def _validate_value(self, val):
"""Check that the value satisfies the leaf's symbolic attributes.
Parameters
----------
val : numeric type
The value assigned.
Returns
-------
numeric type
The value converted to the proper matrix type.
"""
if val is not None:
# Convert val to ndarray or sparse matrix.
val = intf.convert(val)
if intf.shape(val) != self.shape:
raise ValueError(
"Invalid dimensions %s for %s value." %
(intf.shape(val), self.__class__.__name__)
)
projection = self.project(val)
# ^ might be a numpy array, or sparse scipy matrix.
delta = np.abs(val - projection)
# ^ might be a numpy array, scipy matrix, or sparse scipy matrix.
if intf.is_sparse(delta): # is a scipy sparse matrix
is_close_enough = np.allclose(delta.data, 0)
# ^ only check for near-equality on nonzero values.
else:
delta = np.array(delta) # make sure we have a numpy array.
is_close_enough = np.allclose(delta, 0, atol=1e-8)
if not is_close_enough:
if self.attributes['nonneg']:
attr_str = 'nonnegative'
elif self.attributes['nonpos']:
attr_str = 'nonpositive'
elif self.attributes['diag']:
attr_str = 'diagonal'
elif self.attributes['PSD']:
attr_str = 'positive semidefinite'
elif self.attributes['NSD']:
attr_str = 'negative semidefinite'
elif self.attributes['imag']:
attr_str = 'imaginary'
else:
attr_str = ([k for (k, v) in self.attributes.items() if v] + ['real'])[0]
raise ValueError(
"%s value must be %s." % (self.__class__.__name__, attr_str)
)
return val
def _validate_value(self, val):
"""Check that the value satisfies the leaf's symbolic attributes.
Parameters
----------
val : numeric type
The value assigned.
Returns
-------
numeric type
The value converted to the proper matrix type.
"""
if val is not None:
# Convert val to ndarray or sparse matrix.
val = intf.convert(val)
if intf.shape(val) != self.shape:
raise ValueError("Invalid dimensions %s for %s value." %
(intf.shape(val), self.__class__.__name__))
projection = self.project(val)
# ^ might be a numpy array, or sparse scipy matrix.
delta = np.abs(val - projection)
# ^ might be a numpy array, scipy matrix, or sparse scipy matrix.
if intf.is_sparse(delta):
# ^ based on current implementation of project(...),
# is is not possible for this Leaf to be PSD/NSD *and*
# a sparse matrix.
close_enough = np.allclose(delta.data,
0,
atol=SPARSE_PROJECTION_TOL)
# ^ only check for near-equality on nonzero values.
else:
# the data could be a scipy matrix, or a numpy array.
# First we convert to a numpy array.
delta = np.array(delta)
# Now that we have the residual, we need to measure it
# in some canonical way.
if self.attributes['PSD'] or self.attributes['NSD']:
# For PSD/NSD Leafs, we use the largest-singular-value norm.
close_enough = LA.norm(delta,
ord=2) <= PSD_NSD_PROJECTION_TOL
else:
# For all other Leafs we use the infinity norm on
# the vectorized Leaf.
close_enough = np.allclose(delta,
0,
atol=GENERAL_PROJECTION_TOL)
if not close_enough:
if self.attributes['nonneg']:
attr_str = 'nonnegative'
elif self.attributes['pos']:
attr_str = 'positive'
elif self.attributes['nonpos']:
attr_str = 'nonpositive'
elif self.attributes['neg']:
attr_str = 'negative'
elif self.attributes['diag']:
attr_str = 'diagonal'
elif self.attributes['PSD']:
attr_str = 'positive semidefinite'
elif self.attributes['NSD']:
attr_str = 'negative semidefinite'
elif self.attributes['imag']:
attr_str = 'imaginary'
else:
attr_str = ([k
for (k, v) in self.attributes.items() if v] +
['real'])[0]
raise ValueError("%s value must be %s." %
(self.__class__.__name__, attr_str))
return val
