Useless files deleted

GPy/models/__init__.py

@@ -11,7 +11,5 @@ from gplvm import GPLVM
 from warped_gp import WarpedGP
 from bayesian_gplvm import BayesianGPLVM
 from mrd import MRD
-from gp_multioutput import GPMultioutput
 from gp_multioutput_regression import GPMultioutputRegression
 from sparse_gp_multioutput_regression import SparseGPMultioutputRegression
-from sparse_gp_multioutput import SparseGPMultioutput

GPy/models/sparse_gp_multioutput.py

@@ -1,97 +0,0 @@
-# Copyright (c) 2013, Ricardo Andrade
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import numpy as np
-from ..core import SparseGP
-from .. import likelihoods
-from .. import kern
-from ..util import multioutput
-
-
-import pylab as pb
-
-class SparseGPMultioutput(SparseGP):
-    """
-    Multiple output Gaussian process
-
-    This is a thin wrapper around the models.GP class, with a set of sensible defaults
-
-    :param X_list: input observations
-    :param Y_list: observed values
-    :param L_list: a GPy likelihood, defaults to Binomial with probit link_function
-    :param kernel_list: a GPy kernel, defaults to rbf
-    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_X: False|True
-    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_Y: False|True
-
-    .. Note:: Multiple independent outputs are allowed using columns of Y
-
-    """
-
-    def __init__(self,X_list,Y_list,kernel_list=None,normalize_X=False,normalize_Y=False,Z_list=None,num_inducing_list=10,X_variance=None,W=1,mixed_noise_list=[]): #TODO W
-
-        assert len(X_list) == len(Y_list)
-        index = []
-        for x,y,j in zip(X_list,Y_list,range(len(X_list))):
-            assert x.shape[0] == y.shape[0]
-            index.append(np.repeat(j,y.size)[:,None])
-        index = np.vstack(index)
-
-
-        self.likelihood_list = []
-        if mixed_noise_list == []:
-            for Y in Y_list:
-                self.likelihood_list.append(likelihoods.Gaussian(Y,normalize = normalize_Y))
-
-            Y = np.vstack([l_.Y for l_ in self.likelihood_list])
-            likelihood = likelihoods.Gaussian(Y,normalize=False)
-            likelihood.index = index
-
-        else:
-            assert len(Y_list) == len(mixed_noise_list)
-            for noise,Y in zip(mixed_noise_list,Y_list):
-                self.likelihood_list.append(likelihoods.EP(Y,noise))
-            likelihood = likelihoods.EP_Mixed_Noise(Y_list, mixed_noise_list)
-
-        """
-        if noise_list == []:
-            self.likelihood_list = []
-            for Y in Y_list:
-                self.likelihood_list.append(likelihoods.Gaussian(Y,normalize = normalize_Y))
-
-        Y = np.vstack([l_.Y for l_ in self.likelihood_list])
-        likelihood = likelihoods.Gaussian(Y,normalize=False)
-        likelihood.index = index
-        """
-        X = np.hstack([np.vstack(X_list),index])
-        original_dim = X.shape[1] - 1
-
-        if kernel_list is None:
-            kernel_list = [[kern.rbf(original_dim)],[kern.white(original_dim+1)]]
-
-        mkernel = multioutput.build_cor_kernel(input_dim=original_dim, Nout=len(X_list), CK = kernel_list[0], NC = kernel_list[1], W=1)
-
-        z_index = []
-        if Z_list is None:
-            if isinstance(num_inducing_list,int):
-                num_inducing_list = [num_inducing_list for Xj in X_list]
-            Z_list = []
-            for Xj,nj,j in zip(X_list,num_inducing_list,range(len(X_list))):
-                i = np.random.permutation(Xj.shape[0])[:nj]
-                z_index.append(np.repeat(j,nj)[:,None])
-                Z_list.append(Xj[i].copy())
-        else:
-            assert len(Z_list) == len(X_list)
-            for Zj,Xj,j in zip(Z_list,X_list,range(len(Z_list))):
-                assert Zj.shape[1] == Xj.shape[1]
-                z_index.append(np.repeat(j,Zj.shape[0])[:,None])
-
-        Z = np.hstack([np.vstack(Z_list),np.vstack(z_index)])
-
-
-        self.multioutput = True
-        SparseGP.__init__(self, X, likelihood, mkernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
-        self.constrain_fixed('.*iip_\d+_1')
-        self.ensure_default_constraints()