def sparse(self):
'''
Conversion of a FullTensor into a SparseTensor.
Returns
-------
tensap.SparseTensor
A SparseTensor representation of the FullTensor.
'''
dat = np.reshape(self.data, -1, order='F')
ind = np.nonzero(dat)[0]
indices = tensap.MultiIndices.ind2sub(self.shape, ind)
return tensap.SparseTensor(dat, indices, self.shape)
def sparse(self):
'''
Convert the DiagonalTensor to a tensap.SparseTensor.
Returns
-------
tensap.SparseTensor
The DiagonalTensor as a tensap.SparseTensor.
'''
ind = np.nonzero(self.data)[0]
data = self.data[self.data != 0]
indices = tensap.MultiIndices(
np.tile(np.reshape(ind, [-1, 1]), (1, self.order)))
return tensap.SparseTensor(data, indices, self.shape)
def eval_on_tensor_grid(self, x):
'''
Evaluate the Function on a grid x.
Parameters
----------
x : tensap.TensorGrid
The tensap.ensorGrid used for the evaluation..
Raises
------
NotImplementedError
If the method is not implemented.
ValueError
If x is not a tensap.TensorGrid object.
Returns
-------
fx : numpy.ndarray
The evaluation of the Function on the grid.
'''
x_a = x.array()
fx = self.eval(x_a)
if isinstance(x, tensap.SparseTensorGrid):
if np.all(self.output_shape == 1):
fx = tensap.SparseTensor(fx, x.indices, x.shape)
else:
raise NotImplementedError('Method not implemented.')
elif isinstance(x, tensap.FullTensorGrid):
if np.all(self.output_shape == 1):
shape = x.shape
if self.dim > 1:
fx = np.reshape(fx, shape, order='F')
else:
shape = np.concatenate((np.atleast_1d(x.shape),
np.atleast_1d(self.output_shape)))
fx = np.reshape(fx, shape, order='F')
fx = tensap.FullTensor(fx, np.size(shape), shape)
else:
raise ValueError('A TensorGrid object must be provided.')
return fx