{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitting a line to Gaussian data with known covariance matrix" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as pl\n", "import oktopus\n", "from oktopus import MultivariateGaussianLikelihood\n", "from oktopus.models import ExpSquaredKernel, WhiteNoiseKernel\n", "from matplotlib import rc\n", "rc('text', usetex=True)\n", "font = {'family' : 'serif',\n", " 'size' : 18,\n", " 'serif' : 'New Century Schoolbook'}\n", "rc('font', **font)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PHNKczLcVOFVFguuxFFwWo+ZQp2o5pIpXkUuqCXDOz8uB7JOycNRyjXOc89Oc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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0, 10, 100)\n", "def mean(m, b):\n", " return m * x + b\n", "def cov(k, l, s):\n", " return ExpSquaredKernel(x).evaluate(k, l) + WhiteNoiseKernel(len(x)).evaluate(s)\n", "np.random.seed(2)\n", "fake_data = np.random.multivariate_normal(mean(3, 10), cov(1, 1, 4))\n", "pl.plot(x, fake_data)\n", "pl.ylabel(\"y\")\n", "pl.xlabel(\"x\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "logL = MultivariateGaussianLikelihood(fake_data, mean, cov(1, 1, 4), 2)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " direc: array([[ 0. , 1. ],\n", " [-0.64464562, 3.22322808]])\n", " fun: 339.00598722690825\n", " message: 'Optimization terminated successfully.'\n", " nfev: 136\n", " nit: 3\n", " status: 0\n", " success: True\n", " x: array([ 2.96196725, 10.96020354])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "MLE = logL.fit(x0=[-2, 5], method='powell')\n", "MLE" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3MVlzobUe/nwPnHOr7FMLQ/GggjGRKNkNs9fA2TeKYBz+E6z+XKSv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\nBxWlOCJRX9q3YLgWhu2Qc3fVC5yQ+EW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.plot(x, mean(*MLE.x))\n", "pl.plot(x, fake_data)\n", "pl.ylabel(\"y\")\n", "pl.xlabel(\"x\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.18975499, 1.10638358])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logL.uncertainties(MLE.x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }