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https://github.com/SheffieldML/GPy.git
synced 2026-05-12 05:22:38 +02:00
Changed kernels in tests (lots still failing, but now mostly for good reason rather than silly naming problems)
This commit is contained in:
Alan Saul
parent
ca40b9803a
commit
9888d768b5
7 changed files with 92 additions and 87 deletions
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@ -25,10 +25,10 @@ class PsiStatModel(Model):
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self.kern = kernel
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self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance)
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self.add_parameters(self.X, self.X_variance, self.Z, self.kern)
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def log_likelihood(self):
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return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
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def parameters_changed(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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self.X.gradient = psimu
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@ -43,9 +43,9 @@ class PsiStatModel(Model):
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if self.which == 'psi0': dL_dpsi0 += 1
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if self.which == 'psi1': dL_dpsi1 += 1
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if self.which == 'psi2': dL_dpsi2 += 1
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self.kern.update_gradients_variational(numpy.zeros([1,1]),
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dL_dpsi0,
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dL_dpsi1,
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self.kern.update_gradients_variational(numpy.zeros([1,1]),
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dL_dpsi0,
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dL_dpsi1,
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dL_dpsi2, self.X, self.X_variance, self.Z)
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class DPsiStatTest(unittest.TestCase):
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@ -57,14 +57,14 @@ class DPsiStatTest(unittest.TestCase):
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X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
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Z = numpy.random.permutation(X)[:num_inducing]
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Y = X.dot(numpy.random.randn(input_dim, input_dim))
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# 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)]
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# 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)]
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kernels = [
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GPy.kern.linear(input_dim),
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GPy.kern.rbf(input_dim),
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#GPy.kern.bias(input_dim),
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#GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim),
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#GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim)
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GPy.kern.Linear(input_dim),
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GPy.kern.RBF(input_dim),
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#GPy.kern.Bias(input_dim),
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#GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim),
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#GPy.kern.RBF(input_dim) + GPy.kern.Bias(input_dim)
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]
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def testPsi0(self):
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@ -73,7 +73,7 @@ class DPsiStatTest(unittest.TestCase):
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num_inducing=self.num_inducing, kernel=k)
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m.randomize()
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assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k._parameters_)))
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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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@ -119,11 +119,11 @@ if __name__ == "__main__":
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if interactive:
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# N, num_inducing, input_dim, input_dim = 30, 5, 4, 30
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# X = numpy.random.rand(N, input_dim)
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# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
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# k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
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# K = k.K(X)
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# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, input_dim).T
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# Y -= Y.mean(axis=0)
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# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
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# k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
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# m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
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# m.randomize()
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# # self.assertTrue(m.checkgrad())
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@ -136,11 +136,11 @@ if __name__ == "__main__":
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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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Z = numpy.random.permutation(X)[:num_inducing]
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Y = X.dot(numpy.random.randn(input_dim, D))
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# kernel = GPy.kern.bias(input_dim)
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# kernel = GPy.kern.Bias(input_dim)
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#
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# kernels = [GPy.kern.linear(input_dim), GPy.kern.rbf(input_dim), GPy.kern.bias(input_dim),
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# GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim),
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# GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim)]
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# kernels = [GPy.kern.Linear(input_dim), GPy.kern.RBF(input_dim), GPy.kern.Bias(input_dim),
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# GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim),
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# GPy.kern.RBF(input_dim) + GPy.kern.Bias(input_dim)]
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# for k in kernels:
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# m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
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@ -148,32 +148,32 @@ if __name__ == "__main__":
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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.rbf(input_dim)+GPy.kern.bias(input_dim))
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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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# 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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# + GPy.kern.bias(input_dim))
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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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# m = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing,
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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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# 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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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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