neuroprob/inference.py
def validate_model(self, likelihood_set=True):
        """
        Checks model validity and consistency, after setting data.
        
        When setting up a model for sampling, call this method with likelihood_set depending on 
        whether spike trains have been loaded (needed for sampling conditional processes).
        """
        if self.inputs.XZ is None:
            raise ValueError('Inputs object has not been set with data using .set_XZ()')
        if likelihood_set:
            if self.likelihood.spikes is None:
                raise ValueError('Likelihood object has not been set with data using .set_Y()')
            if self.likelihood.tsteps != self.inputs.tsteps:
                raise ValueError('Input and output time dimensions do not match')
            if self.likelihood.filter_len != self.inputs.filter_len:
                raise ValueError('Input and likelihood filtering lengths do not match')
            if self.likelihood.batches != self.inputs.batches:
                raise ValueError('Input and output batching do not match')
            if self.likelihood.trials != self.inputs.trials:
                raise ValueError('Input and output trial numbers do not match')

        Checks model validity and consistency, after setting data.
        
        When setting up a model for sampling, call this method with likelihood_set depending on 
        whether spike trains have been loaded (needed for sampling conditional processes).
        
Checks model validity and consistency, after setting data.
When setting up a model for sampling, call this method with likelihood_set depending on
whether spike trains have been loaded (needed for sampling conditional processes).
Checks model validity and consistency after setting data . When setting up a model for sampling call this method with likelihood_set depending on whether spike trains have been loaded ( needed for sampling conditional processes ) .
def validate_model(self, likelihood_set=True):
        if self.inputs.XZ is None:
            raise ValueError('Inputs object has not been set with data using .set_XZ()')
        if likelihood_set:
            if self.likelihood.spikes is None:
                raise ValueError('Likelihood object has not been set with data using .set_Y()')
            if self.likelihood.tsteps != self.inputs.tsteps:
                raise ValueError('Input and output time dimensions do not match')
            if self.likelihood.filter_len != self.inputs.filter_len:
                raise ValueError('Input and likelihood filtering lengths do not match')
            if self.likelihood.batches != self.inputs.batches:
                raise ValueError('Input and output batching do not match')
            if self.likelihood.trials != self.inputs.trials:
                raise ValueError('Input and output trial numbers do not match')
def validate_model ( self , likelihood_set = True ) : if self . inputs . XZ is None : raise ValueError ( 'Inputs object has not been set with data using .set_XZ()' ) if likelihood_set : if self . likelihood . spikes is None : raise ValueError ( 'Likelihood object has not been set with data using .set_Y()' ) if self . likelihood . tsteps != self . inputs . tsteps : raise ValueError ( 'Input and output time dimensions do not match' ) if self . likelihood . filter_len != self . inputs . filter_len : raise ValueError ( 'Input and likelihood filtering lengths do not match' ) if self . likelihood . batches != self . inputs . batches : raise ValueError ( 'Input and output batching do not match' ) if self . likelihood . trials != self . inputs . trials : raise ValueError ( 'Input and output trial numbers do not match' )
Checks model validity and consistency, after setting data.
Checks model validity and consistency after setting data .
"""
        Checks model validity and consistency, after setting data.
        
        When setting up a model for sampling, call this method with likelihood_set depending on 
        whether spike trains have been loaded (needed for sampling conditional processes).
        """
self
likelihood_set