def predictWithKernelMatrices(self, K1pred, K2pred, row_inds = None, col_inds = None):
"""Computes predictions for test examples.
Parameters
----------
K1pred: {array-like, sparse matrix}, shape = [n_samples1, n_basis_functions1]
the first part of the test data matrix
K2pred: {array-like, sparse matrix}, shape = [n_samples2, n_basis_functions2]
the second part of the test data matrix
Returns
----------
P: array, shape = [n_samples1, n_samples2]
predictions
"""
if row_inds == None:
P = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(self.A, K1pred, K2pred.T, self.label_row_inds, self.label_col_inds)
P = P.reshape((K1pred.shape[0], K2pred.shape[0]), order='F')
else:
P = sparse_kronecker_multiplication_tools_python.x_gets_C_times_M_kron_N_times_B_times_v(
self.A,
K2pred,
K1pred,
np.array(row_inds, dtype = np.int32),
np.array(col_inds, dtype = np.int32),
self.label_row_inds,
self.label_col_inds)
P = np.array(P)
return P
def predictWithKernelMatrices(self,
K1pred,
K2pred,
row_inds=None,
col_inds=None):
"""Computes predictions for test examples.
Parameters
----------
K1pred: {array-like, sparse matrix}, shape = [n_samples1, n_basis_functions1]
the first part of the test data matrix
K2pred: {array-like, sparse matrix}, shape = [n_samples2, n_basis_functions2]
the second part of the test data matrix
Returns
----------
P: array, shape = [n_samples1, n_samples2]
predictions
"""
if row_inds == None:
P = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(
self.A, K1pred, K2pred.T, self.label_row_inds,
self.label_col_inds)
P = P.reshape((K1pred.shape[0], K2pred.shape[0]), order='F')
else:
P = sparse_kronecker_multiplication_tools_python.x_gets_C_times_M_kron_N_times_B_times_v(
self.A, K2pred, K1pred, np.array(row_inds, dtype=np.int32),
np.array(col_inds, dtype=np.int32), self.label_row_inds,
self.label_col_inds)
return P
def mv(v):
v_after = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(
v, X1, X2.T, label_row_inds, label_col_inds)
v_after = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(
v_after, X1.T, X2, label_row_inds,
label_col_inds) + regparam * v
return v_after
def solve_linear(self, regparam):
self.regparam = regparam
X1 = self.resource_pool['xmatrix1']
X2 = self.resource_pool['xmatrix2']
self.X1, self.X2 = X1, X2
if 'maxiter' in self.resource_pool:
maxiter = int(self.resource_pool['maxiter'])
else:
maxiter = None
x1tsize, x1fsize = X1.shape #m, d
x2tsize, x2fsize = X2.shape #q, r
lsize = len(self.label_row_inds) #n
kronfcount = x1fsize * x2fsize
label_row_inds = np.array(self.label_row_inds, dtype=np.int32)
label_col_inds = np.array(self.label_col_inds, dtype=np.int32)
def mv(v):
v_after = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(
v, X1, X2.T, label_row_inds, label_col_inds)
v_after = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(
v_after, X1.T, X2, label_row_inds,
label_col_inds) + regparam * v
return v_after
def mvr(v):
raise Exception('You should not be here!')
return None
def cgcb(v):
self.W = v.reshape((x1fsize, x2fsize), order='F')
self.callback()
G = LinearOperator((kronfcount, kronfcount),
matvec=mv,
rmatvec=mvr,
dtype=np.float64)
v_init = np.array(self.Y).reshape(self.Y.shape[0])
v_init = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(
v_init, X1.T, X2, label_row_inds, label_col_inds)
v_init = np.array(v_init).reshape(kronfcount)
if self.resource_pool.has_key('warm_start'):
x0 = np.array(self.resource_pool['warm_start']).reshape(kronfcount,
order='F')
else:
x0 = None
self.W = bicgstab(G, v_init, x0=x0, maxiter=maxiter,
callback=cgcb)[0].reshape((x1fsize, x2fsize),
order='F')
self.model = LinearPairwiseModel(self.W, X1.shape[1], X2.shape[1])
self.finished()
def gradient(v, X1, X2, Y, rowind, colind, lamb):
#REPLACE
#P = np.dot(X,v)
#P = vecProd(X1, X2, v)
P = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(v, X2, X1.T, colind, rowind)
z = (1. - Y*P)
z = np.where(z>0, z, 0)
sv = np.nonzero(z)[0]
#map to rows and cols
rows = rowind[sv]
cols = colind[sv]
#A = -2*np.dot(X[sv].T, Y[sv])
A = - sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(Y[sv], X1.T, X2, rows, cols)
A = A.reshape(X2.shape[1], X1.shape[1]).T.ravel()
#B = 2 * np.dot(X[sv].T, np.dot(X[sv],v))
v_after = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(v, X2, X1.T, cols, rows)
v_after = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(v_after, X1.T, X2, rows, cols)
B = v_after.reshape(X2.shape[1], X1.shape[1]).T.ravel()
#print "FOOOBAAR"
return A + B + lamb*v
def hessian(v, p, X1, X2, Y, rowind, colind, lamb):
#P = np.dot(X,v)
#P = vecProd(X1, X2, v)
P = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(v, X2, X1.T, colind, rowind)
z = (1. - Y*P)
z = np.where(z>0, z, 0)
sv = np.nonzero(z)[0]
#map to rows and cols
rows = rowind[sv]
cols = colind[sv]
p_after = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(p, X2, X1.T, cols, rows)
p_after = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(p_after, X1.T, X2, rows, cols)
p_after = p_after.reshape(X2.shape[1], X1.shape[1]).T.ravel()
return p_after + lamb*p
def gradient(v):
#REPLACE
#P = np.dot(X,v)
#P = vecProd(X1, X2, v)
P = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(
v, X2, X1.T, colind, rowind)
z = (1. - Y * P)
z = np.where(z > 0, z, 0)
sv = np.nonzero(z)[0]
#map to rows and cols
rows = rowind[sv]
cols = colind[sv]
#A = -2*np.dot(X[sv].T, Y[sv])
A = -2 * sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(
Y[sv], X1.T, X2, rows, cols)
A = A.reshape(X2.shape[1], X1.shape[1]).T.ravel()
#B = 2 * np.dot(X[sv].T, np.dot(X[sv],v))
v_after = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(
v, X2, X1.T, cols, rows)
v_after = 2 * sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(
v_after, X1.T, X2, rows, cols)
B = v_after.reshape(X2.shape[1], X1.shape[1]).T.ravel()
#print "FOOOBAAR"
return A + B + lamb * v
def hessian(v, p):
#P = np.dot(X,v)
#P = vecProd(X1, X2, v)
P = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(
v, X2, X1.T, colind, rowind)
z = (1. - Y * P)
z = np.where(z > 0, z, 0)
sv = np.nonzero(z)[0]
#map to rows and cols
rows = rowind[sv]
cols = colind[sv]
p_after = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(
p, X2, X1.T, cols, rows)
p_after = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(
p_after, X1.T, X2, rows, cols)
p_after = p_after.reshape(X2.shape[1], X1.shape[1]).T.ravel()
return 2 * p_after + lamb * p
def solve_linear(self, regparam):
self.regparam = regparam
X1 = self.resource_pool['xmatrix1']
X2 = self.resource_pool['xmatrix2']
self.X1, self.X2 = X1, X2
if 'maxiter' in self.resource_pool: maxiter = int(self.resource_pool['maxiter'])
else: maxiter = None
x1tsize, x1fsize = X1.shape #m, d
x2tsize, x2fsize = X2.shape #q, r
lsize = len(self.label_row_inds) #n
kronfcount = x1fsize * x2fsize
label_row_inds = np.array(self.label_row_inds, dtype = np.int32)
label_col_inds = np.array(self.label_col_inds, dtype = np.int32)
def mv(v):
v_after = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(v, X1, X2.T, label_row_inds, label_col_inds)
v_after = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(v_after, X1.T, X2, label_row_inds, label_col_inds) + regparam * v
return v_after
def mvr(v):
raise Exception('You should not be here!')
return None
def cgcb(v):
self.W = v.reshape((x1fsize, x2fsize), order = 'F')
self.callback()
G = LinearOperator((kronfcount, kronfcount), matvec = mv, rmatvec = mvr, dtype = np.float64)
v_init = np.array(self.Y).reshape(self.Y.shape[0])
v_init = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(v_init, X1.T, X2, label_row_inds, label_col_inds)
v_init = np.array(v_init).reshape(kronfcount)
if self.resource_pool.has_key('warm_start'):
x0 = np.array(self.resource_pool['warm_start']).reshape(kronfcount, order = 'F')
else:
x0 = None
self.W = bicgstab(G, v_init, x0 = x0, maxiter = maxiter, callback = cgcb)[0].reshape((x1fsize, x2fsize), order='F')
self.model = LinearPairwiseModel(self.W, X1.shape[1], X2.shape[1])
self.finished()
def mv(v):
v_after = sparse_kronecker_multiplication_tools_python.x_gets_subset_of_A_kron_B_times_v(v, X1, X2.T, label_row_inds, label_col_inds)
v_after = sparse_kronecker_multiplication_tools_python.x_gets_A_kron_B_times_sparse_v(v_after, X1.T, X2, label_row_inds, label_col_inds) + regparam * v
return v_after
