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copy and missing data

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Max Zwiessele 2014-02-19 15:32:16 +00:00
parent 0082acad63
commit c4f6b0dbe7
10 changed files with 179 additions and 97 deletions
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11
GPy/examples/dimensionality_reduction.py
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@ -1,9 +1,9 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as _np
default_seed = _np.random.seed(123344)
#default_seed = _np.random.seed(123344)
def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False, output_dim=200, nan=False):
def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan=False):
"""
model for testing purposes. Samples from a GP with rbf kernel and learns
the samples with a new kernel. Normally not for optimization, just model cheking
@ -41,7 +41,7 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False,
if nan:
m.inference_method = GPy.inference.latent_function_inference.var_dtc.VarDTCMissingData()
m.Y[_np.random.binomial(1,p,size=(Y.shape))] = _np.nan
m.Y[_np.random.binomial(1,p,size=(Y.shape)).astype(bool)] = _np.nan
m.parameters_changed()
#===========================================================================
@ -186,6 +186,8 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
return m
def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
_np.random.seed(1234)
x = _np.linspace(0, 4 * _np.pi, N)[:, None]
s1 = _np.vectorize(lambda x: _np.sin(x))
s2 = _np.vectorize(lambda x: _np.cos(x))
@ -293,10 +295,11 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
Y = Ylist[0]
k = kern.linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
inan = _np.random.binomial(1, .3, size=Y.shape)
inan = _np.random.binomial(1, .6, size=Y.shape).astype(bool)
m = BayesianGPLVM(Y, Q, init="random", num_inducing=num_inducing, kernel=k)
m.inference_method = VarDTCMissingData()
m.Y[inan] = _np.nan
m.q.variance *= .1
m.parameters_changed()
if optimize: