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Created March 10, 2014 15:11
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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Invariant GPLVM implementation attempt\n",
"===================================\n",
"\n",
"Load Python modules:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"import GPy\n",
"import numpy as np\n",
"import matplotlib.pyplot as pb\n",
"import string \n",
"from load_plotting import *\n",
"import pca\n",
"\n",
"pb.ion()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"InvariantGPLVM code used:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pylab as pb\n",
"from GPy import kern\n",
"from GPy.core import priors\n",
"from GPy.core import GP\n",
"from GPy.models import GPLVM\n",
"from GPy.likelihoods import Gaussian\n",
"from GPy import util\n",
"from GPy.util.linalg import pca\n",
"from GPy.inference import optimization\n",
"from GPy.util.linalg import pdinv, mdot, tdot, dpotrs, dtrtrs, jitchol, chol_inv\n",
"from GPy.likelihoods import EP, Laplace\n",
"\n",
"class InvariantGPLVM(GPLVM):\n",
" \"\"\"\n",
" Invariant Gaussian Process Latent Variable Model\n",
" \n",
" TODO description here\n",
"\n",
" :param Y: observed data\n",
" :type Y: np.ndarray\n",
" :param input_dim: latent dimensionality\n",
" :type input_dim: int\n",
" :param init: initialisation method for the latent space\n",
" :type init: 'PCA'|'random'\n",
"\n",
" \"\"\"\n",
" def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False):\n",
" if X is None:\n",
" X = self.initialise_latent(init, input_dim, Y)\n",
" if kernel is None:\n",
" kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2))\n",
" likelihood = Gaussian(Y, normalize=normalize_Y, variance=np.exp(-2.))\n",
" GP.__init__(self, X, likelihood, kernel, normalize_X=False)\n",
" self.eLi=chol_inv(jitchol(((1./Y.shape[0])*(np.dot(Y.transpose(), np.dot(self.Ki, Y))))+1E-6))\n",
" self.set_prior('.*X', priors.Gaussian(0, 1))\n",
" self.ensure_default_constraints()\n",
"\n",
" def _get_param_names(self):\n",
" return __builtin__.sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + GP._get_param_names(self)\n",
"\n",
" def _get_params(self):\n",
" #print \"test3\"\n",
" return np.hstack((self.X.flatten(), GP._get_params(self))) \n",
" \n",
" def _set_params(self, x):\n",
" #print \"test2\"\n",
" self.X = x[:self.num_data * self.input_dim].reshape(self.num_data, self.input_dim).copy()\n",
" self._newset_GPparams(x[self.X.size:])\n",
"\n",
" def _newset_GPparams(self, p):\n",
" new_kern_params = p[:self.kern.num_params_transformed()]\n",
" new_likelihood_params = p[self.kern.num_params_transformed():]\n",
" old_likelihood_params = self.likelihood._get_params()\n",
"\n",
" self.kern._set_params_transformed(new_kern_params)\n",
" self.likelihood._set_params_transformed(new_likelihood_params)\n",
"\n",
" self.K = self.kern.K(self.X)\n",
"\n",
" #Re fit likelihood approximation (if it is an approx), as parameters have changed\n",
" if isinstance(self.likelihood, Laplace):\n",
" self.likelihood.fit_full(self.K)\n",
"\n",
" self.K += self.likelihood.covariance_matrix\n",
"\n",
" self.Ki, self.L, self.Li, self.K_logdet = pdinv(self.K)\n",
" #print np.diag((1./self.likelihood.Y.shape[0])*(np.dot(self.likelihood.Y.transpose(), np.dot(self.Ki, self.likelihood.Y))))\n",
" self.eLi=chol_inv(jitchol(((1./self.likelihood.Y.shape[0])*(np.dot(self.likelihood.Y.transpose(), np.dot(self.Ki, self.likelihood.Y))))+1E-6))\n",
" # the gradient of the likelihood wrt the covariance matrix\n",
" if self.likelihood.YYT is None:\n",
" # alpha = np.dot(self.Ki, self.likelihood.Y)\n",
" #alpha, _ = dpotrs(self.L, self.likelihood.Y, lower=1)\n",
" #Replacing Y with Y having its columns multiplied by values in w\n",
" alpha, _ = dpotrs(self.L, np.dot(self.eLi, self.likelihood.Y.transpose()).transpose(), lower=1)\n",
"\n",
" self.dL_dK = 0.5 * (tdot(alpha) - self.output_dim * self.Ki)\n",
" else:\n",
" # tmp = mdot(self.Ki, self.likelihood.YYT, self.Ki)\n",
" print \"TODO\"\n",
" tmp, _ = dpotrs(self.L, np.asfortranarray(self.likelihood.YYT), lower=1)\n",
" tmp, _ = dpotrs(self.L, np.asfortranarray(tmp.T), lower=1)\n",
" self.dL_dK = 0.5 * (tmp - self.output_dim * self.Ki)\n",
"\n",
" #Adding dZ_dK (0 for a non-approximate likelihood, compensates for\n",
" #additional gradients of K when log-likelihood has non-zero Z term)\n",
" self.dL_dK += self.likelihood.dZ_dK\n",
"\n",
"\n",
" def initialise_latent(self, init, input_dim, Y):\n",
" Xr = np.random.randn(Y.shape[0], input_dim)\n",
" if init.lower() == 'pca':\n",
" PC = pca(Y, input_dim)[0]\n",
" Xr[:PC.shape[0], :PC.shape[1]] = PC\n",
" return Xr\n",
" \n",
" \n",
" def log_likelihood(self):\n",
" \"\"\"\n",
" The log marginal likelihood of the GP.\n",
"\n",
" For an EP model, can be written as the log likelihood of a regression\n",
" model for a new variable Y* = v_tilde/tau_tilde, with a covariance\n",
" matrix K* = K + diag(1./tau_tilde) plus a normalization term.\n",
" \"\"\"\n",
" l=( -self.num_data*np.log(np.linalg.det(np.linalg.inv(self.eLi)))-0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) - 0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z)\n",
" #print l\n",
" return l\n",
"\n",
" def _log_likelihood_gradients(self):\n",
" #print \"test1\"\n",
" dL_dX = self.kern.dK_dX(self.dL_dK, self.X)\n",
" #print dL_dX.flatten()\n",
" return np.hstack((dL_dX.flatten(), GP._log_likelihood_gradients(self)))\n",
"\n",
" def _model_fit_term(self):\n",
" \"\"\"\n",
" Computes the model fit using YYT if it's available\n",
" \"\"\"\n",
" eLiY=np.dot(self.eLi, self.likelihood.Y.transpose())\n",
" return -0.5*np.trace(np.dot(eLiY, np.dot(self.Ki, eLiY.transpose())))\n",
" \n",
" #if self.likelihood.YYT is None:\n",
" # tmp, _ = dtrtrs(self.L, np.asfortranarray(self.likelihood.Y), lower=1)\n",
" # return -0.5 * np.sum(np.square(tmp))\n",
" # # return -0.5 * np.sum(np.square(np.dot(self.Li, self.likelihood.Y)))\n",
" #else:\n",
" # return -0.5 * np.sum(np.multiply(self.Ki, self.likelihood.YYT))\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Implementation of Invariant GPLVM\n",
"\n",
"http://arxiv.org/pdf/1203.3534v1.pdf\n",
"\n",
"Approach I: \n",
"\n",
"\n",
"Where $L^{-1} = \\tilde{L} $ in the paper is _self.eLi_ here."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def scale(d):\n",
" return (d-np.min(d))/(np.max(d) - np.min(d))\n",
"\n",
"def igplvm_weather(optimize=True, verbose=1, plot=True):\n",
" f=open(\"lufttemeraptur.txt\",\"r\")\n",
" Yluft=[]\n",
" for line in f:\n",
" if not line[0]==\"#\":\n",
" line=line.replace(\"\\r\",\"\")\n",
" line=line.replace(\"\\n\",\"\")\n",
" l=line.split(\" \")\n",
" l=filter(lambda a: a != '', l)\n",
" if len(l)==7:\n",
" Yluft.append(l[1:])\n",
" Yluft=np.array(Yluft)\n",
" Yluft=Yluft[2519:]\n",
" \n",
" f=open(\"niederschlag.txt\",\"r\")\n",
" Yschlag=[]\n",
" for line in f:\n",
" if not line[0]==\"#\":\n",
" line=line.replace(\"\\r\",\"\")\n",
" line=line.replace(\"\\n\",\"\")\n",
" l=line.split(\" \")\n",
" l=filter(lambda a: a != '', l)\n",
" if len(l)==2:\n",
" Yschlag.append(l[1:])\n",
" Yschlag=np.array(Yschlag)\n",
" Yschlag=Yschlag[2519:]\n",
" \n",
" f=open(\"globalstrahlung.txt\",\"r\")\n",
" Ystrahlung=[]\n",
" for line in f:\n",
" if not line[0]==\"#\":\n",
" line=line.replace(\"\\r\",\"\")\n",
" line=line.replace(\"\\n\",\"\")\n",
" l=line.split(\" \")\n",
" l=filter(lambda a: a != '', l)\n",
" if len(l)==2:\n",
" Ystrahlung.append(l[1:])\n",
" \n",
" Y=np.hstack([Yluft, Yschlag, Ystrahlung])\n",
" Y=Y.astype(float)\n",
" Y=Y[-100:,:]\n",
"\n",
" for d in range(Y.shape[1]):\n",
" Y[:, d]=scale(Y[:,d])\n",
" del Yluft\n",
" del Yschlag\n",
" del Ystrahlung\n",
" # create simple GP model\n",
" kernel = GPy.kern.rbf(2, ARD=True) + GPy.kern.bias(2)\n",
" m = InvariantGPLVM(Y, 2, kernel=kernel)\n",
" m.data_labels = np.ones(Y.shape[0])\n",
" if optimize: m.optimize('scg', messages=verbose, max_f_eval=2000, max_iters=2000)\n",
" if plot: m.plot_latent(labels=m.data_labels)\n",
" return m\n",
"\n",
"\n",
"def gplvm_weather(optimize=True, verbose=1, plot=True):\n",
" f=open(\"lufttemeraptur.txt\",\"r\")\n",
" Yluft=[]\n",
" for line in f:\n",
" if not line[0]==\"#\":\n",
" line=line.replace(\"\\r\",\"\")\n",
" line=line.replace(\"\\n\",\"\")\n",
" l=line.split(\" \")\n",
" l=filter(lambda a: a != '', l)\n",
" if len(l)==7:\n",
" Yluft.append(l[1:])\n",
" Yluft=np.array(Yluft)\n",
" Yluft=Yluft[2519:]\n",
" \n",
" f=open(\"niederschlag.txt\",\"r\")\n",
" Yschlag=[]\n",
" for line in f:\n",
" if not line[0]==\"#\":\n",
" line=line.replace(\"\\r\",\"\")\n",
" line=line.replace(\"\\n\",\"\")\n",
" l=line.split(\" \")\n",
" l=filter(lambda a: a != '', l)\n",
" if len(l)==2:\n",
" Yschlag.append(l[1:])\n",
" Yschlag=np.array(Yschlag)\n",
" Yschlag=Yschlag[2519:]\n",
" \n",
" f=open(\"globalstrahlung.txt\",\"r\")\n",
" Ystrahlung=[]\n",
" for line in f:\n",
" if not line[0]==\"#\":\n",
" line=line.replace(\"\\r\",\"\")\n",
" line=line.replace(\"\\n\",\"\")\n",
" l=line.split(\" \")\n",
" l=filter(lambda a: a != '', l)\n",
" if len(l)==2:\n",
" Ystrahlung.append(l[1:])\n",
" \n",
" Y=np.hstack([Yluft, Yschlag, Ystrahlung])\n",
" Y=Y.astype(float)\n",
" Y=Y[-100:,:]\n",
"\n",
" for d in range(Y.shape[1]):\n",
" Y[:, d]=scale(Y[:,d])\n",
" del Yluft\n",
" del Yschlag\n",
" del Ystrahlung\n",
" # create simple GP model\n",
" kernel = GPy.kern.rbf(2, ARD=True) + GPy.kern.bias(2)\n",
" m = GPy.models.GPLVM(Y, 2, kernel=kernel)\n",
" m.data_labels = np.ones(Y.shape[0])\n",
" if optimize: m.optimize('scg', messages=verbose, max_f_eval=2000, max_iters=2000)\n",
" if plot: m.plot_latent(labels=m.data_labels)\n",
" return m\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m3=igplvm_weather()\n",
"print m3.log_likelihood()\n",
"pb.matshow(m3.eLi)\n",
"pb.title(\"L inverse\")\n",
"pb.colorbar()\n",
"\n",
"pb.figure()\n",
"eL = np.linalg.inv(m3.eLi)\n",
"pb.matshow(eL)\n",
"pb.title(\"L\")\n",
"pb.colorbar()\n",
"\n",
"pb.figure()\n",
"L=np.dot(eL, eL.transpose())\n",
"pb.matshow(L)\n",
"pb.title(\"cov(e)\")\n",
"pb.colorbar()\n",
"\n",
"prE=np.linalg.inv(L)\n",
"pb.matshow(prE)\n",
"\n",
"pb.title(\"precision(e)\")\n",
"pb.colorbar()\n",
"np.set_printoptions(threshold='nan')\n",
"#print prE\n",
"\n",
"\n",
"def threshold(x):\n",
" if x>=2000:\n",
" r=1\n",
" elif x<=-2000:\n",
" r=-1\n",
" else:\n",
" r=0\n",
" return r\n",
"\n",
"tv=np.vectorize(threshold)\n",
"sparseprE=prE.copy()\n",
"sparseprE=tv(sparseprE)\n",
"pb.matshow(sparseprE)\n",
"\n",
"pb.title(\"sparse precision(e)\")\n",
"pb.colorbar()\n",
"np.set_printoptions(threshold='nan')\n",
"#print sparseprE\n",
"dc=np.cov(m3.likelihood.Y.transpose())\n",
"pb.matshow(dc)\n",
"pb.title(\"cov(Y)\")\n",
"pb.colorbar()\n",
"\n",
"print m3.likelihood.Y.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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n5Smo79BipHpJ0JDn7oAUtEb0pJdNvRzyWxpoiX7o+NkcBKCJk54QRZtR0m4K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\n",
"text": [
"<matplotlib.figure.Figure at 0x598ded0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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SdovPW3aJ7GTPsvJeXl5obm5WMBtp2OIT2Wk4tJV8Oo/IBbHwiVwQC5/IBbHw\niVwQC5/IBbHwiVzQ/wFtwE1db2vLxQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x5b45790>"
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x3d8e9d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x597bb10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x3718a50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x3bd2dd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Y6PTinEJ4kpZm72qMazpbT1ihRwhXamn2rksPu8v163Q67r//fqZNm8Zbb73V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\nCGGXJAohhF2SKIQQdv0/wSbABM2AhoUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x5b4a810>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ji9hUl9WGoFZDLpcjPDycVd+U7IRw0cry6CI21WXbJCYmQiaTsf4djJTshHDR\nxPLoIjbVZQFAqVQiNTUVq1evZr2dnfeY/W+Mim8XVkMN8VVTteYKsGal76pdoACuKDh3y7a67Hvv\nvYdPP/0UtbW1rPumCTpCuNCX7LLQJ0ebbzqXF+dbXfb48eMYMGAA5HI5FAoF65Ap2QnhgsfSG9/q\nstnZ2Th27BhSU1PR1NSE2tpaLF++HAcPHjR4XhqzE8JFC8uji9hUl42Pj0dZWRlKSkqQkpKCadOm\nGU10gJKdEG7MtPTGprrs09jOxksYHpUpJBIJ/kYTPMQKfCRJYD2rLZFIgAMs02aFRDDFX2jMTggX\nHJbVnjVKdkK4EOHeeKNj9pqaGkRGRsLf3x8ymQw5OTmWiIsQYTPTDjpzMnplf/fddzFnzhz85z//\nQWtrKxoaGiwRFyHCJrBEZsNgsv/+++/IzMzULgXY2dnBycnJIoERImgifOrNYLKXlJTA1dUVsbGx\nuHz5MsaMGYPExEQ4ODho25zZkqn9enDooA5F/AgRKr7VZbksqz1rBpfecnNzMWHCBGRnZ2PcuHFY\nu3Yt+vTpgw8//PDJh2npjViJLi+9vc9yOe3/CWfpzeAEnVQqhVQqxbhx4wAAkZGRyMvLs0hghAia\nCCfoDCa7u7s7vLy8UFxcDADIyMhAQECARQIjRNDMtF3WnIzOxn/++ed49dVXoVKp4OPjg6SkJEvE\nRYiwiXDMbjTZg4KCcOHCBUvEQoh4COwWnQ3aQUcIF5TshHQTAhuPs0GPuBLCRTPLo4vYVpflso2d\nkp0QLp5xddm2bexFRUUoKCiAv7+/0b4p2QnhwkxLb2yqy7ZtY1+5ciUA9tvYacxOCBf6lt4eKIAq\nBedu2VSXZbONXRdKdkK40HeL7hz65GhzzfTVZVtbW5GXl4ddu3Zpt7Fv27ZNu41dH0p2Qrh4htVl\ndW1jN/SbY9rQmJ0QLp5hdVmu29gp2QnhwkxLb2yry7ZtYw8KCkJBQQE2bdpktG+qLksIODziOoFl\n2vyPcB5xpTE7IVyIcAcdJTshXIjwqTeDY/br169DLpdrDycnJ+zcudNSsREiXCIsXmHwyj5ixAjk\n5+cDADQaDTw9PbFw4UKLBEaIoAkskdlgfRufkZEBHx8feHl5mTMeQsTBmsfsKSkpWLZsWafvU3VZ\nIka8q8tyWFZ71lgtvalUKnh6eqKwsBCurq5/fJiW3oiV6PLSmwfL5bRKkS29nThxAmPGjOmQ6IR0\na9Z6G59hmLXbAAADhklEQVScnIylS5eaOxZCxMPalt4AoKGhARkZGVi0aJEl4iFEHKxt6Q0Ann/+\neVRVVVkiFkLEQ2CJzAbtoCOECxGO2S361NsdxR3q18x9i61fc/ZtzpjFeBtv4WTnsa5pRf2as2+x\n9WvOvs0ZsxjR8+yECAjbUtIJCQkICAhAYGAgli1bhuZm47t8KNkJERA2paRLS0vx5ZdfIi8vD1eu\nXIFarUZKSorxzhkeANBBh9UcXft3r9JzpDNPfnl729G1FBsxYgRz7949hmEYprKykhkxYkSnNg8f\nPmSGDx/OPHr0iGlpaWHmzZvHpKenG+2bV6UaQrqjJxVfG1m2dujSdllnZ2dUV1cDABiGgYuLi/Z1\ne3v37sW6devQq1cvzJw5E1999ZXRvmnpjRBOuK+98S0lfevWLezYsQOlpaVwcnLCkiVL8PXXX+PV\nV181eF5KdkI4ecz5k3xLSefm5uLFF19Ev379AACLFi1Cdna20WSnCTpCODFPLWk2paT9/PyQk5OD\nx48fg2EYZGRkQCaTGe2bxuyEdNGTW+sSlq2HdGnM/ujRI7zyyiu4e/cuvL298e2336Jv376oqKhA\nXFwcfvrpJwDA9u3bceDAAdjY2GD06NH417/+BXt7e8NxU7IT0jVPkr2YZevh4nqenRDyNIHthWWB\nkp0QTsT3JAwlOyGccJ+Nf1Yo2QnhhG7jCekm6DaekG6CruyEdBN0ZSekm6ArOyHdBF3ZCekmaOmN\nkG6CruyEdBM0ZiekmxDflZ2eZyeEE/MUjv/uu+8QEBAAW1tb5OXl6W2XlpYGPz8/DBs2DJ988gmr\nvinZCeHEPMUrAgMDceTIEUyePFlvG7VajbfffhtpaWkoLCxEcnIyioqKjPZNt/GEcGKeMbufn5/R\nNufPn4evry+8vb0BAFFRUTh69Cj8/f0Nfo6SnRBO1rNq5ejoaPIzl5eXw8vLS/taKpXi3LlzRj9H\nyU5IF/GtPKOvumx8fDzCw8ONfl5XxVk2KNkJsTBD1WXZ8PT0RFlZmfZ1WVkZpFKp0c/RBB0hAqXv\nDmLs2LG4ceMGSktLoVKpcPjwYURERBjtj5KdEAE5cuQIvLy8kJOTg7lz52L27NkAgIqKCsydOxcA\nYGdnh127dmHmzJmQyWT405/+ZHRyDqDqsoR0G3RlJ6SboGQnpJugZCekm6BkJ6SboGQnpJugZCek\nm/j/Q7iquZvVqDwAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x6137290>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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MAkSyaFMT6LrU+PLly7FnT89Fe9wtOz5hwgSMHz/eq89knwCRLK5+y38O4Kzs\nqPX19TAYDAAAg8GA+vr6HudIWbLcG0wCRLK4Gh2Y+MPR6T97nJGamorr16/3+Pvf/e533b4WBKHX\nCXlqT9JjEiCSRf5koYMHD7r8nsFgwPXr1xEZGYm6ujqMGDGixzlSliz3BvsEiGTRpmNw3rx52Llz\nJwBg586dWLCg54K7Upcdlzqjl0mASBZtOgZzc3Nx8OBBjB8/HocOHUJubi4AoLa2Funp6QC6Lztu\nMpnwzDPPOJcd/+STTxAVFYVTp04hPT0daWlpHj9T0QtERIGoo01eJPHsOX3+HRv2CRDJwheIiAKc\n/0wbZhIgkoUvEBEFONYEiAIc+wSIAhxrAkQBjjUBogDHmgBRgPOfmgBnDBJ5yZu3+IYOHYqbN29q\nWBrlWBMg8pK//d7kC0REAY5JgCjAMQkQBTgmAaIAxyRAFOCYBIgC3P8HOthwIsjJgB0AAAAASUVO\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x61372d0>"
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print m3.kern\n",
"print m3.kern['bias_variance']\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Name | Value | Constraints | Ties \n",
"---------------------------------------------------------\n",
" rbf_variance | 3.2806 | | \n",
" rbf_lengthscale_0 | 3.2610 | | \n",
" rbf_lengthscale_1 | 3.5655 | | \n",
" bias_variance | 1.4233 | | \n",
"\n",
"[ 1.42329028]\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Copy matlab code here\n",
"#DD = 50; % 14\n",
"#K_pure = K - eye(size(X, 1))*1/theta(end);\n",
"#Y_I = Y * w';\n",
"#for i=1:DD alpha{i} = invK * Y_I(:,i); end\n",
"#for i=1:DD Y_I_hat(:,i) = K_pure*alpha{i}; end\n",
"#Y_hat = Y_I_hat * inv(w');\n",
"#Error = Y - Y_hat;\n",
"#noise_cov = Error'*Error/N;\n",
"#---\n",
"import scipy.stats as stats\n",
"#print m3.K.shape\n",
"K_pure=m3.K - (np.eye(m3.K.shape[0])/ m3.kern['bias_variance'])\n",
"#print m3.likelihood.Y.shape\n",
"#print eL.shape\n",
"YI=np.dot(m3.likelihood.Y, eL.transpose()) #hopefully this tranpose is right\n",
"print YI.shape\n",
"#print m3.Ki.shape\n",
"#print YI[:,1].shape\n",
"alpha=[]\n",
"for i in range(YI.shape[1]):\n",
" alpha.append(np.dot(m3.Ki, YI[:,i]))\n",
"YIhat=[]\n",
"for i in range(YI.shape[1]):\n",
" YIhat.append(np.dot(K_pure, alpha[i]))\n",
"YIhat=np.vstack(YIhat).transpose() #hopefully right?\n",
"print YIhat.shape\n",
"Yhat = np.dot(YIhat, np.linalg.inv(eL))\n",
"error = m3.likelihood.Y - Yhat\n",
"matshow(error)\n",
"pb.title(\"error matrix\")\n",
"\n",
"pb.colorbar()\n",
"\n",
"noisecov=np.dot(error.transpose(), error)/100.\n",
"matshow(noisecov)\n",
"pb.title(\"Noise covariance estimate?\")\n",
"pb.colorbar()\n",
"\n",
"\n",
"pb.figure()\n",
"hist(error[:,2])\n",
"pb.figure()\n",
"stats.probplot(error[:,2], dist=\"norm\", plot=pylab)\n",
"pylab.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(100, 8)\n",
"(100, 8)\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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SIgGThEjAJCESMEmIBAGdTJym/l6MC/HTcnA7vI+LMZqWg3tIw3Jw73E5uL6ElYRIwCQh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\nMBIJvAGF6wW7n8ghqAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x379e7d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x3d598d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x452a6d0>"
]
},
{
"metadata": {},
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KVTHbhoXj4+BjkniFqI5Kk9GHH35o8D4zM5PAwMAaC8hUjhw5gk6nw9HREYDx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\nsEs7YJkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x4584390>"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import scipy.io\n",
"scipy.io.savemat('errmat.mat', mdict={'error': error})\n",
"scipy.io.savemat('Ymat.mat', mdict={'ymat': m3.likelihood.Y})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m4=gplvm_weather()\n",
"print m4.log_likelihood()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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jZ0HTNPzu5iEYM6QDvl62F6FQGKMHtcf5vQuZ0x2q5ScnuZGc5I6x//jyHnjl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"text": [
"<matplotlib.figure.Figure at 0x91798d0>"
]
}
],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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