classifiers.py 文件源码

def predict(self, input_sdr=None):
        """
        Argument inputs is ndarray of indexes into the input space.
        Returns probability of each catagory in output space.
        """
        self.input_sdr.assign(input_sdr)
        pdf = self.stats[self.input_sdr.flat_index]
        if True:
            # Combine multiple probabilities into single pdf. Product, not
            # summation, to combine probabilities of independant events. The
            # problem with this is if a few unexpected bits turn on it
            # mutliplies the result by zero, and the test dataset is going to
            # have unexpected things in it.  
            return np.product(pdf, axis=0, keepdims=False)
        else:
            # Use summation B/C it works well.
            return np.sum(pdf, axis=0, keepdims=False)