def test_adjustSynapses(self):
for (name, cells, inputs, adjustedSegments, activeInputs,
initialPermanence, activeDelta, inactiveDelta,
connectedPermanence,
expected) in (("Basic test", [1, 2, 3], [42, 43, 44], [0, 2],
[42, 44], 0.45, 0.1, -0.1, 0.5, [2, 0, 2]),
("Reward inactive", [1, 2, 3], [42, 43, 44], [0, 2],
[42, 44], 0.45, -0.1, 0.1, 0.5, [1, 0, 1]),
("No segments", [1, 2, 3], [42, 43, 44], [],
[42, 44], 0.45, 0.1, -0.1, 0.5, [0, 0, 0]),
("No active synapses", [1, 2, 3], [42, 43, 44],
[0, 2], [], 0.45, 0.1, -0.1, 0.5, [0, 0, 0]),
("Delta of zero", [1, 2, 3], [42, 43, 44], [0, 2],
[42, 44], 0.55, 0.0, 0.0, 0.5, [3, 3, 3])):
connections = SparseMatrixConnections(2048, 2048)
segments = connections.createSegments(cells)
connections.growSynapses(segments, inputs, initialPermanence)
connections.adjustSynapses(segments[adjustedSegments],
activeInputs, activeDelta,
inactiveDelta)
overlaps = connections.computeActivity(inputs, connectedPermanence)
np.testing.assert_equal(overlaps[segments], expected, name)
def test_adjustSynapses(self):
for (name, cells, inputs,
adjustedSegments, activeInputs,
initialPermanence, activeDelta, inactiveDelta, connectedPermanence,
expected) in (("Basic test",
[1, 2, 3], [42, 43, 44],
[0, 2], [42, 44],
0.45, 0.1, -0.1, 0.5,
[2, 0, 2]),
("Reward inactive",
[1, 2, 3], [42, 43, 44],
[0, 2], [42, 44],
0.45, -0.1, 0.1, 0.5,
[1, 0, 1]),
("No segments",
[1, 2, 3], [42, 43, 44],
[], [42, 44],
0.45, 0.1, -0.1, 0.5,
[0, 0, 0]),
("No active synapses",
[1, 2, 3], [42, 43, 44],
[0, 2], [],
0.45, 0.1, -0.1, 0.5,
[0, 0, 0]),
("Delta of zero",
[1, 2, 3], [42, 43, 44],
[0, 2], [42, 44],
0.55, 0.0, 0.0, 0.5,
[3, 3, 3])
):
connections = SparseMatrixConnections(2048, 2048)
segments = connections.createSegments(cells)
connections.growSynapses(segments, inputs, initialPermanence)
connections.adjustSynapses(segments[adjustedSegments],
activeInputs, activeDelta, inactiveDelta)
overlaps = connections.computeActivity(inputs, connectedPermanence)
np.testing.assert_equal(overlaps[segments], expected, name)
def test_whenPermanenceFallsBelowZero(self):
connections = SparseMatrixConnections(2048, 2048)
segments = connections.createSegments([1, 2, 3])
connections.growSynapses(segments, [42, 43], 0.05)
connections.adjustSynapses(segments, [42, 43], -0.06, 0.0)
np.testing.assert_equal(
connections.mapSegmentsToSynapseCounts(segments), [0, 0, 0])
connections.growSynapses(segments, [42, 43], 0.05)
connections.adjustSynapses(segments, [], 0.0, -0.06)
np.testing.assert_equal(
connections.mapSegmentsToSynapseCounts(segments), [0, 0, 0])
connections.growSynapses(segments, [42, 43], 0.05)
connections.adjustActiveSynapses(segments, [42, 43], -0.06)
np.testing.assert_equal(
connections.mapSegmentsToSynapseCounts(segments), [0, 0, 0])
connections.growSynapses(segments, [42, 43], 0.05)
connections.adjustInactiveSynapses(segments, [], -0.06)
np.testing.assert_equal(
connections.mapSegmentsToSynapseCounts(segments), [0, 0, 0])
def test_whenPermanenceFallsBelowZero(self):
connections = SparseMatrixConnections(2048, 2048)
segments = connections.createSegments([1, 2, 3])
connections.growSynapses(segments, [42, 43], 0.05)
connections.adjustSynapses(segments, [42, 43], -0.06, 0.0)
np.testing.assert_equal(connections.mapSegmentsToSynapseCounts(segments),
[0, 0, 0])
connections.growSynapses(segments, [42, 43], 0.05)
connections.adjustSynapses(segments, [], 0.0, -0.06)
np.testing.assert_equal(connections.mapSegmentsToSynapseCounts(segments),
[0, 0, 0])
connections.growSynapses(segments, [42, 43], 0.05)
connections.adjustActiveSynapses(segments, [42, 43], -0.06)
np.testing.assert_equal(connections.mapSegmentsToSynapseCounts(segments),
[0, 0, 0])
connections.growSynapses(segments, [42, 43], 0.05)
connections.adjustInactiveSynapses(segments, [], -0.06)
np.testing.assert_equal(connections.mapSegmentsToSynapseCounts(segments),
[0, 0, 0])
