Esempio n. 1
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    def testAdaptShouldIncrementSynapses(self):
        """
    Test that connections are generated on predefined segments.
    """
        random = Random(1981)
        active_cells = np.array(random.sample(
            np.arange(0, NUM_CELLS, 1, dtype="uint32"), 40),
                                dtype="uint32")
        active_cells.sort()

        presynaptic_input = list(range(0, 10))
        presynaptic_input_set = set(presynaptic_input)
        inputSDR = SDR(1024)
        inputSDR.sparse = presynaptic_input

        connections = Connections(NUM_CELLS, 0.51)
        for i in range(NUM_CELLS):
            seg = connections.createSegment(i, 1)

        for cell in active_cells:
            segments = connections.segmentsForCell(cell)
            segment = segments[0]
            for c in presynaptic_input:
                connections.createSynapse(segment, c, 0.1)
            connections.adaptSegment(segment, inputSDR, 0.1, 0.001, True)

            presynamptic_cells = self._getPresynapticCells(
                connections, segment, 0.2)
            self.assertEqual(presynamptic_cells, presynaptic_input_set,
                             "Missing synapses")

            presynamptic_cells = self._getPresynapticCells(
                connections, segment, 0.3)
            self.assertEqual(presynamptic_cells, set(), "Too many synapses")
Esempio n. 2
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    def testNumSynapses(self):
        """
    Test that connections are generated on predefined segments.
    """
        random = Random(1981)
        active_cells = np.array(random.sample(
            np.arange(0, NUM_CELLS, 1, dtype="uint32"), 40),
                                dtype="uint32")
        active_cells.sort()

        presynaptic_input = list(range(0, 10))
        presynaptic_input_set = set(presynaptic_input)
        inputSDR = SDR(1024)
        inputSDR.sparse = presynaptic_input

        connections = Connections(NUM_CELLS, 0.3)
        for i in range(NUM_CELLS):
            seg = connections.createSegment(i, 1)

        for cell in active_cells:
            segments = connections.segmentsForCell(cell)
            segment = segments[0]
            for c in presynaptic_input:
                connections.createSynapse(segment, c, 0.1)

            connections.adaptSegment(segment, inputSDR, 0.1, 0.0, False)

            num_synapses = connections.numSynapses(segment)
            self.assertEqual(num_synapses, len(presynaptic_input),
                             "Missing synapses")

        self.assertEqual(connections.numSynapses(),
                         len(presynaptic_input) * 40, "Missing synapses")
Esempio n. 3
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    def testComputeActivity(self):
        """
    Test that connections are generated on predefined segments.
    """
        random = Random(1981)
        active_cells = np.array(random.sample(
            np.arange(0, NUM_CELLS, 1, dtype="uint32"), 40),
                                dtype="uint32")
        active_cells.sort()

        presynaptic_input = list(range(0, 10))
        presynaptic_input_set = set(presynaptic_input)
        inputSDR = SDR(1024)
        inputSDR.sparse = presynaptic_input
        l = len(presynaptic_input)

        connections = Connections(NUM_CELLS, 0.51, False)
        for i in range(NUM_CELLS):
            seg = connections.createSegment(i, 1)

        numActiveConnectedSynapsesForSegment = connections.computeActivity(
            inputSDR, False)
        for count in numActiveConnectedSynapsesForSegment:
            self.assertEqual(count, 0, "Segment should not be active")

        for cell in active_cells:
            segments = connections.segmentsForCell(cell)
            segment = segments[0]
            for c in presynaptic_input:
                connections.createSynapse(segment, c, 0.1)

        numActiveConnectedSynapsesForSegment = connections.computeActivity(
            inputSDR, False)
        for count in numActiveConnectedSynapsesForSegment:
            self.assertEqual(count, 0, "Segment should not be active")

        for cell in active_cells:
            segments = connections.segmentsForCell(cell)
            segment = segments[0]
            connections.adaptSegment(segment, inputSDR, 0.5, 0.0, False)

        active_cells_set = set(active_cells)
        numActiveConnectedSynapsesForSegment = connections.computeActivity(
            inputSDR, False)
        for cell, count in enumerate(numActiveConnectedSynapsesForSegment):
            if cell in active_cells_set:
                self.assertEqual(count, l, "Segment should be active")
            else:
                self.assertEqual(count, 0, "Segment should not be active")
Esempio n. 4
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    def testCreateSynapse(self):
        # empty connections, create segment and a synapse
        co = Connections(NUM_CELLS, 0.51)
        self.assertEqual(co.numSynapses(), 0)
        self.assertEqual(co.numSegments(), 0)

        # 1st, create a segment
        seg = co.createSegment(NUM_CELLS - 1, 1)
        self.assertEqual(co.numSegments(), 1)

        #1. create a synapse on that segment
        syn1 = co.createSynapse(seg, NUM_CELLS - 1, 0.52)
        self.assertEqual(pytest.approx(co.permanenceForSynapse(syn1)), 0.52)
        self.assertEqual(co.numSynapses(), 1)

        #2. creating a duplicit synapse should not crash!
        syn2 = co.createSynapse(seg, NUM_CELLS - 1, 0.52)
        self.assertEqual(syn1, syn2,
                         "creating duplicate synapses should return the same")
        self.assertEqual(co.numSynapses(), 1,
                         "Duplicate synapse, number should not increase")

        #3. create a different synapse
        syn3 = co.createSynapse(seg, 1, 0.52)
        self.assertNotEqual(
            syn1, syn3, "creating a different synapse must create a new one")
        self.assertEqual(co.numSynapses(), 2,
                         "New synapse should increase the number")

        #4. create existing synapse with a new value -> should update permanence
        #4.a lower permanence -> keep max()
        syn4 = co.createSynapse(
            seg, NUM_CELLS - 1,
            0.11)  #all the same just permanence is a lower val
        self.assertEqual(syn1, syn4, "just updating existing syn")
        self.assertEqual(co.numSynapses(), 2,
                         "Duplicate synapse, number should not increase")
        self.assertEqual(pytest.approx(co.permanenceForSynapse(syn1)), 0.52,
                         "update keeps the larger value")

        #4.b higher permanence -> update
        syn5 = co.createSynapse(
            seg, NUM_CELLS - 1,
            0.99)  #all the same just permanence is a higher val
        self.assertEqual(syn1, syn5, "just updating existing syn")
        self.assertEqual(co.numSynapses(), 2,
                         "Duplicate synapse, number should not increase")
        self.assertEqual(pytest.approx(co.permanenceForSynapse(syn1)), 0.99,
                         "updated to the larger permanence value")
Esempio n. 5
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  def testDestroySynapse(self):
    # empty connections, create segment seg and a synapse syn
    co = Connections(NUM_CELLS, 0.51)
    seg = co.createSegment(NUM_CELLS-1, 1)
    syn1 = co.createSynapse(seg, NUM_CELLS-1, 0.52)

    # destroy the synapse
    co.destroySynapse(syn1)
    self.assertEqual(co.numSynapses(), 0)
Esempio n. 6
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    def testDestroySynapse(self):
        # empty connections, create segment seg and a synapse syn
        co = Connections(NUM_CELLS, 0.51)
        seg = co.createSegment(NUM_CELLS - 1, 1)
        syn1 = co.createSynapse(seg, NUM_CELLS - 1, 0.52)

        # destroy the synapse
        co.destroySynapse(syn1)
        self.assertEqual(co.numSynapses(), 0)

        with pytest.raises(
                RuntimeError
        ):  # NTA_CHECK, data for removed synapse must not be accessible!
            permRemoved = co.permanenceForSynapse(syn1)
            assert permRemoved == perm1

        # double remove should be just ignored
        co.destroySynapse(syn1)