def test_normalize_by_average_weight():
store = Store([1, 2, 3], [1, 2, 3], 3, min_weight=-10, max_weight=10, average_weight=2)
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [1, 2, 3]
store.increase(0)
store.normalize()
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [1, 1, 2]
def test_normalize_by_default_average_weight():
store = Store([1, 2, 3], [1, 2, 3], 3, min_weight=-10, max_weight=10)
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [-1, 0, 1]
store.increase(0)
store.normalize()
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [-1, -1, 0]
def test_normalize_by_default_average_weight():
store = Store([1, 2, 3], [1, 2, 3], 3, min_weight=-10, max_weight=10)
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [-1, 0, 1]
store.increase(0)
store.normalize()
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [-1, -1, 0]
def test_add():
store = Store([], [], 5, min_weight=-10, max_weight=10)
store.add(1)
assert store.get_objects() == [1]
assert store.get_weights() == [0]
obj = {1: 2}
store.add(obj)
assert store.get_objects() == [1, obj]
assert store.get_weights() == [0, 0]
def test_increase():
store = Store([1, 2, 3], [10, -10, -10], 3, min_weight=-10, max_weight=10)
store.increase(0)
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [11, -10, -10]
store.increase(2)
store.increase(2)
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [11, -10, -8]
def test_add():
store = Store([], [], 5, min_weight=-10, max_weight=10)
store.add(1)
assert store.get_objects() == [1]
assert store.get_weights() == [0]
obj = {1: 2}
store.add(obj)
assert store.get_objects() == [1, obj]
assert store.get_weights() == [0, 0]
def test_increase():
store = Store([1, 2, 3], [10, -10, -10], 3, min_weight=-10, max_weight=10)
store.increase(0)
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [11, -10, -10]
store.increase(2)
store.increase(2)
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [11, -10, -8]
def test_normalize_by_average_weight():
store = Store([1, 2, 3], [1, 2, 3],
3,
min_weight=-10,
max_weight=10,
average_weight=2)
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [1, 2, 3]
store.increase(0)
store.normalize()
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [1, 1, 2]
def test_normalize_by_max():
store = Store([1, 2, 3], [-10, -2, 10], 3, min_weight=-10, max_weight=10)
store.normalize()
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [-10, -2, 10] # nothing changed
store.increase(2)
# wight of 3 now is 11
store.normalize()
# 1 - already min weight
# 2 - decreased because decreasing all
# 3 - decreased from 11 to 10
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [-10, -3, 10]
def test_normalize_by_max():
store = Store([1, 2, 3], [-10, -2, 10], 3, min_weight=-10, max_weight=10)
store.normalize()
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [-10, -2, 10] # nothing changed
store.increase(2)
# wight of 3 now is 11
store.normalize()
# 1 - already min weight
# 2 - decreased because decreasing all
# 3 - decreased from 11 to 10
assert store.get_objects() == [1, 2, 3]
assert store.get_weights() == [-10, -3, 10]
class Core(object):
KEY = "core"
def __init__(self, conf, data=None):
self.conf = conf
patterns = map(Matrix, data["patterns"]) if data else []
pattern_weights = data["pattern_weights"] if data else []
self._patterns_store = Store(
patterns,
pattern_weights,
self.conf.UNIT_OUTPUT_WIDTH,
min_weight=self.conf.UNIT_MIN_PATTERN_WEIGHT,
max_weight=self.conf.UNIT_MAX_PATTERN_WEIGHT,
average_weight=self.conf.UNIT_AVERAGE_PATTERN_WEIGHT,
)
def get_data(self):
return {
"_key": self.KEY,
"patterns": [p.get_data() for p in self._patterns_store.get_objects()],
"pattern_weights": self._patterns_store.get_weights(),
}
def activate(self, message, prediction=None, learn=True, activation_threshold=None):
"""
Activate self with message, perform learning
return result_signal if activated else None
:param message: (Matrix) message to activate
:param learn: (bool) if True learn patterns with this message
:param activation_threshold: possibility to override conf.UNIT_ACTIVATION_THRESHOLD
for this method
"""
# TODO: add prediction
result_signal = self._activate(message, activation_threshold)
if learn and result_signal:
self._learn(message, result_signal)
return result_signal
def _activate(self, message, activation_threshold=None):
activation_threshold = activation_threshold or self.conf.UNIT_ACTIVATION_THRESHOLD
result_signal = [0.0] * self.conf.UNIT_OUTPUT_WIDTH
for i, pattern in enumerate(self._patterns_store.get_objects()):
activity = message.average_similarity(pattern)
result_signal[i] = activity
activated = max(result_signal) >= activation_threshold
return result_signal if activated else None
def decode(self, signal, activation_threshold=None):
assert len(signal) == self.conf.UNIT_OUTPUT_WIDTH
candidates = []
for activity, pattern in zip(signal, self._patterns_store.get_objects()):
candidate = matrix_multiply(pattern, activity)
candidate_activity = self._activate(candidate, activation_threshold)
if candidate_activity:
candidates.append((candidate_activity, candidate))
if not candidates:
return None
return max(candidates, key=lambda item: item[0])[1]
def add_patterns(self, new_patterns):
for pattern in new_patterns:
self._patterns_store.add(pattern)
def _learn(self, message, output_signal):
max_activity = max(output_signal)
for i, activity in enumerate(output_signal):
if activity == max_activity:
self._patterns_store.increase(i)
pattern = self._patterns_store.get_objects()[i]
pattern.approximate(message, activity * self.conf.UNIT_LEARNING_FACTOR)
self._patterns_store.normalize()
def test_add_to_full_store_remove_oldest():
store = Store([1, 2, 3], [0, 0, 0], 3, min_weight=-10, max_weight=10)
store.add(4)
assert store.get_objects() == [2, 3, 4]
assert store.get_weights() == [0, 0, 0]
def test_add_to_full_store():
store = Store([1, 2, 3], [-1, -2, 1], 3, min_weight=-10, max_weight=10)
store.add(4)
assert store.get_objects() == [1, 3, 4]
assert store.get_weights() == [-1, 1, 0]
def test_add_to_full_store_remove_oldest():
store = Store([1, 2, 3], [0, 0, 0], 3, min_weight=-10, max_weight=10)
store.add(4)
assert store.get_objects() == [2, 3, 4]
assert store.get_weights() == [0, 0, 0]
def test_add_to_full_store():
store = Store([1, 2, 3], [-1, -2, 1], 3, min_weight=-10, max_weight=10)
store.add(4)
assert store.get_objects() == [1, 3, 4]
assert store.get_weights() == [-1, 1, 0]
class Core(object):
KEY = 'core'
def __init__(self, conf, data=None):
self.conf = conf
patterns = map(Matrix, data['patterns']) if data else []
pattern_weights = data['pattern_weights'] if data else []
self._patterns_store = Store(
patterns,
pattern_weights,
self.conf.UNIT_OUTPUT_WIDTH,
min_weight=self.conf.UNIT_MIN_PATTERN_WEIGHT,
max_weight=self.conf.UNIT_MAX_PATTERN_WEIGHT,
average_weight=self.conf.UNIT_AVERAGE_PATTERN_WEIGHT)
def get_data(self):
return {
'_key': self.KEY,
'patterns':
[p.get_data() for p in self._patterns_store.get_objects()],
'pattern_weights': self._patterns_store.get_weights()
}
def activate(self,
message,
prediction=None,
learn=True,
activation_threshold=None):
"""
Activate self with message, perform learning
return result_signal if activated else None
:param message: (Matrix) message to activate
:param learn: (bool) if True learn patterns with this message
:param activation_threshold: possibility to override conf.UNIT_ACTIVATION_THRESHOLD
for this method
"""
# TODO: add prediction
result_signal = self._activate(message, activation_threshold)
if learn and result_signal:
self._learn(message, result_signal)
return result_signal
def _activate(self, message, activation_threshold=None):
activation_threshold = activation_threshold or self.conf.UNIT_ACTIVATION_THRESHOLD
result_signal = [0.0] * self.conf.UNIT_OUTPUT_WIDTH
for i, pattern in enumerate(self._patterns_store.get_objects()):
activity = message.average_similarity(pattern)
result_signal[i] = activity
activated = (max(result_signal) >= activation_threshold)
return result_signal if activated else None
def decode(self, signal, activation_threshold=None):
assert len(signal) == self.conf.UNIT_OUTPUT_WIDTH
candidates = []
for activity, pattern in zip(signal,
self._patterns_store.get_objects()):
candidate = matrix_multiply(pattern, activity)
candidate_activity = self._activate(candidate,
activation_threshold)
if candidate_activity:
candidates.append((candidate_activity, candidate))
if not candidates:
return None
return max(candidates, key=lambda item: item[0])[1]
def add_patterns(self, new_patterns):
for pattern in new_patterns:
self._patterns_store.add(pattern)
def _learn(self, message, output_signal):
max_activity = max(output_signal)
for i, activity in enumerate(output_signal):
if activity == max_activity:
self._patterns_store.increase(i)
pattern = self._patterns_store.get_objects()[i]
pattern.approximate(message,
activity * self.conf.UNIT_LEARNING_FACTOR)
self._patterns_store.normalize()