hmm_class.py 文件源码

def _beta_cal(self,observations,c_scale):
        # Calculate Beta maxtrix
        num_states = self.em_prob.shape[0]
        total_stages = len(observations)

        # Initialize values
        ob_ind = self.obs_map[ observations[total_stages-1] ]
        beta = np.asmatrix(np.zeros((num_states,total_stages)))

        # Handle beta base case
        beta[:,total_stages-1] = c_scale[total_stages-1]

        # Iteratively calculate beta(t) for all 't'
        for curr_t in range(total_stages-1,0,-1):
            ob_ind = self.obs_map[observations[curr_t]]
            beta[:,curr_t-1] = np.multiply( beta[:,curr_t] , self.em_prob[:,ob_ind] )
            beta[:,curr_t-1] = np.dot( self.trans_prob, beta[:,curr_t-1] )
            beta[:,curr_t-1] = np.multiply( beta[:,curr_t-1] , c_scale[curr_t -1 ] )

        # return the computed beta
        return beta