def getKM(self, X):
"""Returns the kernel matrix between the basis vectors and X.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
K : array, shape = [n_samples, n_bvectors]
kernel matrix
"""
X = array_tools.as_2d_array(X, True)
test_X = X
if sp.issparse(test_X):
test_X = array_tools.spmat_resize(test_X, self.train_X.shape[1])
else:
test_X = array_tools.as_dense_matrix(test_X)
gamma = self.gamma
m = self.train_X.shape[0]
n = test_X.shape[0]
#The Gaussian kernel matrix is constructed from a linear kernel matrix
linkm = self.train_X * test_X.T
linkm = array_tools.as_dense_matrix(linkm)
if sp.issparse(test_X):
test_norms = ((test_X.T.multiply(test_X.T)).sum(axis=0)).T
else:
test_norms = (np.multiply(test_X.T, test_X.T).sum(axis=0)).T
K = mat(np.ones((m, 1), dtype = float64)) * test_norms.T
K = K + self.train_norms * mat(np.ones((1, n), dtype = float64))
K = K - 2 * linkm
K = - gamma * K
K = np.exp(K)
return K.A.T
def getKM(self, X):
"""Returns the kernel matrix between the basis vectors and X.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
K : array, shape = [n_samples, n_bvectors]
kernel matrix
"""
X = array_tools.as_2d_array(X, True)
test_X = X
if sp.issparse(test_X):
test_X = array_tools.spmat_resize(test_X, self.train_X.shape[1])
else:
test_X = array_tools.as_dense_matrix(test_X)
gamma = self.gamma
m = self.train_X.shape[0]
n = test_X.shape[0]
#The Gaussian kernel matrix is constructed from a linear kernel matrix
linkm = self.train_X * test_X.T
linkm = array_tools.as_dense_matrix(linkm)
if sp.issparse(test_X):
test_norms = ((test_X.T.multiply(test_X.T)).sum(axis=0)).T
else:
test_norms = (np.multiply(test_X.T, test_X.T).sum(axis=0)).T
K = mat(np.ones((m, 1), dtype=float64)) * test_norms.T
K = K + self.train_norms * mat(np.ones((1, n), dtype=float64))
K = K - 2 * linkm
K = -gamma * K
K = np.exp(K)
return K.A.T
def getKM(self, X):
"""Returns the kernel matrix between the basis vectors and X.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
K : array, shape = [n_samples, n_bvectors]
kernel matrix
"""
test_X = X
degree, coef0, gamma = self.degree, self.coef0, self.gamma
if sp.issparse(test_X):
test_X = array_tools.spmat_resize(test_X, self.train_X.shape[1])
else:
test_X = array_tools.as_dense_matrix(test_X)
train_X = self.train_X
K = array_tools.as_array(train_X * test_X.T)
K *= gamma
K += coef0
K = K**degree
if self.bias != 0:
K += self.bias
return K.T
def getKM(self, X):
"""Returns the kernel matrix between the basis vectors and X.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
K : array, shape = [n_samples, n_bvectors]
kernel matrix
"""
X = array_tools.as_2d_array(X, True)
test_X = X
degree, coef0, gamma = self.degree, self.coef0, self.gamma
if sp.issparse(test_X):
test_X = array_tools.spmat_resize(test_X, self.train_X.shape[1])
else:
test_X = array_tools.as_dense_matrix(test_X)
train_X = self.train_X
K = array_tools.as_array(train_X * test_X.T)
K *= gamma
K += coef0
K = K ** degree
return K.T
def __init__(self, W, b):
"""Initializes a primal model
@param W: coefficients of the linear model, one column per task
@type W: numpy matrix
@param b: bias of the model, one column per task
@type b: numpy matrix
"""
self.W = array_tools.as_dense_matrix(W)
self.b = b
def getPrimalDataMatrix(X, bias):
"""
Constructs the feature representation of the data.
If bias is defined, a bias feature with value
sqrt(bias) is added to each example. This function
should be used when making predictions, or training
the primal formulation of the learner.
@param X: matrix containing the data
@type X: scipy.sparse.base.spmatrix
@param dimensionality: dimensionality of the feature space
(by default the number of rows in the data matrix)
@type dimensionality: integer
@return: data matrix
@rtype: scipy sparse matrix in csc format
"""
#if sp.issparse(X):
# X = X.todense()
X = array_tools.as_dense_matrix(X)
if bias!=0:
bias_slice = sqrt(bias)*ones((X.shape[0],1),dtype=float64)
X = np.hstack([X,bias_slice])
return X
def getPrimalDataMatrix(X, bias):
"""
Constructs the feature representation of the data.
If bias is defined, a bias feature with value
sqrt(bias) is added to each example. This function
should be used when making predictions, or training
the primal formulation of the learner.
@param X: matrix containing the data
@type X: scipy.sparse.base.spmatrix
@param dimensionality: dimensionality of the feature space
(by default the number of rows in the data matrix)
@type dimensionality: integer
@return: data matrix
@rtype: scipy sparse matrix in csc format
"""
#if sp.issparse(X):
# X = X.todense()
X = array_tools.as_dense_matrix(X)
if bias != 0:
bias_slice = sqrt(bias) * mat(ones((X.shape[0], 1), dtype=float64))
X = np.hstack([X, bias_slice])
return X
def getKM(self, X):
"""Returns the kernel matrix between the basis vectors and X.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
K : array, shape = [n_samples, n_bvectors]
kernel matrix
"""
test_X = X
if sp.issparse(test_X):
test_X = array_tools.spmat_resize(test_X, self.train_X.shape[1])
else:
test_X = array_tools.as_dense_matrix(test_X)
train_X = self.train_X
K = train_X * test_X.T
K = array_tools.as_array(K)
if self.bias != 0:
K += self.bias
return K.T
def getKM(self, X):
"""Returns the kernel matrix between the basis vectors and X.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
K : array, shape = [n_samples, n_bvectors]
kernel matrix
"""
X = array_tools.as_2d_array(X, True)
test_X = X
if sp.issparse(test_X):
test_X = array_tools.spmat_resize(test_X, self.train_X.shape[1])
else:
test_X = array_tools.as_dense_matrix(test_X)
train_X = self.train_X
K = train_X * test_X.T
K = array_tools.as_array(K)
if self.bias != 0:
K += self.bias
return K.T
