def train(self, samples):
"""!
@brief Trains syncpr network using Hebbian rule for adjusting strength of connections between oscillators during training.
@param[in] samples (list): list of patterns where each pattern is represented by list of features that are equal to [-1; 1].
"""
# Verify pattern for learning
for pattern in samples:
self.__validate_pattern(pattern)
if (self._ccore_network_pointer is not None):
return wrapper.syncpr_train(self._ccore_network_pointer, samples)
length = len(self)
number_samples = len(samples)
for i in range(length):
for j in range(i + 1, len(self), 1):
# go through via all patterns
for p in range(number_samples):
value1 = samples[p][i]
value2 = samples[p][j]
self._coupling[i][j] += value1 * value2
self._coupling[i][j] /= length
self._coupling[j][i] = self._coupling[i][j]
def train(self, samples):
"""!
@brief Trains syncpr network using Hebbian rule for adjusting strength of connections between oscillators during training.
@param[in] samples (list): list of patterns where each pattern is represented by list of features that are equal to [-1; 1].
"""
# Verify pattern for learning
for pattern in samples:
self.__validate_pattern(pattern);
if (self._ccore_network_pointer is not None):
return wrapper.syncpr_train(self._ccore_network_pointer, samples);
length = len(self);
number_samples = len(samples);
for i in range(length):
for j in range(i + 1, len(self), 1):
# go through via all patterns
for p in range(number_samples):
value1 = samples[p][i];
value2 = samples[p][j];
self._coupling[i][j] += value1 * value2;
self._coupling[i][j] /= length;
self._coupling[j][i] = self._coupling[i][j];