def _validate_variable(self, variable, context=None):
"""Validate that variable has only one item: activation_input.
"""
# Skip LearningMechanism._validate_variable in call to super(), as it requires variable to have 3 items
variable = super(LearningMechanism, self)._validate_variable(variable, context)
if np.array(variable).ndim != 2 or not is_numeric(variable):
raise KohonenLearningMechanismError("Variable for {} ({}) must be a list with two items "
"or a 2d np.array, all of which may contain only numbers".
format(self.name, variable))
return variable
def _validate_variable(self, variable, context=None):
"""Validate that variable has only one item: activation_input.
"""
# Skip LearningMechanism._validate_variable in call to super(), as it requires variable to have 3 items
variable = super(LearningMechanism,
self)._validate_variable(variable, context)
# # MODIFIED 9/22/17 NEW: [HACK] JDC: 6/29/18 -> CAUSES DEFAULT variable [[0]] OR ANYTHING OF size=1 TO FAIL
# if np.array(np.squeeze(variable)).ndim != 1 or not is_numeric(variable):
# MODIFIED 6/29/18 NEWER JDC: ALLOW size=1, AND DEFER FAILURE TO LearningFunction IF enbale_learning=True
if np.array(variable)[0].ndim != 1 or not is_numeric(variable):
# MODIFIED 9/22/17 END
raise AutoAssociativeLearningMechanismError(
"Variable for {} ({}) must be "
"a list or 1d np.array containing only numbers".format(
self.name, variable))
return variable
