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https://github.com/SheffieldML/GPy.git
synced 2026-05-13 05:52:38 +02:00
Dont call parameters_changed ever yourself anymore and parameters are now inplace once in memory
This commit is contained in:
Max Zwiessele
parent
56d749ded8
commit
0df263956f
21 changed files with 601 additions and 284 deletions
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@ -1,14 +1,17 @@
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# ## Copyright (c) 2013, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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from GPy.core import Model
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from GPy.core import SparseGP
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from GPy.util.linalg import PCA
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import numpy
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import numpy as np
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import itertools
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import pylab
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from GPy.kern import Kern
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from GPy.models.bayesian_gplvm import BayesianGPLVM
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from ..core import Model, SparseGP
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from ..util.linalg import PCA
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from ..kern import Kern
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from bayesian_gplvm import BayesianGPLVM
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from ..core.parameterization.variational import NormalPosterior, NormalPrior
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from ..inference.latent_function_inference.var_dtc import VarDTCMissingData
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from ..likelihoods.gaussian import Gaussian
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class MRD2(Model):
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"""
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@ -20,11 +23,101 @@ class MRD2(Model):
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to match up, whereas the dimensionality p_d can differ.
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:param [array-like] Ylist: List of datasets to apply MRD on
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:param array-like q_mean: mean of starting latent space q in [n x q]
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:param array-like q_variance: variance of starting latent space q in [n x q]
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:param :class:`~GPy.inference.latent_function_inference
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:param input_dim: latent dimensionality
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:type input_dim: int
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:param array-like X: mean of starting latent space q in [n x q]
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:param array-like X_variance: variance of starting latent space q in [n x q]
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:param initx: initialisation method for the latent space :
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* 'concat' - PCA on concatenation of all datasets
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* 'single' - Concatenation of PCA on datasets, respectively
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* 'random' - Random draw from a Normal(0,1)
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:type initx: ['concat'|'single'|'random']
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:param initz: initialisation method for inducing inputs
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:type initz: 'permute'|'random'
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:param num_inducing: number of inducing inputs to use
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:param Z: initial inducing inputs
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:param kernel: list of kernels or kernel to copy for each output
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:type kernel: [GPy.kern.kern] | GPy.kern.kern | None (default)
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:param :class:`~GPy.inference.latent_function_inference inference_method: the inference method to use
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:param :class:`~GPy.likelihoods.likelihood.Likelihood` likelihood: the likelihood to use
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:param str name: the name of this model
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"""
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def __init__(self, Ylist, input_dim, X=None, X_variance=None,
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initx = 'PCA', initz = 'permute',
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num_inducing=10, Z=None, kernel=None,
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inference_method=None, likelihood=None, name='mrd'):
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super(MRD2, self).__init__(name)
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# sort out the kernels
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if kernel is None:
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from ..kern import RBF
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self.kern = [RBF(input_dim, ARD=1, name='Y_{}'.format(i)) for i in range(len(Ylist))]
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elif isinstance(kernel, Kern):
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self.kern = [kernel.copy(name='Y_{}'.format(i)) for i in range(len(Ylist))]
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else:
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assert len(kernel) == len(Ylist), "need one kernel per output"
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assert all([isinstance(k, Kern) for k in kernel]), "invalid kernel object detected!"
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self.input_dim = input_dim
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self.num_inducing = num_inducing
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self._in_init_ = True
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X = self._init_X(initx, Ylist)
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self.Z = self._init_Z(initz, X)
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self.num_inducing = self.Z.shape[0] # ensure M==N if M>N
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if X_variance is None:
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X_variance = np.random.uniform(0,.2,X.shape)
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self.variational_prior = NormalPrior()
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self.X = NormalPosterior(X, X_variance)
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if likelihood is None:
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likelihood = Gaussian()
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if inference_method is None:
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if any(np.any(np.isnan(y)) for y in Ylist):
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self.inference_method = VarDTCMissingData(limit=len(Ylist))
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self.Ylist = Ylist
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def parameters_changed(self):
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for y in self.Ylist:
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pass
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def _init_X(self, init='PCA', likelihood_list=None):
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if likelihood_list is None:
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likelihood_list = self.likelihood_list
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Ylist = []
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for likelihood_or_Y in likelihood_list:
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if type(likelihood_or_Y) is np.ndarray:
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Ylist.append(likelihood_or_Y)
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else:
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Ylist.append(likelihood_or_Y.Y)
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del likelihood_list
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if init in "PCA_concat":
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X = PCA(np.hstack(Ylist), self.input_dim)[0]
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elif init in "PCA_single":
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X = np.zeros((Ylist[0].shape[0], self.input_dim))
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for qs, Y in itertools.izip(np.array_split(np.arange(self.input_dim), len(Ylist)), Ylist):
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X[:, qs] = PCA(Y, len(qs))[0]
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else: # init == 'random':
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X = np.random.randn(Ylist[0].shape[0], self.input_dim)
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self.X = X
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return X
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def _init_Z(self, init="permute", X=None):
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if X is None:
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X = self.X
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if init in "permute":
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Z = np.random.permutation(X.copy())[:self.num_inducing]
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elif init in "random":
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Z = np.random.randn(self.num_inducing, self.input_dim) * X.var()
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self.Z = Z
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return Z
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class MRD(Model):
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"""
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@ -84,7 +177,7 @@ class MRD(Model):
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del self._in_init_
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self.gref = self.bgplvms[0]
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nparams = numpy.array([0] + [SparseGP._get_params(g).size - g.Z.size for g in self.bgplvms])
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nparams = np.array([0] + [SparseGP._get_params(g).size - g.Z.size for g in self.bgplvms])
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self.nparams = nparams.cumsum()
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self.num_data = self.gref.num_data
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@ -216,7 +309,7 @@ class MRD(Model):
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X_var = self.gref.X_variance.ravel()
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Z = self.gref.Z.ravel()
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thetas = [SparseGP._get_params(g)[g.Z.size:] for g in self.bgplvms]
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params = numpy.hstack([X, X_var, Z, numpy.hstack(thetas)])
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params = np.hstack([X, X_var, Z, np.hstack(thetas)])
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return params
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# def _set_var_params(self, g, X, X_var, Z):
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@ -239,13 +332,13 @@ class MRD(Model):
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# set params for all:
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for g, s, e in itertools.izip(self.bgplvms, self.nparams, self.nparams[1:]):
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g._set_params(numpy.hstack([X, X_var, Z, thetas[s:e]]))
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g._set_params(np.hstack([X, X_var, Z, thetas[s:e]]))
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# self._set_var_params(g, X, X_var, Z)
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# self._set_kern_params(g, thetas[s:e].copy())
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# g._compute_kernel_matrices()
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# if self.auto_scale_factor:
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# g.scale_factor = numpy.sqrt(g.psi2.sum(0).mean() * g.likelihood.precision)
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# # self.scale_factor = numpy.sqrt(self.psi2.sum(0).mean() * self.likelihood.precision)
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# g.scale_factor = np.sqrt(g.psi2.sum(0).mean() * g.likelihood.precision)
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# # self.scale_factor = np.sqrt(self.psi2.sum(0).mean() * self.likelihood.precision)
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# g._computations()
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@ -264,48 +357,18 @@ class MRD(Model):
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dKLmu, dKLdS = self.gref.dKL_dmuS()
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dLdmu -= dKLmu
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dLdS -= dKLdS
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dLdmuS = numpy.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
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dLdmuS = np.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
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dldzt1 = reduce(lambda a, b: a + b, (SparseGP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms))
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return numpy.hstack((dLdmuS,
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return np.hstack((dLdmuS,
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dldzt1,
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numpy.hstack([numpy.hstack([g.dL_dtheta(),
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np.hstack([np.hstack([g.dL_dtheta(),
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g.likelihood._gradients(\
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partial=g.partial_for_likelihood)]) \
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for g in self.bgplvms])))
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def _init_X(self, init='PCA', likelihood_list=None):
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if likelihood_list is None:
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likelihood_list = self.likelihood_list
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Ylist = []
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for likelihood_or_Y in likelihood_list:
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if type(likelihood_or_Y) is numpy.ndarray:
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Ylist.append(likelihood_or_Y)
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else:
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Ylist.append(likelihood_or_Y.Y)
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del likelihood_list
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if init in "PCA_concat":
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X = PCA(numpy.hstack(Ylist), self.input_dim)[0]
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elif init in "PCA_single":
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X = numpy.zeros((Ylist[0].shape[0], self.input_dim))
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for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.input_dim), len(Ylist)), Ylist):
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X[:, qs] = PCA(Y, len(qs))[0]
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else: # init == 'random':
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X = numpy.random.randn(Ylist[0].shape[0], self.input_dim)
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self.X = X
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return X
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def _init_Z(self, init="permute", X=None):
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if X is None:
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X = self.X
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if init in "permute":
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Z = numpy.random.permutation(X.copy())[:self.num_inducing]
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elif init in "random":
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Z = numpy.random.randn(self.num_inducing, self.input_dim) * X.var()
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self.Z = Z
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return Z
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def _handle_plotting(self, fignum, axes, plotf, sharex=False, sharey=False):
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if axes is None:
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fig = pylab.figure(num=fignum)
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@ -358,7 +421,7 @@ class MRD(Model):
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"""
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if titles is None:
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titles = [r'${}$'.format(name) for name in self.names]
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ymax = reduce(max, [numpy.ceil(max(g.input_sensitivity())) for g in self.bgplvms])
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ymax = reduce(max, [np.ceil(max(g.input_sensitivity())) for g in self.bgplvms])
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def plotf(i, g, ax):
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ax.set_ylim([0,ymax])
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g.kern.plot_ARD(ax=ax, title=titles[i], *args, **kwargs)
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