apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-07 11:02:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

remove dead hierachical code

Browse source
This commit is contained in:
James Hensman 2014-11-04 17:55:55 +00:00
parent b3aa8fa1e2
commit 669a2f0cef
2 changed files with 0 additions and 77 deletions
Download patch file Download diff file Expand all files Collapse all files

76
GPy/kern/_src/hierarchical.py
View file

@ -1,76 +0,0 @@
# Copyright (c) 2012, James Hesnsman
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from kernpart import Kernpart
import numpy as np
from independent_outputs import index_to_slices
class Hierarchical(Kernpart):
"""
A kernel part which can reopresent a hierarchy of indepencnce: a generalisation of independent_outputs
"""
def __init__(self,parts,name='hierarchy'):
self.levels = len(parts)
self.input_dim = parts[0].input_dim + 1
self.num_params = np.sum([k.num_params for k in parts])
self.name = name
self.parts = parts
self.param_starts = np.hstack((0,np.cumsum([k.num_params for k in self.parts[:-1]])))
self.param_stops = np.cumsum([k.num_params for k in self.parts])
def _get_params(self):
return np.hstack([k._get_params() for k in self.parts])
def _set_params(self,x):
[k._set_params(x[start:stop]) for k, start, stop in zip(self.parts, self.param_starts, self.param_stops)]
def _get_param_names(self):
return sum([[str(i)+'_'+k.name+'_'+n for n in k._get_param_names()] for i,k in enumerate(self.parts)],[])
def _sort_slices(self,X,X2):
slices = [index_to_slices(x) for x in X[:,-self.levels:].T]
X = X[:,:-self.levels]
if X2 is None:
slices2 = slices
X2 = X
else:
slices2 = [index_to_slices(x) for x in X2[:,-self.levels:].T]
X2 = X2[:,:-self.levels]
return X, X2, slices, slices2
def K(self,X,X2,target):
X, X2, slices, slices2 = self._sort_slices(X,X2)
[[[[k.K(X[s],X2[s2],target[s,s2]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_,slices2_)] for k, slices_, slices2_ in zip(self.parts,slices,slices2)]
def Kdiag(self,X,target):
raise NotImplementedError
#X,slices = X[:,:-1],index_to_slices(X[:,-1])
#[[self.k.Kdiag(X[s],target[s]) for s in slices_i] for slices_i in slices]
def _param_grad_helper(self,dL_dK,X,X2,target):
X, X2, slices, slices2 = self._sort_slices(X,X2)
[[[[k._param_grad_helper(dL_dK[s,s2],X[s],X2[s2],target[p_start:p_stop]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_, slices2_)] for k, p_start, p_stop, slices_, slices2_ in zip(self.parts, self.param_starts, self.param_stops, slices, slices2)]
def gradients_X(self,dL_dK,X,X2,target):
raise NotImplementedError
#X,slices = X[:,:-1],index_to_slices(X[:,-1])
#if X2 is None:
#X2,slices2 = X,slices
#else:
#X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
#[[[self.k.gradients_X(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
#
def dKdiag_dX(self,dL_dKdiag,X,target):
raise NotImplementedError
#X,slices = X[:,:-1],index_to_slices(X[:,-1])
#[[self.k.dKdiag_dX(dL_dKdiag[s],X[s],target[s,:-1]) for s in slices_i] for slices_i in slices]
def dKdiag_dtheta(self,dL_dKdiag,X,target):
raise NotImplementedError
#X,slices = X[:,:-1],index_to_slices(X[:,-1])
#[[self.k.dKdiag_dX(dL_dKdiag[s],X[s],target) for s in slices_i] for slices_i in slices]