plot(X, y_poly, color='green', lw=lw, label='Polynomial model')\n",
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"plt.xlabel('data')\n",
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"plt.ylabel('target')\n",
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"plt.title('Cubic Gaussian Distribution')\n",
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"plt.legend()\n",
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"plt.show()"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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