pyclustering/nnet/hysteresis.py
def _calculate_states(self, solution, t, step, int_step):
        """!
        @brief Calculates new states for neurons using differential calculus. Returns new states for neurons.
        
        @param[in] solution (solve_type): Type solver of the differential equation.
        @param[in] t (double): Current time of simulation.
        @param[in] step (double): Step of solution at the end of which states of oscillators should be calculated.
        @param[in] int_step (double): Step differentiation that is used for solving differential equation.
        
        @return (list) New states for neurons.
        
        """
        
        next_states = [0] * self._num_osc;
        
        for index in range (0, self._num_osc, 1):            
            result = odeint(self._neuron_states, self._states[index], numpy.arange(t - step, t, int_step), (index , ));
            next_states[index] = result[len(result) - 1][0];
        
        self._outputs = [val for val in self._outputs_buffer];
        return next_states;
def _calculate_states(self, solution, t, step, int_step):
        """!
        @brief Calculates new states for neurons using differential calculus. Returns new states for neurons.
        
        @param[in] solution (solve_type): Type solver of the differential equation.
        @param[in] t (double): Current time of simulation.
        @param[in] step (double): Step of solution at the end of which states of oscillators should be calculated.
        @param[in] int_step (double): Step differentiation that is used for solving differential equation.
        
        @return (list) New states for neurons.
        
        """
        
        next_states = [0] * self._num_osc;
        
        for index in range (0, self._num_osc, 1):            
            result = odeint(self._neuron_states, self._states[index], numpy.arange(t - step, t, int_step), (index , ));
            next_states[index] = result[len(result) - 1][0];
        
        self._outputs = [val for val in self._outputs_buffer];
        return next_states;
def _calculate_states ( self , solution , t , step , int_step ) : next_states = [ 0 ] * self . _num_osc for index in range ( 0 , self . _num_osc , 1 ) : result = odeint ( self . _neuron_states , self . _states [ index ] , numpy . arange ( t - step , t , int_step ) , ( index , ) ) next_states [ index ] = result [ len ( result ) - 1 ] [ 0 ] self . _outputs = [ val for val in self . _outputs_buffer ] return next_states
!
        @brief Calculates new states for neurons using differential calculus. Returns new states for neurons.
        
        @param[in] solution (solve_type): Type solver of the differential equation.
        @param[in] t (double): Current time of simulation.
        @param[in] step (double): Step of solution at the end of which states of oscillators should be calculated.
        @param[in] int_step (double): Step differentiation that is used for solving differential equation.
        
        @return (list) New states for neurons.
!