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Changed all M's for num_inducing

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Alan Saul 2013-06-05 15:29:45 +01:00
parent aac4f6a237
commit 3475b52b6c
21 changed files with 142 additions and 142 deletions
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24
GPy/testing/psi_stat_gradient_tests.py
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@ -11,7 +11,7 @@ import itertools
from GPy.core import model
class PsiStatModel(model):
def __init__(self, which, X, X_variance, Z, M, kernel):
def __init__(self, which, X, X_variance, Z, num_inducing, kernel):
self.which = which
self.X = X
self.X_variance = X_variance
@ -64,8 +64,8 @@ class DPsiStatTest(unittest.TestCase):
def testPsi0(self):
for k in self.kernels:
m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.num_inducing, kernel=k)
m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
try:
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
except:
@ -74,33 +74,33 @@ class DPsiStatTest(unittest.TestCase):
# def testPsi1(self):
# for k in self.kernels:
# m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
# M=self.M, kernel=k)
# num_inducing=self.num_inducing, kernel=k)
# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_lin(self):
k = self.kernels[0]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.num_inducing, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_lin_bia(self):
k = self.kernels[3]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.num_inducing, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_rbf(self):
k = self.kernels[1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.num_inducing, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_rbf_bia(self):
k = self.kernels[-1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.num_inducing, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_bia(self):
k = self.kernels[2]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.num_inducing, kernel=k)
num_inducing=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
@ -122,11 +122,11 @@ if __name__ == "__main__":
numpy.random.seed(0)
input_dim = 5
N = 50
M = 10
num_inducing = 10
D = 15
X = numpy.random.randn(N, input_dim)
X_var = .5 * numpy.ones_like(X) + .1 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
Z = numpy.random.permutation(X)[:M]
Z = numpy.random.permutation(X)[:num_inducing]
Y = X.dot(numpy.random.randn(input_dim, D))
# kernel = GPy.kern.bias(input_dim)
#
@ -148,7 +148,7 @@ if __name__ == "__main__":
# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim))
m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
M=M, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
m3.ensure_default_constraints()
# + GPy.kern.bias(input_dim))
# m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,