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LinearCF Psi Stat not working yet, strange bug in psi computations

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This commit is contained in:
Max Zwiessele 2013-05-01 17:09:38 +01:00
parent c502b66ea3
commit 42474f0044
8 changed files with 353 additions and 244 deletions
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46
GPy/testing/psi_stat_tests.py
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@ -106,18 +106,18 @@ if __name__ == "__main__":
import sys
interactive = 'i' in sys.argv
if interactive:
N, M, Q, D = 30, 5, 4, 30
X = numpy.random.rand(N, Q)
k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X)
Y = numpy.random.multivariate_normal(numpy.zeros(N), K, D).T
Y -= Y.mean(axis=0)
k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
m.ensure_default_constraints()
m.randomize()
# self.assertTrue(m.checkgrad())
# N, M, Q, D = 30, 5, 4, 30
# X = numpy.random.rand(N, Q)
# k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
# K = k.K(X)
# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, D).T
# Y -= Y.mean(axis=0)
# k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
# m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
# m.ensure_default_constraints()
# m.randomize()
# # self.assertTrue(m.checkgrad())
numpy.random.seed(0)
Q = 5
N = 50
M = 10
@ -126,11 +126,11 @@ if __name__ == "__main__":
X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
Z = numpy.random.permutation(X)[:M]
Y = X.dot(numpy.random.randn(Q, D))
kernel = GPy.kern.bias(Q)
kernels = [GPy.kern.linear(Q), GPy.kern.rbf(Q), GPy.kern.bias(Q),
GPy.kern.linear(Q) + GPy.kern.bias(Q),
GPy.kern.rbf(Q) + GPy.kern.bias(Q)]
# kernel = GPy.kern.bias(Q)
#
# kernels = [GPy.kern.linear(Q), GPy.kern.rbf(Q), GPy.kern.bias(Q),
# GPy.kern.linear(Q) + GPy.kern.bias(Q),
# GPy.kern.rbf(Q) + GPy.kern.bias(Q)]
# for k in kernels:
# m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
@ -143,11 +143,13 @@ if __name__ == "__main__":
# M=M, kernel=kernel)
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=kernel)
m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
M=M, kernel=GPy.kern.rbf(Q))
# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.rbf(Q))
m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
M=M, kernel=GPy.kern.linear(Q) + GPy.kern.bias(Q))
m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
M=M, kernel=GPy.kern.rbf(Q) + GPy.kern.bias(Q))
M=M, kernel=GPy.kern.linear(Q))
m3.ensure_default_constraints()
# + GPy.kern.bias(Q))
# m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.rbf(Q) + GPy.kern.bias(Q))
else:
unittest.main()