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hard-merging in the examples and testing dirs from master.
This is probably a dumb way to do it, but I don;t know better.
This commit is contained in:
James Hensman
parent
8022de2a86
commit
375e2f6225
16 changed files with 1747 additions and 758 deletions
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@ -40,10 +40,9 @@ class PsiStatModel(Model):
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return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
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def _log_likelihood_gradients(self):
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psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
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try:
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psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
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except AttributeError:
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psiZ = numpy.zeros(self.num_inducing * self.input_dim)
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#psimu, psiS = numpy.ones(self.N * self.input_dim), numpy.ones(self.N * self.input_dim)
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psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
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#psiZ = numpy.ones(self.num_inducing * self.input_dim)
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thetagrad = self.kern.__getattribute__("d" + self.which + "_dtheta")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance).flatten()
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return numpy.hstack((psimu.flatten(), psiS.flatten(), psiZ.flatten(), thetagrad))
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@ -64,40 +63,54 @@ class DPsiStatTest(unittest.TestCase):
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def testPsi0(self):
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for k in self.kernels:
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m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,
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m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,\
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num_inducing=self.num_inducing, kernel=k)
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m.ensure_default_constraints()
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m.randomize()
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assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
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# def testPsi1(self):
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# for k in self.kernels:
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# m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
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# num_inducing=self.num_inducing, kernel=k)
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# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
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def testPsi1(self):
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for k in self.kernels:
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m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.ensure_default_constraints()
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m.randomize()
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assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
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def testPsi2_lin(self):
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k = self.kernels[0]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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num_inducing=self.num_inducing, kernel=k)
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m.ensure_default_constraints()
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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def testPsi2_lin_bia(self):
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k = self.kernels[3]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.ensure_default_constraints()
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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def testPsi2_rbf(self):
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k = self.kernels[1]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.ensure_default_constraints()
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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def testPsi2_rbf_bia(self):
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k = self.kernels[-1]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.ensure_default_constraints()
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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def testPsi2_bia(self):
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k = self.kernels[2]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.ensure_default_constraints()
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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@ -116,9 +129,9 @@ if __name__ == "__main__":
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# m.randomize()
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# # self.assertTrue(m.checkgrad())
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numpy.random.seed(0)
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input_dim = 5
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N = 50
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num_inducing = 10
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input_dim = 3
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N = 3
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num_inducing = 2
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D = 15
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X = numpy.random.randn(N, input_dim)
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X_var = .5 * numpy.ones_like(X) + .1 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
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@ -135,18 +148,35 @@ if __name__ == "__main__":
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# num_inducing=num_inducing, kernel=k)
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# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
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#
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# m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim))
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m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
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num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim)+GPy.kern.bias(input_dim))
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# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=kernel)
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# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=kernel)
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# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim))
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m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
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# m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
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# + GPy.kern.bias(input_dim))
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# m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim))
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# m = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing,
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# kernel=(
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# GPy.kern.rbf(input_dim, ARD=1)
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# +GPy.kern.linear(input_dim, ARD=1)
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# +GPy.kern.bias(input_dim))
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# )
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# m.ensure_default_constraints()
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m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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num_inducing=num_inducing, kernel=(
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GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1)
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#+GPy.kern.linear(input_dim, numpy.random.rand(input_dim), ARD=1)
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#+GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1)
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#+GPy.kern.rbf(input_dim, numpy.random.rand(), numpy.random.rand(), ARD=0)
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+GPy.kern.bias(input_dim)
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+GPy.kern.white(input_dim)
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)
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)
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m2.ensure_default_constraints()
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else:
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unittest.main()
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