def get_weight_variance(self, weights): """ Get the variance of the weights. """ if get_simulator().is_a_pynn_random(weights): return utility_calls.get_variance(weights) elif numpy.isscalar(weights): return 0.0 elif hasattr(weights, "__getitem__"): return numpy.var(weights) raise Exception("Unrecognised weight format")
def get_weight_variance(self, weights): """ Get the variance of the weights. """ if get_simulator().is_a_pynn_random(weights): return utility_calls.get_variance(weights) elif numpy.isscalar(weights): return 0.0 elif hasattr(weights, "__getitem__"): return numpy.var(weights) raise Exception("Unrecognised weight format")
def get_delay_variance(self, delays): """ Get the variance of the delays. """ if get_simulator().is_a_pynn_random(delays): return utility_calls.get_variance(delays) elif numpy.isscalar(delays): return 0.0 elif hasattr(delays, "__getitem__"): return numpy.var(delays) raise Exception("Unrecognised delay format")
def get_delay_variance(self, delays): """ Get the variance of the delays. """ if get_simulator().is_a_pynn_random(delays): return utility_calls.get_variance(delays) elif numpy.isscalar(delays): return 0.0 elif hasattr(delays, "__getitem__"): return numpy.var(delays) raise Exception("Unrecognised delay format")
def _get_delay_variance(delays, connection_slices): """ Get the variance of the delays """ if isinstance(delays, RandomDistribution): return utility_calls.get_variance(delays) elif numpy.isscalar(delays): return 0.0 elif hasattr(delays, "__getitem__"): return numpy.var([ delays[connection_slice] for connection_slice in connection_slices]) raise Exception("Unrecognised delay format")
def _get_weight_variance(weights, connection_slices): """ Get the variance of the weights """ if isinstance(weights, RandomDistribution): return utility_calls.get_variance(weights) elif numpy.isscalar(weights): return 0.0 elif hasattr(weights, "__getitem__"): return numpy.var([ numpy.abs(weights[connection_slice]) for connection_slice in connection_slices]) raise Exception("Unrecognised weight format")
def _get_weight_variance(weights, connection_slices): """ Get the variance of the weights """ if get_simulator().is_a_pynn_random(weights): return utility_calls.get_variance(weights) elif numpy.isscalar(weights): return 0.0 elif hasattr(weights, "__getitem__"): return numpy.var([ numpy.abs(weights[connection_slice]) for connection_slice in connection_slices]) raise Exception("Unrecognised weight format")
def _get_delay_variance(delays, connection_slices): """ Get the variance of the delays """ if get_simulator().is_a_pynn_random(delays): return utility_calls.get_variance(delays) elif numpy.isscalar(delays): return 0.0 elif hasattr(delays, "__getitem__"): return numpy.var([ delays[connection_slice] for connection_slice in connection_slices]) raise Exception("Unrecognised delay format")
def _get_weight_variance(weights, connection_slices): """ Get the variance of the weights """ if isinstance(weights, RandomDistribution): return utility_calls.get_variance(weights) elif numpy.isscalar(weights): return 0.0 elif hasattr(weights, "__getitem__"): return numpy.var([ weights[connection_slice] for connection_slice in connection_slices ]) raise Exception("Unrecognised weight format")
def get_weight_variance(self, weights): """ Get the variance of the weights. :param weights: :type weights: ~numpy.ndarray or ~pyNN.random.NumpyRNG or int or float or list(int) or list(float) :rtype: float """ if isinstance(weights, RandomDistribution): return utility_calls.get_variance(weights) elif numpy.isscalar(weights): return 0.0 elif hasattr(weights, "__getitem__"): return numpy.var(weights) raise Exception("Unrecognised weight format")
def get_weight_variance(self, weights, synapse_info): """ Get the variance of the weights. :param weights: :type weights: ~numpy.ndarray or ~pyNN.random.NumpyRNG or int or float or list(int) or list(float) :rtype: float """ if isinstance(weights, RandomDistribution): return utility_calls.get_variance(weights) elif isinstance(weights, str): d = self._get_distances(weights, synapse_info) return numpy.var(_expr_context.eval(weights, d=d)) elif numpy.isscalar(weights): return 0.0 elif hasattr(weights, "__getitem__"): return numpy.var(weights) raise SpynnakerException("Unrecognised weight format")