coregionalisation changed to coregionalization

GPy/util/__init__.py

@@ -14,4 +14,3 @@ import visualize
 import decorators
 import classification
 import latent_space_visualizations
-#import multioutput

GPy/util/multioutput.py

@@ -1,35 +0,0 @@
-import numpy as np
-import warnings
-from .. import kern
-
-def build_lcm(input_dim, num_outputs, CK = [], NC = [], W_columns=1,W=None,kappa=None):
-    #TODO build_icm or build_lcm
-    """
-    Builds a kernel for a linear coregionalization model
-
-    :input_dim: Input dimensionality
-    :num_outputs: Number of outputs
-    :param CK: List of coregionalized kernels (i.e., this will be multiplied by a coregionalise kernel).
-    :param K: List of kernels that will be added up together with CK, but won't be multiplied by a coregionalise kernel
-    :param W_columns: number tuples of the corregionalization parameters 'coregion_W'
-    :type W_columns: integer
-    """
-
-    for k in CK:
-        if k.input_dim <> input_dim:
-            k.input_dim = input_dim
-            warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
-
-    for k in NC:
-        if k.input_dim <> input_dim + 1:
-            k.input_dim = input_dim + 1
-            warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
-
-    kernel = CK[0].prod(kern.coregionalise(num_outputs,W_columns,W,kappa),tensor=True)
-    for k in CK[1:]:
-        k_coreg = kern.coregionalise(num_outputs,W_columns,W,kappa)
-        kernel += k.prod(k_coreg,tensor=True)
-    for k in NC:
-        kernel += k
-
-    return kernel