def decompositionFromPool(self, rpool):
kernel = rpool['kernel_obj']
self.X = rpool['train_features']
if rpool.has_key('basis_vectors'):
basis_vectors = rpool['basis_vectors']
else:
basis_vectors = None
if "bias" in rpool:
self.bias = float(rpool["bias"])
else:
self.bias = 0.
if basis_vectors != None or self.X.shape[1] > self.X.shape[0]:
#First possibility: subset of regressors has been invoked
if basis_vectors != None:
K_r = kernel.getKM(self.X).T
Krr = kernel.getKM(basis_vectors)
svals, evecs, U, Z = decomposition.decomposeSubsetKM(K_r, Krr)
#Second possibility: dual mode if more attributes than examples
else:
K = kernel.getKM(self.X).T
svals, evecs = decomposition.decomposeKernelMatrix(K)
U, Z = None, None
#Third possibility, primal decomposition
else:
#Invoking getPrimalDataMatrix adds the bias feature
X = getPrimalDataMatrix(self.X,self.bias)
svals, evecs, U = decomposition.decomposeDataMatrix(X.T)
U, Z = None, None
return svals, evecs, U, Z
def decompositionFromPool(self, rpool):
kernel = rpool['kernel_obj']
self.X = rpool['train_features']
if rpool.has_key('basis_vectors'):
basis_vectors = rpool['basis_vectors']
else:
basis_vectors = None
if "bias" in rpool:
self.bias = float(rpool["bias"])
else:
self.bias = 0.
if basis_vectors != None or self.X.shape[1] > self.X.shape[0]:
#First possibility: subset of regressors has been invoked
if basis_vectors != None:
K_r = kernel.getKM(self.X).T
Krr = kernel.getKM(basis_vectors)
svals, evecs, U, Z = decomposition.decomposeSubsetKM(K_r, Krr)
#Second possibility: dual mode if more attributes than examples
else:
K = kernel.getKM(self.X).T
svals, evecs = decomposition.decomposeKernelMatrix(K)
U, Z = None, None
#Third possibility, primal decomposition
else:
#Invoking getPrimalDataMatrix adds the bias feature
X = getPrimalDataMatrix(self.X, self.bias)
svals, evecs, U = decomposition.decomposeDataMatrix(X.T)
U, Z = None, None
return svals, evecs, U, Z
def decompositionFromPool(self, rpool):
kernel = rpool[data_sources.KERNEL_OBJ]
self.X = rpool[data_sources.TRAIN_FEATURES]
if rpool.has_key(data_sources.BASIS_VECTORS):
bvectors = rpool[data_sources.BASIS_VECTORS]
else:
bvectors = None
if "bias" in rpool:
self.bias = float(rpool["bias"])
else:
self.bias = 0.
if bvectors != None or self.X.shape[1] > self.X.shape[0]:
K = kernel.getKM(self.X).T
#First possibility: subset of regressors has been invoked
if bvectors != None:
svals, evecs, U, Z = decomposition.decomposeSubsetKM(
K, bvectors)
#Second possibility: dual mode if more attributes than examples
else:
svals, evecs = decomposition.decomposeKernelMatrix(K)
U, Z = None, None
#Third possibility, primal decomposition
else:
#Invoking getPrimalDataMatrix adds the bias feature
X = getPrimalDataMatrix(self.X, self.bias)
svals, evecs, U = decomposition.decomposeDataMatrix(X.T)
U, Z = None, None
return svals, evecs, U, Z
def decompositionFromPool(self, rpool):
kernel = rpool[data_sources.KERNEL_OBJ]
self.X = rpool[data_sources.TRAIN_FEATURES]
if rpool.has_key(data_sources.BASIS_VECTORS):
bvectors = rpool[data_sources.BASIS_VECTORS]
else:
bvectors = None
if "bias" in rpool:
self.bias = float(rpool["bias"])
else:
self.bias = 0.
if bvectors != None or self.X.shape[1] > self.X.shape[0]:
K = kernel.getKM(self.X).T
#First possibility: subset of regressors has been invoked
if bvectors != None:
svals, evecs, U, Z = decomposition.decomposeSubsetKM(K, bvectors)
#Second possibility: dual mode if more attributes than examples
else:
svals, evecs = decomposition.decomposeKernelMatrix(K)
U, Z = None, None
#Third possibility, primal decomposition
else:
#Invoking getPrimalDataMatrix adds the bias feature
X = getPrimalDataMatrix(self.X,self.bias)
svals, evecs, U = decomposition.decomposeDataMatrix(X.T)
U, Z = None, None
return svals, evecs, U, Z
def decompositionFromPool(self, rpool):
kernel = rpool['kernel_obj']
self.X = array_tools.as_2d_array(rpool['X'], True)
if rpool.has_key('basis_vectors'):
basis_vectors = array_tools.as_2d_array(rpool['basis_vectors'], True)
if not self.X.shape[1] == basis_vectors.shape[1]:
raise Exception("X and basis_vectors have different number of columns")
else:
basis_vectors = None
if "bias" in rpool:
self.bias = float(rpool["bias"])
else:
self.bias = 1.
if basis_vectors != None or self.X.shape[1] > self.X.shape[0]:
#First possibility: subset of regressors has been invoked
if basis_vectors != None:
K_r = kernel.getKM(self.X).T
Krr = kernel.getKM(basis_vectors)
svals, evecs, U, Z = decomposition.decomposeSubsetKM(K_r, Krr)
#Second possibility: dual mode if more attributes than examples
else:
K = kernel.getKM(self.X).T
svals, evecs = decomposition.decomposeKernelMatrix(K)
U, Z = None, None
#Third possibility, primal decomposition
else:
#Invoking getPrimalDataMatrix adds the bias feature
X = getPrimalDataMatrix(self.X,self.bias)
svals, evecs, U = decomposition.decomposeDataMatrix(X.T)
U, Z = None, None
return svals, evecs, U, Z
def decompositionFromPool(self, rpool):
"""Builds decomposition representing the training data from resource pool.
Default implementation
builds and decomposes the kernel matrix itself (standard case), or the
empirical kernel map of the training data, if reduced set approximation is
used. Inheriting classes may also re-implement this by decomposing the feature
map of the data (e.g. linear kernel with low-dimensional data).
@param rpool: resource pool
@type rpool: dict
@return: svals, evecs, U, Z
@rtype: tuple of numpy matrices
"""
train_X = rpool['X']
kernel = rpool['kernel_obj']
if rpool.has_key('basis_vectors'):
basis_vectors = rpool['basis_vectors']
if not train_X.shape[1] == basis_vectors.shape[1]:
raise Exception("X and basis_vectors have different number of columns")
K_r = kernel.getKM(train_X).T
Krr = kernel.getKM(basis_vectors)
svals, evecs, U, Z = decomposition.decomposeSubsetKM(K_r, Krr)
else:
K = kernel.getKM(train_X).T
svals, evecs = decomposition.decomposeKernelMatrix(K)
U, Z = None, None
return svals, evecs, U, Z
def decompositionFromPool(self, rpool):
K_train = rpool['kernel_matrix']
if rpool.has_key('basis_vectors'):
svals, rsvecs, U, Z = decomposition.decomposeSubsetKM(K_train, rpool['basis_vectors'])
else:
svals, rsvecs = decomposition.decomposeKernelMatrix(K_train)
U, Z = None, None
return svals, rsvecs, U, Z
def decompositionFromPool(self, rpool):
K_train = rpool[data_sources.KMATRIX]
if rpool.has_key(data_sources.BASIS_VECTORS):
svals, rsvecs, U, Z = decomposition.decomposeSubsetKM(K_train, rpool[data_sources.BASIS_VECTORS])
else:
svals, rsvecs = decomposition.decomposeKernelMatrix(K_train)
U, Z = None, None
return svals, rsvecs, U, Z
def decompositionFromPool(self, rpool):
K_train = rpool[data_sources.KMATRIX]
if rpool.has_key(data_sources.BASIS_VECTORS):
svals, rsvecs, U, Z = decomposition.decomposeSubsetKM(
K_train, rpool[data_sources.BASIS_VECTORS])
else:
svals, rsvecs = decomposition.decomposeKernelMatrix(K_train)
U, Z = None, None
return svals, rsvecs, U, Z
def decompositionFromPool(self, rpool):
K_train = rpool['kernel_matrix']
if rpool.has_key('basis_vectors'):
svals, rsvecs, U, Z = decomposition.decomposeSubsetKM(
K_train, rpool['basis_vectors'])
else:
svals, rsvecs = decomposition.decomposeKernelMatrix(K_train)
U, Z = None, None
return svals, rsvecs, U, Z
def decompositionFromPool(self, rpool):
K_train = rpool['kernel_matrix']
if rpool.has_key('basis_vectors'):
if not K_train.shape[1] == rpool["basis_vectors"].shape[1]:
raise Exception("When using basis vectors, both kernel matrices must contain equal number of columns")
svals, rsvecs, U, Z = decomposition.decomposeSubsetKM(K_train.T, rpool['basis_vectors'])
else:
svals, rsvecs = decomposition.decomposeKernelMatrix(K_train)
U, Z = None, None
return svals, rsvecs, U, Z
def decompositionFromPool(self, rpool):
"""Builds decomposition representing the training data from resource pool.
Default implementation
builds and decomposes the kernel matrix itself (standard case), or the
empirical kernel map of the training data, if reduced set approximation is
used. Inheriting classes may also re-implement this by decomposing the feature
map of the data (e.g. linear kernel with low-dimensional data).
@param rpool: resource pool
@type rpool: dict
@return: svals, evecs, U, Z
@rtype: tuple of numpy matrices
"""
train_X = rpool[data_sources.TRAIN_FEATURES]
kernel = rpool[data_sources.KERNEL_OBJ]
if rpool.has_key(data_sources.BASIS_VECTORS):
bvectors = rpool[data_sources.BASIS_VECTORS]
K = kernel.getKM(train_X).T
svals, evecs, U, Z = decomposition.decomposeSubsetKM(K, bvectors)
else:
K = kernel.getKM(train_X).T
svals, evecs = decomposition.decomposeKernelMatrix(K)
U, Z = None, None
return svals, evecs, U, Z
def decompositionFromPool(self, rpool):
"""Builds decomposition representing the training data from resource pool.
Default implementation
builds and decomposes the kernel matrix itself (standard case), or the
empirical kernel map of the training data, if reduced set approximation is
used. Inheriting classes may also re-implement this by decomposing the feature
map of the data (e.g. linear kernel with low-dimensional data).
@param rpool: resource pool
@type rpool: dict
@return: svals, evecs, U, Z
@rtype: tuple of numpy matrices
"""
train_X = rpool[data_sources.TRAIN_FEATURES]
kernel = rpool[data_sources.KERNEL_OBJ]
if rpool.has_key(data_sources.BASIS_VECTORS):
bvectors = rpool[data_sources.BASIS_VECTORS]
K = kernel.getKM(train_X).T
svals, evecs, U, Z = decomposition.decomposeSubsetKM(K, bvectors)
else:
K = kernel.getKM(train_X).T
svals, evecs = decomposition.decomposeKernelMatrix(K)
U, Z = None, None
return svals, evecs, U, Z
def decompositionFromPool(self, rpool):
"""Builds decomposition representing the training data from resource pool.
Default implementation
builds and decomposes the kernel matrix itself (standard case), or the
empirical kernel map of the training data, if reduced set approximation is
used. Inheriting classes may also re-implement this by decomposing the feature
map of the data (e.g. linear kernel with low-dimensional data).
@param rpool: resource pool
@type rpool: dict
@return: svals, evecs, U, Z
@rtype: tuple of numpy matrices
"""
train_X = rpool['train_features']
kernel = rpool['kernel_obj']
if rpool.has_key('basis_vectors'):
basis_vectors = rpool['basis_vectors']
K_r = kernel.getKM(train_X).T
Krr = kernel.getKM(basis_vectors)
svals, evecs, U, Z = decomposition.decomposeSubsetKM(K_r, Krr)
else:
K = kernel.getKM(train_X).T
svals, evecs = decomposition.decomposeKernelMatrix(K)
U, Z = None, None
return svals, evecs, U, Z
