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")
