app/corex.py
def calculate_latent(self, X_event):
        """"Calculate the probability distribution for hidden factors for each sample."""
        alpha_rep = np.repeat(self.alpha, self.dim_visible, axis=1)
        log_p_y_given_x_unnorm = (1. - self.balance) * self.log_p_y + np.transpose(np.dot(X_event.T, alpha_rep*self.log_marg), (1, 0, 2))

        return self.normalize_latent(log_p_y_given_x_unnorm)
Calculate the probability distribution for hidden factors for each sample.
Calculate the probability distribution for hidden factors for each sample.
Calculate the probability distribution for hidden factors for each sample .
def calculate_latent(self, X_event):
        alpha_rep = np.repeat(self.alpha, self.dim_visible, axis=1)
        log_p_y_given_x_unnorm = (1. - self.balance) * self.log_p_y + np.transpose(np.dot(X_event.T, alpha_rep*self.log_marg), (1, 0, 2))
        return self.normalize_latent(log_p_y_given_x_unnorm)
def calculate_latent ( self , X_event ) : alpha_rep = np . repeat ( self . alpha , self . dim_visible , axis = 1 ) log_p_y_given_x_unnorm = ( 1. - self . balance ) * self . log_p_y + np . transpose ( np . dot ( X_event . T , alpha_rep * self . log_marg ) , ( 1 , 0 , 2 ) ) return self . normalize_latent ( log_p_y_given_x_unnorm )
Calculate the probability distribution for hidden factors for each sample.
Calculate the probability distribution for hidden factors for each sample .
""""Calculate the probability distribution for hidden factors for each sample."""
self
X_event