def get_Phi(grid, X_train, svd=False):
def eval_op(x, op, size):
result_vec = sg.DataVector(size)
x = sg.DataVector(np.array(x).flatten())
op.mult(x, result_vec)
return result_vec.array().copy()
def eval_op_transpose(x, op, size):
result_vec = sg.DataVector(size)
x = sg.DataVector(np.array(x).flatten())
op.multTranspose(x, result_vec)
return result_vec.array().copy()
data_train = to_data_matrix(X_train)
num_elem = X_train.shape[0]
op = sg.createOperationMultipleEval(grid, data_train)
matvec = lambda x: eval_op(x, op, num_elem)
rmatvec = lambda x: eval_op_transpose(x, op, grid.getSize())
shape = (num_elem, grid.getSize())
linop = LinearOperator(shape, matvec, rmatvec, dtype='float64')
if svd:
k = min(grid.getSize(), X_train.shape[0])
_, s, _ = svds(linop, k=k - 1)
return s
else:
Phi = linop.matmat(np.matrix(np.identity(grid.getSize())))
return Phi
def get_Phi(X_train):
def eval_op(x, op, size):
result_vec = sg.DataVector(size)
x = sg.DataVector(np.array(x).flatten())
op.mult(x, result_vec)
return result_vec.array().copy()
def eval_op_transpose(x, op, size):
result_vec = sg.DataVector(size)
x = sg.DataVector(np.array(x).flatten())
op.multTranspose(x, result_vec)
return result_vec.array().copy()
num_elem = X_train.array().shape[0]
grid = sg.Grid.createModLinearGrid(10)
gen = grid.getGenerator()
gen.regular(2)
op = sg.createOperationMultipleEval(grid, X_train)
matvec = lambda x: eval_op(x, op, num_elem)
rmatvec = lambda x: eval_op_transpose(x, op, grid.getSize())
shape = (num_elem, grid.getSize())
linop = LinearOperator(shape, matvec, rmatvec, dtype='float64')
Phi = linop.matmat(np.matrix(np.identity(grid.getSize())))
return Phi
