test

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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": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Oil data example"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def igplvm_oil_100(optimize=True, verbose=1, plot=True):\n",
      "    data = GPy.util.datasets.oil_100()\n",
      "    Y = data['X']\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 = data['Y'].argmax(axis=1)\n",
      "    print m.data_labels\n",
      "    print m.data_labels.shape\n",
      "    #m.w=np.ones(m.w.shape)\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",
      "def gplvm_oil_100(optimize=True, verbose=1, plot=True):\n",
      "    data = GPy.util.datasets.oil_100()\n",
      "    Y = data['X']\n",
      "    # create simple GP model\n",
      "    kernel = GPy.kern.rbf(2, ARD=True) + GPy.kern.bias(2)\n",
      "    m = GPLVM(Y, 2, kernel=kernel)\n",
      "    m.data_labels = data['Y'].argmax(axis=1)\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"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m3=igplvm_oil_100()\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",
      "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": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Y is not zero mean, centering it locally (GPy.util.linalg.pca)\n",
        "[2 1 0 1 0 1 0 0 1 0 1 0 1 0 0 2 2 2 2 0 1 0 0 2 2 0 1 2 1 2 1 2 2 0 0 1 1\n",
        " 1 1 1 2 1 0 1 0 0 0 2 2 1 1 0 2 0 0 0 2 2 1 0 1 0 0 2 2 0 2 0 1 2 1 1 0 0\n",
        " 1 1 0 2 2 1 1 0 0 1 1 2 2 1 2 0 2 2 0 2 2 2 0 2 0 2]\n",
        "(100,)\n",
        " I      F              Scale          |g|        \n",
        "\r",
        "0001  -3.368947e+02   7.176112e+01   3.772268e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0002  -5.233436e+02   2.870445e+02   3.772268e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "\r",
        "0003  -6.873671e+02   1.435222e+02   4.906830e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0004  -6.873671e+02   7.176112e+01   2.734096e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0005  -6.873671e+02   2.870445e+02   2.734096e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0006  -8.307876e+02   1.148178e+03   2.734096e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0007  -8.657251e+02   5.740890e+02   1.610045e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "\r",
        "0008  -8.657251e+02   5.740890e+02   2.111685e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0009  -9.048780e+02   2.296356e+03   2.111685e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0010  -9.233861e+02   1.148178e+03   1.053001e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0011  -9.370707e+02   5.740890e+02   1.359422e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0012  -9.471672e+02   5.740890e+02   1.757559e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0013  -9.471809e+02   5.740890e+02   2.132704e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0014  -9.699697e+02   2.296356e+03   3.406693e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0015  -9.906478e+02   2.296356e+03   3.061175e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0016  -1.010042e+03   2.296356e+03   2.861971e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0017  -1.019179e+03   1.148178e+03   1.326324e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0018  -1.026272e+03   5.740890e+02   1.026019e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0019  -1.032647e+03   2.870445e+02   9.712589e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0020  -1.038689e+03   1.435222e+02   1.095623e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "\r",
        "0021  -1.044125e+03   7.176112e+01   8.969955e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0022  -1.049413e+03   3.588056e+01   1.052972e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0023  -1.054183e+03   1.794028e+01   8.280098e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0024  -1.058848e+03   8.970140e+00   9.880293e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0025  -1.063117e+03   4.485070e+00   7.799302e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0026  -1.067316e+03   2.242535e+00   9.393163e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0027  -1.071227e+03   1.121267e+00   7.597122e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0028  -1.075109e+03   5.606337e-01   9.133936e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0029  -1.078798e+03   2.803169e-01   7.695712e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0030  -1.082506e+03   1.401584e-01   9.141002e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0031  -1.086109e+03   7.007922e-02   8.109735e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0032  -1.089782e+03   3.503961e-02   9.463593e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0033  -1.093426e+03   1.751980e-02   8.826547e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0034  -1.097175e+03   8.759902e-03   1.010044e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0035  -1.100939e+03   4.379951e-03   9.747340e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0036  -1.104806e+03   2.189976e-03   1.090388e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0037  -1.108680e+03   1.094988e-03   1.063631e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0038  -1.112608e+03   5.474939e-04   1.157920e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0039  -1.116492e+03   2.737469e-04   1.120749e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0040  -1.120359e+03   1.368735e-04   1.187654e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0041  -1.124126e+03   6.843674e-05   1.133527e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0042  -1.127821e+03   3.421837e-05   1.177583e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0043  -1.131383e+03   1.710918e-05   1.111238e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "0249  -1.299465e+03   1.000000e-60   3.416396e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0250  -1.299804e+03   1.000000e-60   3.825635e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0251  -1.300140e+03   1.000000e-60   3.401074e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0252  -1.300475e+03   1.000000e-60   3.825722e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0253  -1.300808e+03   1.000000e-60   3.387650e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0254  -1.301140e+03   1.000000e-60   3.823693e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0255  -1.301470e+03   1.000000e-60   3.376175e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0256  -1.301800e+03   1.000000e-60   3.819471e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0257  -1.302127e+03   1.000000e-60   3.366583e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0258  -1.302453e+03   1.000000e-60   3.813167e+04"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-71-50700ae38eb8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mm3\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0migplvm_oil_100\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mm3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog_likelihood\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mpb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meLi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mpb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"L inverse\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mpb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-70-1744296d73d9>\u001b[0m in \u001b[0;36migplvm_oil_100\u001b[1;34m(optimize, verbose, plot)\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_labels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;31m#m.w=np.ones(m.w.shape)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0moptimize\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'scg'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessages\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_f_eval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_iters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_latent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/is/ei/jmcmurr/.local/lib/python2.7/site-packages/GPy-0.4.6-py2.7.egg/GPy/core/model.pyc\u001b[0m in \u001b[0;36moptimize\u001b[1;34m(self, optimizer, start, **kwargs)\u001b[0m\n\u001b[0;32m    357\u001b[0m         \u001b[0mopt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 359\u001b[1;33m         \u001b[0mopt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf_fp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective_and_gradients\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobjective_function_gradients\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    360\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    361\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimization_runs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/is/ei/jmcmurr/.local/lib/python2.7/site-packages/GPy-0.4.6-py2.7.egg/GPy/inference/optimization.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m         \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     53\u001b[0m         \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mend\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/is/ei/jmcmurr/.local/lib/python2.7/site-packages/GPy-0.4.6-py2.7.egg/GPy/inference/optimization.pyc\u001b[0m in \u001b[0;36mopt\u001b[1;34m(self, f_fp, f, fp)\u001b[0m\n\u001b[0;32m    217\u001b[0m                          \u001b[0mmax_f_eval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_f_eval\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m                          \u001b[0mxtol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxtol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mftol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mftol\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 219\u001b[1;33m                          gtol=self.gtol)\n\u001b[0m\u001b[0;32m    220\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx_opt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopt_result\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/is/ei/jmcmurr/.local/lib/python2.7/site-packages/GPy-0.4.6-py2.7.egg/GPy/inference/scg.pyc\u001b[0m in \u001b[0;36mSCG\u001b[1;34m(f, gradf, x, optargs, maxiters, max_f_eval, display, xtol, ftol, gtol)\u001b[0m\n\u001b[0;32m     96\u001b[0m             \u001b[0msigma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msigma0\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkappa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m             \u001b[0mxplus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msigma\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 98\u001b[1;33m             \u001b[0mgplus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgradf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxplus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0moptargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     99\u001b[0m             \u001b[0mtheta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mgplus\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mgradnew\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0msigma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/is/ei/jmcmurr/.local/lib/python2.7/site-packages/GPy-0.4.6-py2.7.egg/GPy/core/model.pyc\u001b[0m in \u001b[0;36mobjective_function_gradients\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    305\u001b[0m         \"\"\"\n\u001b[0;32m    306\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 307\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_params_transformed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    308\u001b[0m             \u001b[0mobj_grads\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transform_gradients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_log_likelihood_gradients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_log_prior_gradients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    309\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fail_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/is/ei/jmcmurr/.local/lib/python2.7/site-packages/GPy-0.4.6-py2.7.egg/GPy/core/parameterized.pyc\u001b[0m in \u001b[0;36m_set_params_transformed\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    277\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set_params_transformed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m         \u001b[1;34m\"\"\" takes the vector x, which is then modified (by untying, reparameterising or inserting fixed values), and then call self._set_params\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 279\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_untransform_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    280\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_untransform_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-65-8b62a61363b1>\u001b[0m in \u001b[0;36m_set_params\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     47\u001b[0m         \u001b[1;31m#print \"test2\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_data\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_dim\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_dim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_newset_GPparams\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_newset_GPparams\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-65-8b62a61363b1>\u001b[0m in \u001b[0;36m_newset_GPparams\u001b[1;34m(self, p)\u001b[0m\n\u001b[0;32m     57\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlikelihood\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_params_transformed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_likelihood_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mK\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkern\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m         \u001b[1;31m#Re fit likelihood approximation (if it is an approx), as parameters have changed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/is/ei/jmcmurr/.local/lib/python2.7/site-packages/GPy-0.4.6-py2.7.egg/GPy/kern/kern.pyc\u001b[0m in \u001b[0;36mK\u001b[1;34m(self, X, X2, which_parts)\u001b[0m\n\u001b[0;32m    335\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mX2\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    336\u001b[0m             \u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 337\u001b[1;33m             \u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi_s\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpart_i_used\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_slices\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhich_parts\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpart_i_used\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    338\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m             \u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/is/ei/jmcmurr/.local/lib/python2.7/site-packages/GPy-0.4.6-py2.7.egg/GPy/kern/parts/rbf.pyc\u001b[0m in \u001b[0;36mK\u001b[1;34m(self, X, X2, target)\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 101\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_K_computations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    102\u001b[0m         \u001b[0mtarget\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariance\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_K_dvar\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/is/ei/jmcmurr/.local/lib/python2.7/site-packages/GPy-0.4.6-py2.7.egg/GPy/kern/parts/rbf.pyc\u001b[0m in \u001b[0;36m_K_computations\u001b[1;34m(self, X, X2)\u001b[0m\n\u001b[0;32m    262\u001b[0m                 \u001b[0mX2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX2\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlengthscale\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    263\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_K_dist2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m2.\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 264\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_K_dvar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m0.5\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_K_dist2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    266\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_psi_computations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pb.plot(m3.likelihood.Y.transpose()[8,:])\n",
      "pb.plot(m3.likelihood.Y.transpose()[9,:])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "[<matplotlib.lines.Line2D at 0x794d2d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UmH2XrN7HumoEqyYWpAKYKwuoCgrg+3/8Po5MHDE8TlMAZ70FlJ8/BaAN1d2e1/TYDhRA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\nwMDAkKFgBMDAwMCQoWAEwMDAwJCh+P9Ae6SOgqmigwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x46b9690>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m4=gplvm_oil_100()\n",
      "print m4.log_likelihood()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Y is not zero mean, centering it locally (GPy.util.linalg.pca)\n",
        " I      F              Scale          |g|        \n",
        "\r",
        "0001   6.164492e+02   1.000000e+00   1.330821e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0002   6.164492e+02   4.000000e+00   1.330821e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "\r",
        "0003   6.164492e+02   1.600000e+01   1.330821e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0004   4.993518e+02   6.400000e+01   1.330821e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0005   2.032535e+02   6.400000e+01   5.951695e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0006   8.751818e+01   3.200000e+01   6.335666e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0007   8.751818e+01   1.600000e+01   5.304522e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "\r",
        "0008  -6.312158e+01   6.400000e+01   5.304522e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0009  -2.060418e+02   3.200000e+01   2.168073e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0010  -2.060418e+02   1.600000e+01   2.605461e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0011  -2.463763e+02   6.400000e+01   2.605461e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0012  -2.939164e+02   6.400000e+01   9.386607e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0013  -3.042423e+02   6.400000e+01   6.661178e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0014  -3.590103e+02   2.560000e+02   1.403630e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0015  -3.853609e+02   1.280000e+02   6.900896e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0016  -4.145357e+02   1.280000e+02   1.182061e+05"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0017  -4.260634e+02   6.400000e+01   4.542137e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0018  -4.357954e+02   6.400000e+01   4.809633e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0019  -4.429922e+02   3.200000e+01   3.258071e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0020  -4.496035e+02   1.600000e+01   3.161051e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "\r",
        "0021  -4.561882e+02   8.000000e+00   3.152983e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0022  -4.635860e+02   4.000000e+00   3.506479e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0023  -4.732464e+02   2.000000e+00   4.737523e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0024  -4.871949e+02   1.000000e+00   7.079799e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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       "metadata": {},
       "output_type": "display_data",
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ms7l1TY1wNpvNrXeayWTyNtt7XCj8eNgzHFf/E2rC/UmEejG+15lcNXwutZEB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\n1qI4lUUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x120e6d50>"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print m3.kern\n",
      "print m4.kern"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "        Name         |  Value   |  Constraints  |  Ties  \n",
        "---------------------------------------------------------\n",
        "    rbf_variance     |  2.2757  |               |        \n",
        "  rbf_lengthscale_0  |  1.7442  |               |        \n",
        "  rbf_lengthscale_1  |  0.1373  |               |        \n",
        "    bias_variance    |  1.0675  |               |        \n",
        "\n",
        "        Name         |  Value   |  Constraints  |  Ties  \n",
        "---------------------------------------------------------\n",
        "    rbf_variance     |  0.3977  |               |        \n",
        "  rbf_lengthscale_0  |  2.0404  |               |        \n",
        "  rbf_lengthscale_1  |  0.1817  |               |        \n",
        "    bias_variance    |  0.8690  |               |        \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "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": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Y is not zero mean, centering it locally (GPy.util.linalg.pca)\n",
        " I      F              Scale          |g|        \n",
        "\r",
        "0001  -1.207870e+03   1.000000e+00   1.101045e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0002  -1.207870e+03   5.603672e+01   1.586556e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0003  -1.313213e+03   2.241469e+02   1.586556e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0004  -1.313213e+03   1.120734e+02   2.411307e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0005  -1.365419e+03   4.482938e+02   2.411307e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0006  -1.399545e+03   2.241469e+02   2.149128e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0007  -1.399545e+03   2.241469e+02   5.346311e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "\r",
        "0008  -1.446239e+03   8.965876e+02   5.346311e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0009  -1.459614e+03   4.482938e+02   2.249554e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0010  -1.469134e+03   2.241469e+02   1.081175e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0011  -1.475563e+03   1.120734e+02   1.748025e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0012  -1.480737e+03   5.603672e+01   1.308771e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0013  -1.485542e+03   2.801836e+01   1.591262e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0014  -1.489697e+03   1.400918e+01   1.333206e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0015  -1.493606e+03   7.004590e+00   1.463558e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0016  -1.497123e+03   3.502295e+00   1.284573e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0017  -1.500462e+03   1.751148e+00   1.377684e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0018  -1.503534e+03   8.755738e-01   1.240638e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0019  -1.506470e+03   4.377869e-01   1.315686e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0020  -1.509211e+03   2.188934e-01   1.207062e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0021  -1.511843e+03   1.094467e-01   1.268314e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "\r",
        "0022  -1.514326e+03   5.472336e-02   1.181070e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0023  -1.516717e+03   2.736168e-02   1.230296e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0024  -1.518991e+03   1.368084e-02   1.159706e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0025  -1.521186e+03   6.840420e-03   1.199190e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0026  -1.523285e+03   3.420210e-03   1.141578e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0027  -1.525316e+03   1.710105e-03   1.173084e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0028  -1.527268e+03   8.550525e-04   1.125820e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0029  -1.529160e+03   4.275263e-04   1.150564e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0030  -1.530984e+03   2.137631e-04   1.111735e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0031  -1.532755e+03   1.068816e-04   1.131201e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0032  -1.534468e+03   5.344078e-05   1.099209e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0033  -1.536134e+03   2.672039e-05   1.113945e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0034  -1.537749e+03   1.336020e-05   1.087741e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0035  -1.539322e+03   6.680098e-06   1.098669e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0036  -1.540849e+03   3.340049e-06   1.077260e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0037  -1.542339e+03   1.670024e-06   1.084951e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0038  -1.543788e+03   8.350122e-07   1.067525e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0039  -1.545203e+03   4.175061e-07   1.072457e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0040  -1.546583e+03   2.087531e-07   1.058669e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0041  -1.547930e+03   1.043765e-07   1.060870e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0042  -1.549246e+03   5.218826e-08   1.050402e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0043  -1.550532e+03   2.609413e-08   1.050453e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0044  -1.551789e+03   1.304707e-08   1.042647e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0045  -1.553019e+03   6.523533e-09   1.040770e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0046  -1.554224e+03   3.261767e-09   1.035390e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0047  -1.555403e+03   1.630883e-09   1.031851e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0048  -1.556558e+03   8.154416e-10   1.028320e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0049  -1.557690e+03   4.077208e-10   1.023667e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0050  -1.558801e+03   2.038604e-10   1.021910e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0051  -1.559889e+03   1.019302e-10   1.015846e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0052  -1.560958e+03   5.096510e-11   1.015854e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0053  -1.562007e+03   2.548255e-11   1.008505e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0054  -1.563037e+03   1.274128e-11   1.010185e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0055  -1.564048e+03   6.370638e-12   1.001751e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0056  -1.565042e+03   3.185319e-12   1.004622e+04"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0057  -1.566019e+03   1.592659e-12   9.953333e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "\r",
        "0058  -1.566979e+03   7.963297e-13   9.993268e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0059  -1.567923e+03   3.981649e-13   9.893857e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0060  -1.568853e+03   1.990824e-13   9.943982e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0061  -1.569767e+03   9.954122e-14   9.835608e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0062  -1.570667e+03   4.977061e-14   9.896279e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0063  -1.571552e+03   2.488530e-14   9.783441e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0064  -1.572425e+03   1.244265e-14   9.848769e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0065  -1.573284e+03   6.221326e-15   9.731951e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0066  -1.574130e+03   3.110663e-15   9.805865e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0067  -1.574964e+03   1.555331e-15   9.682356e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0068  -1.575787e+03   7.776657e-16   9.763270e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0069  -1.576597e+03   3.888329e-16   9.636617e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0070  -1.577396e+03   1.944164e-16   9.724062e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0071  -1.578184e+03   9.720822e-17   9.591191e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0072  -1.578962e+03   4.860411e-17   9.685467e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0073  -1.579729e+03   2.430205e-17   9.549395e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0074  -1.580486e+03   1.215103e-17   9.647263e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0075  -1.581233e+03   6.075514e-18   9.509890e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0076  -1.581971e+03   3.037757e-18   9.610781e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0077  -1.582699e+03   1.518878e-18   9.472048e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0078  -1.583418e+03   7.594392e-19   9.575122e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0079  -1.584128e+03   3.797196e-19   9.434739e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0080  -1.584830e+03   1.898598e-19   9.542284e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0081  -1.585523e+03   9.492990e-20   9.399036e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0082  -1.586209e+03   4.746495e-20   9.509488e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0083  -1.586885e+03   2.373248e-20   9.365522e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0084  -1.587555e+03   1.186624e-20   9.477726e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0085  -1.588216e+03   5.933119e-21   9.332595e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0086  -1.588870e+03   2.966559e-21   9.447156e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0087  -1.589517e+03   1.483280e-21   9.301439e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0088  -1.590157e+03   7.416398e-22   9.416234e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0089  -1.590789e+03   3.708199e-22   9.272248e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0090  -1.591415e+03   1.854100e-22   9.386860e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0091  -1.592034e+03   9.270498e-23   9.242935e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0092  -1.592647e+03   4.635249e-23   9.359977e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0093  -1.593253e+03   2.317625e-23   9.214804e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0094  -1.593853e+03   1.158812e-23   9.330903e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0095  -1.594446e+03   5.794061e-24   9.188474e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0096  -1.595034e+03   2.897031e-24   9.305015e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0097  -1.595616e+03   1.448515e-24   9.162552e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "0098  -1.596193e+03   7.242577e-25   9.277485e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "1975  -1.778030e+03   1.000000e-60   7.341105e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1976  -1.778063e+03   1.000000e-60   7.282336e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1977  -1.778096e+03   1.000000e-60   7.341443e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1978  -1.778130e+03   1.000000e-60   7.282171e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1979  -1.778163e+03   1.000000e-60   7.340401e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1980  -1.778196e+03   1.000000e-60   7.282085e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1981  -1.778229e+03   1.000000e-60   7.339706e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1982  -1.778262e+03   1.000000e-60   7.281254e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1983  -1.778295e+03   1.000000e-60   7.339611e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1984  -1.778328e+03   1.000000e-60   7.280760e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1985  -1.778361e+03   1.000000e-60   7.338959e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1986  -1.778394e+03   1.000000e-60   7.280302e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1987  -1.778427e+03   1.000000e-60   7.338461e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1988  -1.778460e+03   1.000000e-60   7.280161e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1989  -1.778493e+03   1.000000e-60   7.337638e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1990  -1.778526e+03   1.000000e-60   7.279451e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1991  -1.778559e+03   1.000000e-60   7.337526e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1992  -1.778592e+03   1.000000e-60   7.279034e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1993  -1.778625e+03   1.000000e-60   7.337241e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1994  -1.778658e+03   1.000000e-60   7.278682e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1995  -1.778691e+03   1.000000e-60   7.337049e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1996  -1.778723e+03   1.000000e-60   7.277865e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1997  -1.778756e+03   1.000000e-60   7.336366e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1998  -1.778789e+03   1.000000e-60   7.277114e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "1999  -1.778822e+03   1.000000e-60   7.336229e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "2000  -1.778855e+03   1.000000e-60   7.276644e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \r",
        "2000  -1.778855e+03   1.000000e-60   7.336464e+03"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " \n",
        "maxiter exceeded\n",
        "2032.7844429"
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       "text": [
        "\n",
        "(100, 8)"
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n5Smo79BipHpJ0JDn7oAUtEb0pJdNvRzyWxpoiX7o+NkcBKCJk54QRZtR0m4K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\nF82hIWowYrtps/k4RN2y7aZbZSfZa6rsTaElf/SKS34cLL2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