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Merge branch 'params' of github.com:SheffieldML/GPy into params
This commit is contained in:
James Hensman
commit
65a2c2a320
9 changed files with 112 additions and 14 deletions
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@ -54,19 +54,21 @@ class SparseGP(GP):
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self.add_parameter(self.Z, index=0)
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self.parameters_changed()
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def parameters_changed(self):
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self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
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#The derivative of the bound wrt the inducing inputs Z ( unless they're all fixed)
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def _update_gradients_Z(self, add=False):
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#The derivative of the bound wrt the inducing inputs Z ( unless they're all fixed)
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if not self.Z.is_fixed:
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self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
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if add: self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
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else: self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
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if self.X_variance is None:
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self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
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else:
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self.Z.gradient += self.kern.dpsi1_dZ(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
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self.Z.gradient += self.kern.dpsi2_dZ(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
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def parameters_changed(self):
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self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
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self._update_gradients_Z(add=False)
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def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
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"""
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Make a prediction for the latent function values
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@ -59,8 +59,9 @@ class Optimizer():
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"""
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See GPy.plotting.matplot_dep.inference_plots
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"""
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import sys
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import inference_plots
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from ...plotting.matplot_dep import inference_plots
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inference_plots.plot_optimizer(self)
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@ -14,7 +14,8 @@ class Bias(Kernpart):
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:type variance: float
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"""
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super(Bias, self).__init__(input_dim, name)
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self.variance = Param("variance", variance)
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from ...core.parameterization.transformations import Logexp
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self.variance = Param("variance", variance, Logexp())
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self.add_parameter(self.variance)
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def K(self,X,X2,target):
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@ -61,7 +61,7 @@ class Linear(Kernpart):
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def update_gradients_full(self, dL_dK, X):
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#self.variances.gradient[:] = 0
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self._param_grad_helper(dL_dK, X, self.variances.gradient)
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self._param_grad_helper(dL_dK, X, None, self.variances.gradient)
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def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
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tmp = dL_dKdiag[:, None] * X ** 2
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@ -265,7 +265,7 @@ class RBF(Kernpart):
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}
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"""
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num_data, num_inducing, input_dim = X.shape[0], X.shape[0], self.input_dim
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X, dvardLdK = param_to_array(X, dvardLdK)
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X, dvardLdK, var_len3 = param_to_array(X, dvardLdK, var_len3)
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weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'target', 'dvardLdK', 'var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
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else:
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code = """
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@ -282,7 +282,7 @@ class RBF(Kernpart):
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}
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"""
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num_data, num_inducing, input_dim = X.shape[0], X2.shape[0], self.input_dim
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X, X2, dvardLdK = param_to_array(X, X2, dvardLdK)
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X, X2, dvardLdK, var_len3 = param_to_array(X, X2, dvardLdK, var_len3)
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weave.inline(code, arg_names=['num_data', 'num_inducing', 'input_dim', 'X', 'X2', 'target', 'dvardLdK', 'var_len3'], type_converters=weave.converters.blitz, **self.weave_options)
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return target
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@ -72,9 +72,10 @@ class BayesianGPLVM(SparseGP, GPLVM):
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return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.num_data
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def parameters_changed(self):
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super(BayesianGPLVM, self).parameters_changed()
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self._log_marginal_likelihood -= self.KL_divergence()
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self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
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self._update_gradients_Z(add=False)
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self._log_marginal_likelihood -= self.KL_divergence()
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dL_dmu, dL_dS = self.dL_dmuS()
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# dL:
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@ -161,6 +162,38 @@ class BayesianGPLVM(SparseGP, GPLVM):
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return dim_reduction_plots.plot_steepest_gradient_map(self,*args,**kwargs)
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class BayesianGPLVMWithMissingData(BayesianGPLVM):
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def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
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Z=None, kernel=None, inference_method=None, likelihood=None, name='bayesian gplvm', **kwargs):
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from ..util.subarray_and_sorting import common_subarrays
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self.subarrays = common_subarrays(Y)
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import ipdb;ipdb.set_trace()
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BayesianGPLVM.__init__(self, Y, input_dim, X=X, X_variance=X_variance, init=init, num_inducing=num_inducing, Z=Z, kernel=kernel, inference_method=inference_method, likelihood=likelihood, name=name, **kwargs)
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def parameters_changed(self):
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super(BayesianGPLVM, self).parameters_changed()
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self._log_marginal_likelihood -= self.KL_divergence()
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dL_dmu, dL_dS = self.dL_dmuS()
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# dL:
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self.q.mean.gradient = dL_dmu
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self.q.variance.gradient = dL_dS
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# dKL:
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self.q.mean.gradient -= self.X
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self.q.variance.gradient -= (1. - (1. / (self.X_variance))) * 0.5
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if __name__ == '__main__':
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import numpy as np
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X = np.random.randn(20,2)
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W = np.linspace(0,1,10)[None,:]
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Y = (X*W).sum(1)
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missing = np.random.binomial(1,.1,size=Y.shape)
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pass
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def latent_cost_and_grad(mu_S, kern, Z, dL_dpsi0, dL_dpsi1, dL_dpsi2):
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"""
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objective function for fitting the latent variables for test points
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60
GPy/testing/index_operations_tests.py
Normal file
60
GPy/testing/index_operations_tests.py
Normal file
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@ -0,0 +1,60 @@
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'''
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Created on 12 Feb 2014
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@author: maxz
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'''
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import unittest
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import numpy as np
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from GPy.core.parameterization.index_operations import ParameterIndexOperations,\
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ParameterIndexOperationsView
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one, two, three = 'one', 'two', 'three'
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class Test(unittest.TestCase):
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def setUp(self):
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self.param_index = ParameterIndexOperations()
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self.param_index.add(one, [3])
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self.param_index.add(two, [0,5])
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self.param_index.add(three, [2,4,7])
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def test_remove(self):
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self.param_index.remove(three, np.r_[3:10])
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self.assertListEqual(self.param_index[three].tolist(), [2])
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self.param_index.remove(one, [1])
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self.assertListEqual(self.param_index[one].tolist(), [3])
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def test_index_view(self):
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#=======================================================================
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# 0 1 2 3 4 5 6 7 8 9
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# one
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# two two
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# three three three
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# view: [0 1 2 3 4 5 ]
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#=======================================================================
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view = ParameterIndexOperationsView(self.param_index, 2, 6)
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self.assertSetEqual(set(view.properties()), set([one, two, three]))
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for v,p in zip(view.properties_for(np.r_[:6]), self.param_index.properties_for(np.r_[2:2+6])):
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self.assertEqual(v, p)
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self.assertSetEqual(set(view[two]), set([3]))
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self.assertSetEqual(set(self.param_index[two]), set([0, 5]))
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view.add(two, np.array([0]))
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self.assertSetEqual(set(view[two]), set([0,3]))
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self.assertSetEqual(set(self.param_index[two]), set([0, 2, 5]))
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view.clear()
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for v,p in zip(view.properties_for(np.r_[:6]), self.param_index.properties_for(np.r_[2:2+6])):
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self.assertEqual(v, p)
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self.assertEqual(v, [])
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param_index = ParameterIndexOperations()
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param_index.add(one, [3])
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param_index.add(two, [0,5])
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param_index.add(three, [2,4,7])
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view2 = ParameterIndexOperationsView(param_index, 2, 6)
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view.update(view2)
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for [i,v],[i2,v2] in zip(sorted(param_index.items()), sorted(self.param_index.items())):
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self.assertEqual(i, i2)
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self.assertTrue(np.all(v == v2))
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if __name__ == "__main__":
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#import sys;sys.argv = ['', 'Test.test_index_view']
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unittest.main()
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@ -10,6 +10,7 @@ import datasets
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import mocap
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import decorators
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import classification
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import subarray_and_sorting
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import caching
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try:
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@ -29,7 +29,7 @@ class Cacher(object):
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return self.cached_outputs[-1]
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def on_cache_changed(self, X):
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print id(X)
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#print id(X)
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i = self.cached_inputs.index(X)
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self.inputs_changed[i] = True
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