GPCoregionalizedRegresssion added

GPy/likelihoods/mixed_noise.py

@@ -0,0 +1,58 @@
+import numpy as np
+from scipy import stats, special
+from GPy.util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
+import link_functions
+from likelihood import Likelihood
+from ..core.parameterization import Param
+from ..core.parameterization.transformations import Logexp
+from ..core.parameterization import Parameterized
+import itertools
+
+class MixedNoise(Likelihood):
+    def __init__(self, likelihoods_list, noise_index, variance = None, name='mixed_noise'):
+
+        Nlike = len(likelihoods_list)
+        self.order = np.unique(noise_index)
+
+        assert self.order.size == Nlike
+
+        if variance is None:
+            variance = np.ones(Nlike)
+        else:
+            assert variance.size == Nlike
+
+        super(Likelihood, self).__init__(name=name)
+
+        self.add_parameters(*likelihoods_list)
+        self.likelihoods_list = likelihoods_list
+        self.noise_index = noise_index
+        self.log_concave = False
+        self.likelihoods_indices = [noise_index.flatten()==j for j in self.order]
+
+    def covariance_matrix(self, Y, noise_index, **Y_metadata):
+        variance = np.zeros(Y.shape[0])
+        for lik, ind in itertools.izip(self.likelihoods_list, self.likelihoods_indices):
+            variance[ind] = lik.variance
+        return np.diag(variance)
+
+    def update_gradients(self, partial, noise_index, **Y_metadata):
+        [lik.update_gradients(partial[ind]) for lik,ind in itertools.izip(self.likelihoods_list, self.likelihoods_indices)]
+
+    def predictive_values(self, mu, var, full_cov=False, noise_index=None, **Y_metadata):
+        _variance = np.array([ self.likelihoods_list[j].variance for j in noise_index ])
+        if full_cov:
+            var += np.eye(var.shape[0])*_variance
+            d = 2*np.sqrt(np.diag(var))
+            low, up = mu - d, mu + d
+        else:
+            var += _variance
+            d = 2*np.sqrt(var)
+            low, up = mu - d, mu + d
+        return mu, var, low, up
+
+    def predictive_variance(self, mu, sigma, noise_index, predictive_mean=None, **Y_metadata):
+        if isinstance(noise_index,int):
+            _variance = self.variance[noise_index]
+        else:
+            _variance = np.array([ self.variance[j] for j in noise_index ])[:,None]
+        return _variance + sigma**2