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REFACTORING: model names, lowercase, classes uppercase

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This commit is contained in:
Max Zwiessele 2013-06-05 13:02:03 +01:00
parent 2a39440619
commit 2e5e8ac026
50 changed files with 436 additions and 3307 deletions
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46
GPy/testing/psi_stat_gradient_tests.py
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@ -17,14 +17,14 @@ class PsiStatModel(model):
self.X_variance = X_variance
self.Z = Z
self.N, self.input_dim = X.shape
self.M, input_dim = Z.shape
self.num_inducing, input_dim = Z.shape
assert self.input_dim == input_dim, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
self.kern = kernel
super(PsiStatModel, self).__init__()
self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance)
def _get_param_names(self):
Xnames = ["{}_{}_{}".format(what, i, j) for what, i, j in itertools.product(['X', 'X_variance'], range(self.N), range(self.input_dim))]
Znames = ["Z_{}_{}".format(i, j) for i, j in itertools.product(range(self.M), range(self.input_dim))]
Znames = ["Z_{}_{}".format(i, j) for i, j in itertools.product(range(self.num_inducing), range(self.input_dim))]
return Xnames + Znames + self.kern._get_param_names()
def _get_params(self):
return numpy.hstack([self.X.flatten(), self.X_variance.flatten(), self.Z.flatten(), self.kern._get_params()])
@ -34,7 +34,7 @@ class PsiStatModel(model):
start, end = end, end + self.X_variance.size
self.X_variance = x[start: end].reshape(self.N, self.input_dim)
start, end = end, end + self.Z.size
self.Z = x[start: end].reshape(self.M, self.input_dim)
self.Z = x[start: end].reshape(self.num_inducing, self.input_dim)
self.kern._set_params(x[end:])
def log_likelihood(self):
return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
@ -43,19 +43,19 @@ class PsiStatModel(model):
try:
psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
except AttributeError:
psiZ = numpy.zeros(self.M * self.input_dim)
psiZ = numpy.zeros(self.num_inducing * self.input_dim)
thetagrad = self.kern.__getattribute__("d" + self.which + "_dtheta")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance).flatten()
return numpy.hstack((psimu.flatten(), psiS.flatten(), psiZ.flatten(), thetagrad))
class DPsiStatTest(unittest.TestCase):
input_dim = 5
N = 50
M = 10
D = 20
num_inducing = 10
input_dim = 20
X = numpy.random.randn(N, input_dim)
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(input_dim, D))
Z = numpy.random.permutation(X)[:num_inducing]
Y = X.dot(numpy.random.randn(input_dim, input_dim))
# kernels = [GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)), GPy.kern.rbf(input_dim, ARD=True), GPy.kern.bias(input_dim)]
kernels = [GPy.kern.linear(input_dim), GPy.kern.rbf(input_dim), GPy.kern.bias(input_dim),
@ -65,7 +65,7 @@ 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.M, kernel=k)
M=self.num_inducing, kernel=k)
try:
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
except:
@ -80,27 +80,27 @@ class DPsiStatTest(unittest.TestCase):
def testPsi2_lin(self):
k = self.kernels[0]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
M=self.M, kernel=k)
M=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.M, kernel=k)
M=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.M, kernel=k)
M=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.M, kernel=k)
M=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.M, kernel=k)
M=self.num_inducing, kernel=k)
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
@ -108,14 +108,14 @@ if __name__ == "__main__":
import sys
interactive = 'i' in sys.argv
if interactive:
# N, M, input_dim, D = 30, 5, 4, 30
# N, num_inducing, input_dim, input_dim = 30, 5, 4, 30
# X = numpy.random.rand(N, input_dim)
# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# K = k.K(X)
# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, D).T
# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, input_dim).T
# Y -= Y.mean(axis=0)
# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, M=M)
# m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
# m.ensure_default_constraints()
# m.randomize()
# # self.assertTrue(m.checkgrad())
@ -136,22 +136,22 @@ if __name__ == "__main__":
# for k in kernels:
# m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=k)
# num_inducing=num_inducing, kernel=k)
# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
#
# m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.linear(input_dim))
# num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim))
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=kernel)
# num_inducing=num_inducing, kernel=kernel)
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=kernel)
# num_inducing=num_inducing, kernel=kernel)
# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.rbf(input_dim))
# 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)))
m3.ensure_default_constraints()
# + GPy.kern.bias(input_dim))
# m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# M=M, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))
# num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))
else:
unittest.main()