From 10fcb4027b344c374be3b374578a2421d2b6daf5 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Mon, 16 Dec 2013 17:17:47 +0000 Subject: [PATCH] Fixed some tests --- GPy/examples/dimensionality_reduction.py | 4 ++-- GPy/models_modules/sparse_gplvm.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py index 3af42ef1..46fc6797 100644 --- a/GPy/examples/dimensionality_reduction.py +++ b/GPy/examples/dimensionality_reduction.py @@ -41,7 +41,7 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False): # randomly obstruct data with percentage p p = .8 Y_obstruct = Y.copy() - Y_obstruct[np.random.uniform(size=(Y.shape)) < p] = np.nan + Y_obstruct[_np.random.uniform(size=(Y.shape)) < p] = _np.nan #=========================================================================== m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing) m.lengthscales = lengthscales @@ -52,7 +52,7 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False): pb.title('PCA initialisation') m2.plot() pb.title('PCA initialisation') - + if optimize: m.optimize('scg', messages=verbose) m2.optimize('scg', messages=verbose) diff --git a/GPy/models_modules/sparse_gplvm.py b/GPy/models_modules/sparse_gplvm.py index 4e401ee3..44f6c7ef 100644 --- a/GPy/models_modules/sparse_gplvm.py +++ b/GPy/models_modules/sparse_gplvm.py @@ -6,7 +6,7 @@ import numpy as np import pylab as pb import sys, pdb from sparse_gp_regression import SparseGPRegression -from gplvm import GPLVM +from gplvm import GPLVM, initialise_latent # from .. import kern # from ..core import model # from ..util.linalg import pdinv, PCA @@ -24,7 +24,7 @@ class SparseGPLVM(SparseGPRegression, GPLVM): """ def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10): - X = self.initialise_latent(init, input_dim, Y) + X = initialise_latent(init, input_dim, Y) SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing) self.ensure_default_constraints()