def simulate(self, steps, stimulus):
"""!
@brief Performs static simulation of pulse coupled neural network using.
@param[in] steps (uint): Number steps of simulations during simulation.
@param[in] stimulus (list): Stimulus for oscillators, number of stimulus should be equal to number of oscillators.
@return (pcnn_dynamic) Dynamic of oscillatory network - output of each oscillator on each step of simulation.
"""
if len(stimulus) != len(self):
raise NameError(
'Number of stimulus should be equal to number of oscillators. Each stimulus corresponds to only one oscillators.'
)
if self.__ccore_pcnn_pointer is not None:
ccore_instance_dynamic = wrapper.pcnn_simulate(
self.__ccore_pcnn_pointer, steps, stimulus)
return pcnn_dynamic(None, ccore_instance_dynamic)
dynamic = []
dynamic.append(self._outputs)
for step in range(1, steps, 1):
self._outputs = self._calculate_states(stimulus)
dynamic.append(self._outputs)
return pcnn_dynamic(dynamic)
def simulate(self, steps, stimulus):
"""!
@brief Performs static simulation of pulse coupled neural network using.
@param[in] steps (uint): Number steps of simulations during simulation.
@param[in] stimulus (list): Stimulus for oscillators, number of stimulus should be equal to number of oscillators.
@return (pcnn_dynamic) Dynamic of oscillatory network - output of each oscillator on each step of simulation.
"""
if (len(stimulus) != len(self)):
raise NameError('Number of stimulus should be equal to number of oscillators. Each stimulus corresponds to only one oscillators.');
if (self.__ccore_pcnn_pointer is not None):
ccore_instance_dynamic = wrapper.pcnn_simulate(self.__ccore_pcnn_pointer, steps, stimulus);
return pcnn_dynamic(None, ccore_instance_dynamic);
dynamic = [];
dynamic.append(self._outputs);
for step in range(1, steps, 1):
self._outputs = self._calculate_states(stimulus);
dynamic.append(self._outputs);
return pcnn_dynamic(dynamic);