def test_preserve_data_params(self):
dp1 = {
"n_bits_in": 14,
"n_bits_out": 10,
"n_ones_in": 3,
"n_ones_out": 3,
"n_samples": 40,
"n_bits": -1,
"algorithm": "unique"
}
dp2 = {
"n_bits_in": 8,
"n_bits_out": 16,
"n_ones_in": 2,
"n_ones_out": 4,
"n_samples": 50,
"n_bits": -1,
"algorithm": "random"
}
net1 = NetworkBuilder(data_params=dp1)
net2 = NetworkBuilder(data_params=dp2)
pool = NetworkPool(net1.build())
pool.add_network(net2.build())
output = (stub_simulation(net1.mat_out, data_params=dp1) +
stub_simulation(net2.mat_out, data_params=dp2))
analysis_instances = pool.build_analysis(output)
self.assertEqual(len(analysis_instances), 2)
self.assertEqual(dp1, analysis_instances[0]["data_params"])
self.assertEqual(dp2, analysis_instances[1]["data_params"])
def test_neuron_count(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
pool = NetworkPool()
pool.add_network(builder.build())
pool.add_network(builder.build(topology_params={"multiplicity": 3}))
self.assertEqual(20, pool.neuron_count())
def test_calculate_latencies(self):
dp1 = {
"n_bits_in": 14,
"n_bits_out": 10,
"n_ones_in": 3,
"n_ones_out": 3,
"n_samples": 40
}
dp2 = {
"n_bits_in": 8,
"n_bits_out": 16,
"n_ones_in": 2,
"n_ones_out": 4,
"n_samples": 50
}
net1 = NetworkBuilder(data_params=dp1)
net2 = NetworkBuilder(data_params=dp2)
pool = NetworkPool(net1.build())
pool.add_network(net2.build())
output = (
stub_simulation(net1.mat_out, data_params=dp1, latency=20.0) +
stub_simulation(net2.mat_out, data_params=dp2, latency=30.0))
analysis_instances = pool.build_analysis(output)
np.testing.assert_almost_equal(
[20.0] * dp1["n_samples"],
analysis_instances[0].calculate_latencies())
np.testing.assert_almost_equal(
[30.0] * dp2["n_samples"],
analysis_instances[1].calculate_latencies())
def test_neuron_count(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
pool = NetworkPool()
pool.add_network(builder.build())
pool.add_network(builder.build(topology_params={"multiplicity": 3}))
self.assertEqual(20, pool.neuron_count())
def test_preserve_data_params(self):
dp1 = {
"n_bits_in": 14,
"n_bits_out": 10,
"n_ones_in": 3,
"n_ones_out": 3,
"n_samples": 40,
"n_bits": -1,
"algorithm": "unique"
}
dp2 = {
"n_bits_in": 8,
"n_bits_out": 16,
"n_ones_in": 2,
"n_ones_out": 4,
"n_samples": 50,
"n_bits": -1,
"algorithm": "random"
}
net1 = NetworkBuilder(data_params=dp1)
net2 = NetworkBuilder(data_params=dp2)
pool = NetworkPool(net1.build())
pool.add_network(net2.build())
output = (stub_simulation(net1.mat_out, data_params=dp1) +
stub_simulation(net2.mat_out, data_params=dp2))
analysis_instances = pool.build_analysis(output)
self.assertEqual(len(analysis_instances), 2)
self.assertEqual(dp1, analysis_instances[0]["data_params"])
self.assertEqual(dp2, analysis_instances[1]["data_params"])
def test_calculate_latencies(self):
dp1 = {
"n_bits_in": 14,
"n_bits_out": 10,
"n_ones_in": 3,
"n_ones_out": 3,
"n_samples": 40
}
dp2 = {
"n_bits_in": 8,
"n_bits_out": 16,
"n_ones_in": 2,
"n_ones_out": 4,
"n_samples": 50
}
net1 = NetworkBuilder(data_params=dp1)
net2 = NetworkBuilder(data_params=dp2)
pool = NetworkPool(net1.build())
pool.add_network(net2.build())
output = (stub_simulation(net1.mat_out, data_params=dp1, latency=20.0) +
stub_simulation(net2.mat_out, data_params=dp2, latency=30.0))
analysis_instances = pool.build_analysis(output)
np.testing.assert_almost_equal(
[20.0] * dp1["n_samples"],
analysis_instances[0].calculate_latencies())
np.testing.assert_almost_equal(
[30.0] * dp2["n_samples"],
analysis_instances[1].calculate_latencies())
def test_neuron_count(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build()
self.assertEqual(5, net.neuron_count())
self.assertEqual(10, net.neuron_count(count_sources=True))
net = builder.build(topology_params={"multiplicity": 3})
self.assertEqual(15, net.neuron_count())
self.assertEqual(30, net.neuron_count(count_sources=True))
def test_neuron_count(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build()
self.assertEqual(5, net.neuron_count())
self.assertEqual(10, net.neuron_count(count_sources=True))
net = builder.build(topology_params={"multiplicity": 3})
self.assertEqual(15, net.neuron_count())
self.assertEqual(30, net.neuron_count(count_sources=True))
def test_preserve_meta_data(self):
ud1 = {"foo": "bar"}
ud2 = {"foo2": "bar2"}
net1 = NetworkBuilder(data_params={})
net2 = NetworkBuilder(data_params={})
pool = NetworkPool(net1.build(meta_data=ud1))
pool.add_network(net2.build(meta_data=ud2))
output = (stub_simulation(net1.mat_out) + stub_simulation(net2.mat_out))
analysis_instances = pool.build_analysis(output)
self.assertEqual(len(analysis_instances), 2)
self.assertEqual(ud1, analysis_instances[0]["meta_data"])
self.assertEqual(ud2, analysis_instances[1]["meta_data"])
def test_preserve_meta_data(self):
ud1 = {"foo": "bar"}
ud2 = {"foo2": "bar2"}
net1 = NetworkBuilder(data_params={})
net2 = NetworkBuilder(data_params={})
pool = NetworkPool(net1.build(meta_data=ud1))
pool.add_network(net2.build(meta_data=ud2))
output = (stub_simulation(net1.mat_out) +
stub_simulation(net2.mat_out))
analysis_instances = pool.build_analysis(output)
self.assertEqual(len(analysis_instances), 2)
self.assertEqual(ud1, analysis_instances[0]["meta_data"])
self.assertEqual(ud2, analysis_instances[1]["meta_data"])
def test_build_analysis(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build(input_params=[{"multiplicity": 1},
{"multiplicity": 2}])
analysis_instances = net.build_analysis(time_mux_output_data)
test_time_mux_res(self, analysis_instances)
def test_add_net_build_analysis(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build(input_params=[{}, {}])
pool = NetworkPool()
pool.add_network(net)
analysis_instances = pool.build_analysis(time_mux_output_data)
test_time_mux_res(self, analysis_instances)
def test_add_net_build_analysis(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build(input_params=[{}, {}])
pool = NetworkPool()
pool.add_network(net)
analysis_instances = pool.build_analysis(time_mux_output_data)
test_time_mux_res(self, analysis_instances)
def test_build_analysis(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build(input_params=[{
"multiplicity": 1
}, {
"multiplicity": 2
}])
analysis_instances = net.build_analysis(time_mux_output_data)
test_time_mux_res(self, analysis_instances)
def test_match(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build()
output = [
{}, {"spikes": [[0.0, 100.0], [101.0], [300.0, 301.0], [200.0], [201.0, 101.0]]},
]
output_spikes, output_indices = net.match(output)
self.assertEqual([[0.0, 100.0], [101.0], [300.0, 301.0], [200.0],
[201.0, 101.0]], output_spikes)
self.assertEqual([[0, 0], [1], [2, 2], [1], [2, 1]], output_indices)
def test_add_nets_build_analysis(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build(input_params=[{"multiplicity": 1},
{"multiplicity": 2}])
pool = NetworkPool()
pool.add_networks([net, net, net])
analysis_instances = pool.build_analysis(time_mux_output_data * 3)
self.assertEqual(6, len(analysis_instances))
test_time_mux_res(self, analysis_instances[0:2])
test_time_mux_res(self, analysis_instances[2:4])
test_time_mux_res(self, analysis_instances[4:6])
def test_add_nets_build_analysis(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build(input_params=[{
"multiplicity": 1
}, {
"multiplicity": 2
}])
pool = NetworkPool()
pool.add_networks([net, net, net])
analysis_instances = pool.build_analysis(time_mux_output_data * 3)
self.assertEqual(6, len(analysis_instances))
test_time_mux_res(self, analysis_instances[0:2])
test_time_mux_res(self, analysis_instances[2:4])
test_time_mux_res(self, analysis_instances[4:6])
def test_match(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build()
output = [
{},
{
"spikes": [[0.0, 100.0], [101.0], [300.0, 301.0], [200.0],
[201.0, 101.0]]
},
]
output_spikes, output_indices = net.match(output)
self.assertEqual(
[[0.0, 100.0], [101.0], [300.0, 301.0], [200.0], [201.0, 101.0]],
output_spikes)
self.assertEqual([[0, 0], [1], [2, 2], [1], [2, 1]], output_indices)
def test_build(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build(topology_params={"params": {"cm": 0.2}})
topo = {
"connections": net["connections"],
"populations": net["populations"]
}
times = net["input_times"]
indices = net["input_indices"]
compare({'connections': [
((0, 0), (1, 1), 0.03, 0.0),
((0, 0), (1, 4), 0.03, 0.0),
((0, 1), (1, 1), 0.03, 0.0),
((0, 1), (1, 4), 0.03, 0.0),
((0, 2), (1, 0), 0.03, 0.0),
((0, 2), (1, 1), 0.03, 0.0),
((0, 2), (1, 2), 0.03, 0.0),
((0, 2), (1, 3), 0.03, 0.0),
((0, 3), (1, 0), 0.03, 0.0),
((0, 3), (1, 1), 0.03, 0.0),
((0, 4), (1, 2), 0.03, 0.0),
((0, 4), (1, 3), 0.03, 0.0)],
'populations': [
{'count': 5,
'params': [
{'spike_times': [100.0]},
{'spike_times': [100.0]},
{'spike_times': [0.0, 200.0]},
{'spike_times': [200.0]},
{'spike_times': [0.0]}
],
'record': [],
'type': 'SpikeSourceArray'
},
{'count': 5,
'params': [
{'tau_refrac': 0.1, 'tau_m': 20.0, 'e_rev_E': 0.0,
'i_offset': 0.0, 'cm': 0.2, 'e_rev_I': -70.0,
'v_thresh': -50.0, 'tau_syn_E': 5.0, 'v_rest': -65.0,
'tau_syn_I': 5.0, 'v_reset': -65.0}] * 5,
'type': 'IF_cond_exp',
'record': ['spikes']}],
}, topo)
self.assertEqual([[100.0], [100.0], [0.0, 200.0], [200.0], [0.0]],
times)
self.assertEqual([[1], [1], [0, 2], [2], [0]], indices)
def test_calculate_output_matrix_stub_sim(self):
dp1 = {
"n_bits_in": 14,
"n_bits_out": 10,
"n_ones_in": 3,
"n_ones_out": 3,
"n_samples": 40
}
dp2 = {
"n_bits_in": 8,
"n_bits_out": 16,
"n_ones_in": 2,
"n_ones_out": 4,
"n_samples": 50
}
dp3 = {
"n_bits_in": 5,
"n_bits_out": 8,
"n_ones_in": 1,
"n_ones_out": 3,
"n_samples": 20
}
tp1 = {
"multiplicity": 1
}
tp2 = {
"multiplicity": 3
}
tp3 = {
"multiplicity": 2
}
# Build three networks
net1 = NetworkBuilder(data_params=dp1)
net2 = NetworkBuilder(data_params=dp2)
net3 = NetworkBuilder(data_params=dp3)
# Add them to a network pool
pool = NetworkPool()
pool.add_networks([
net1.build(topology_params=tp1),
net2.build(topology_params=tp2),
net3.build(topology_params=tp3)])
# Simulate some expected output
output = (
stub_simulation(net1.mat_out, data_params=dp1,
topology_params=tp1, latency=20.0) +
stub_simulation(net2.mat_out, data_params=dp2,
topology_params=tp2, latency=30.0) +
stub_simulation(net3.mat_out, data_params=dp3,
topology_params=tp3, latency=10.0))
# Fetch the analysis instance for the output
analysis_instances = pool.build_analysis(output)
# Compare the output matrices
m1 = analysis_instances[0].calculate_output_matrix()
m2 = analysis_instances[1].calculate_output_matrix()
m3 = analysis_instances[2].calculate_output_matrix()
np.testing.assert_almost_equal(net1.mat_out, m1)
np.testing.assert_almost_equal(net2.mat_out, m2)
np.testing.assert_almost_equal(net3.mat_out, m3)
def test_calculate_output_matrix_stub_sim(self):
dp1 = {
"n_bits_in": 14,
"n_bits_out": 10,
"n_ones_in": 3,
"n_ones_out": 3,
"n_samples": 40
}
dp2 = {
"n_bits_in": 8,
"n_bits_out": 16,
"n_ones_in": 2,
"n_ones_out": 4,
"n_samples": 50
}
dp3 = {
"n_bits_in": 5,
"n_bits_out": 8,
"n_ones_in": 1,
"n_ones_out": 3,
"n_samples": 20
}
tp1 = {"multiplicity": 1}
tp2 = {"multiplicity": 3}
tp3 = {"multiplicity": 2}
# Build three networks
net1 = NetworkBuilder(data_params=dp1)
net2 = NetworkBuilder(data_params=dp2)
net3 = NetworkBuilder(data_params=dp3)
# Add them to a network pool
pool = NetworkPool()
pool.add_networks([
net1.build(topology_params=tp1),
net2.build(topology_params=tp2),
net3.build(topology_params=tp3)
])
# Simulate some expected output
output = (stub_simulation(
net1.mat_out, data_params=dp1, topology_params=tp1, latency=20.0) +
stub_simulation(net2.mat_out,
data_params=dp2,
topology_params=tp2,
latency=30.0) +
stub_simulation(net3.mat_out,
data_params=dp3,
topology_params=tp3,
latency=10.0))
# Fetch the analysis instance for the output
analysis_instances = pool.build_analysis(output)
# Compare the output matrices
m1 = analysis_instances[0].calculate_output_matrix()
m2 = analysis_instances[1].calculate_output_matrix()
m3 = analysis_instances[2].calculate_output_matrix()
np.testing.assert_almost_equal(net1.mat_out, m1)
np.testing.assert_almost_equal(net2.mat_out, m2)
np.testing.assert_almost_equal(net3.mat_out, m3)
def test_build(self):
mat_in, mat_out = test_data()
builder = NetworkBuilder(mat_in, mat_out)
net = builder.build(topology_params={"params": {"cm": 0.2}})
topo = {
"connections": net["connections"],
"populations": net["populations"]
}
times = net["input_times"]
indices = net["input_indices"]
compare(
{
'connections': [((0, 0), (1, 1), 0.03, 0.0),
((0, 0), (1, 4), 0.03, 0.0),
((0, 1), (1, 1), 0.03, 0.0),
((0, 1), (1, 4), 0.03, 0.0),
((0, 2), (1, 0), 0.03, 0.0),
((0, 2), (1, 1), 0.03, 0.0),
((0, 2), (1, 2), 0.03, 0.0),
((0, 2), (1, 3), 0.03, 0.0),
((0, 3), (1, 0), 0.03, 0.0),
((0, 3), (1, 1), 0.03, 0.0),
((0, 4), (1, 2), 0.03, 0.0),
((0, 4), (1, 3), 0.03, 0.0)],
'populations': [{
'count':
5,
'params': [{
'spike_times': [100.0]
}, {
'spike_times': [100.0]
}, {
'spike_times': [0.0, 200.0]
}, {
'spike_times': [200.0]
}, {
'spike_times': [0.0]
}],
'record': [],
'type':
'SpikeSourceArray'
}, {
'count':
5,
'params': [{
'tau_refrac': 0.1,
'tau_m': 20.0,
'e_rev_E': 0.0,
'i_offset': 0.0,
'cm': 0.2,
'e_rev_I': -70.0,
'v_thresh': -50.0,
'tau_syn_E': 5.0,
'v_rest': -65.0,
'tau_syn_I': 5.0,
'v_reset': -65.0
}] * 5,
'type':
'IF_cond_exp',
'record': ['spikes']
}],
}, topo)
self.assertEqual([[100.0], [100.0], [0.0, 200.0], [200.0], [0.0]],
times)
self.assertEqual([[1], [1], [0, 2], [2], [0]], indices)