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