def test_normal_flow(self):
"""Test a BiFlow with normal nodes."""
flow = BiFlow([mdp.nodes.SFANode(output_dim=5),
mdp.nodes.PolynomialExpansionNode(degree=3),
mdp.nodes.SFANode(output_dim=20)])
data_iterables = [[np.random.random((20,10)) for _ in range(6)],
None,
[np.random.random((20,10)) for _ in range(6)]]
flow.train(data_iterables)
x = np.random.random([100,10])
flow.execute(x)
def test_normal_flow(self):
"""Test a BiFlow with normal nodes."""
flow = BiFlow([
mdp.nodes.SFANode(output_dim=5),
mdp.nodes.PolynomialExpansionNode(degree=3),
mdp.nodes.SFANode(output_dim=20)
])
data_iterables = [[np.random.random((20, 10)) for _ in range(6)], None,
[np.random.random((20, 10)) for _ in range(6)]]
flow.train(data_iterables)
x = np.random.random([100, 10])
flow.execute(x)
def test_normal_multiphase(self):
"""Test training and execution with multiple training phases.
The node with multiple training phases is a hinet.FlowNode.
"""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = mdp.hinet.FlowNode(mdp.Flow([sfa_node, sfa2_node]))
flow = BiFlow([flownode,
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.SFANode(output_dim=5)])
data_iterables = [[np.random.random((30,10)) for _ in range(6)],
None,
[np.random.random((30,10)) for _ in range(6)]]
flow.train(data_iterables)
x = np.random.random([100,10])
flow.execute(x)
def test_normal_multiphase(self):
"""Test training and execution with multiple training phases.
The node with multiple training phases is a hinet.FlowNode.
"""
sfa_node = mdp.nodes.SFANode(input_dim=10, output_dim=8)
sfa2_node = mdp.nodes.SFA2Node(input_dim=8, output_dim=6)
flownode = mdp.hinet.FlowNode(mdp.Flow([sfa_node, sfa2_node]))
flow = BiFlow([
flownode,
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.SFANode(output_dim=5)
])
data_iterables = [[np.random.random((30, 10)) for _ in range(6)], None,
[np.random.random((30, 10)) for _ in range(6)]]
flow.train(data_iterables)
x = np.random.random([100, 10])
flow.execute(x)
def test_training_targets(self):
"""Test targeting during training and stop_training."""
tracelog = []
verbose = False
node1 = TraceJumpBiNode(
output_dim=1,
tracelog=tracelog,
node_id="node_1",
train_results=[[None]],
stop_train_results=[None],
execute_results=[None, (None, {"b": 2}, "node_3"),
(None, {"b": 2}, EXIT_TARGET),],
verbose=verbose)
node2 = TraceJumpBiNode(
output_dim=1,
tracelog=tracelog,
node_id="node_2",
train_results=[[None]],
stop_train_results=[(None, {"b": 2}, "node_1")],
execute_results=[None, (None, None, "node_1"),
(None, None, "node_1")],
verbose=verbose)
node3 = TraceJumpBiNode(
output_dim=1,
tracelog=tracelog,
node_id="node_3",
train_results=[[(None, {"a": 1}, "node_2"), None]],
stop_train_results=[(None, {"a": 1}, "node_2")],
execute_results=[(None, {"b": 2}, EXIT_TARGET)],
verbose=verbose)
biflow = BiFlow([node1, node2, node3])
data_iterables = [[np.random.random((1,1)) for _ in range(2)],
[np.random.random((1,1)) for _ in range(2)],
[np.random.random((1,1)) for _ in range(2)]]
biflow.train(data_iterables)
# print ",\n".join(str(log) for log in tracelog)
# tracelog reference
reference = [
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset'),
('node_1', 'train'),
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset'),
('node_1', 'train'),
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset'),
('node_1', 'stop_training'),
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset'),
('node_1', 'execute'),
('node_2', 'train'),
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset'),
('node_1', 'execute'),
('node_2', 'train'),
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset'),
('node_2', 'stop_training'),
('node_1', 'execute'),
('node_2', 'execute'),
('node_3', 'execute'),
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset'),
('node_1', 'execute'),
('node_2', 'execute'),
('node_3', 'train'),
('node_2', 'execute'),
('node_1', 'execute'),
('node_3', 'train'),
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset'),
('node_1', 'execute'),
('node_2', 'execute'),
('node_3', 'train'),
('node_2', 'execute'),
('node_1', 'execute'),
('node_3', 'train'),
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset'),
('node_3', 'stop_training'),
('node_2', 'execute'),
('node_3', 'execute'),
('node_1', 'bi_reset'),
('node_2', 'bi_reset'),
('node_3', 'bi_reset')
]
assert tracelog == reference
def test_fda_binode(self):
"""Test using the FDABiNode in a BiFlow."""
samples = mdp.numx_rand.random((100,10))
labels = mdp.numx.arange(100)
flow = BiFlow([mdp.nodes.PCANode(), nodes.FDABiNode()])
flow.train([[samples],[samples]], [None,[{"labels": labels}]])
def test_training_targets(self):
"""Test targeting during training and stop_training."""
tracelog = []
verbose = False
node1 = TraceJumpBiNode(output_dim=1,
tracelog=tracelog,
node_id="node_1",
train_results=[[None]],
stop_train_results=[None],
execute_results=[
None,
(None, {
"b": 2
}, "node_3"),
(None, {
"b": 2
}, EXIT_TARGET),
],
verbose=verbose)
node2 = TraceJumpBiNode(output_dim=1,
tracelog=tracelog,
node_id="node_2",
train_results=[[None]],
stop_train_results=[(None, {
"b": 2
}, "node_1")],
execute_results=[
None, (None, None, "node_1"),
(None, None, "node_1")
],
verbose=verbose)
node3 = TraceJumpBiNode(output_dim=1,
tracelog=tracelog,
node_id="node_3",
train_results=[[(None, {
"a": 1
}, "node_2"), None]],
stop_train_results=[(None, {
"a": 1
}, "node_2")],
execute_results=[(None, {
"b": 2
}, EXIT_TARGET)],
verbose=verbose)
biflow = BiFlow([node1, node2, node3])
data_iterables = [[np.random.random((1, 1)) for _ in range(2)],
[np.random.random((1, 1)) for _ in range(2)],
[np.random.random((1, 1)) for _ in range(2)]]
biflow.train(data_iterables)
# print ",\n".join(str(log) for log in tracelog)
# tracelog reference
reference = [('node_1', 'bi_reset'), ('node_2', 'bi_reset'),
('node_3', 'bi_reset'), ('node_1', 'train'),
('node_1', 'bi_reset'), ('node_2', 'bi_reset'),
('node_3', 'bi_reset'), ('node_1', 'train'),
('node_1', 'bi_reset'), ('node_2', 'bi_reset'),
('node_3', 'bi_reset'), ('node_1', 'stop_training'),
('node_1', 'bi_reset'), ('node_2', 'bi_reset'),
('node_3', 'bi_reset'), ('node_1', 'execute'),
('node_2', 'train'), ('node_1', 'bi_reset'),
('node_2', 'bi_reset'), ('node_3', 'bi_reset'),
('node_1', 'execute'), ('node_2', 'train'),
('node_1', 'bi_reset'), ('node_2', 'bi_reset'),
('node_3', 'bi_reset'), ('node_2', 'stop_training'),
('node_1', 'execute'), ('node_2', 'execute'),
('node_3', 'execute'), ('node_1', 'bi_reset'),
('node_2', 'bi_reset'), ('node_3', 'bi_reset'),
('node_1', 'execute'), ('node_2', 'execute'),
('node_3', 'train'), ('node_2', 'execute'),
('node_1', 'execute'), ('node_3', 'train'),
('node_1', 'bi_reset'), ('node_2', 'bi_reset'),
('node_3', 'bi_reset'), ('node_1', 'execute'),
('node_2', 'execute'), ('node_3', 'train'),
('node_2', 'execute'), ('node_1', 'execute'),
('node_3', 'train'), ('node_1', 'bi_reset'),
('node_2', 'bi_reset'), ('node_3', 'bi_reset'),
('node_3', 'stop_training'), ('node_2', 'execute'),
('node_3', 'execute'), ('node_1', 'bi_reset'),
('node_2', 'bi_reset'), ('node_3', 'bi_reset')]
assert tracelog == reference
def test_fda_binode(self):
"""Test using the FDABiNode in a BiFlow."""
samples = mdp.numx_rand.random((100, 10))
labels = mdp.numx.arange(100)
flow = BiFlow([mdp.nodes.PCANode(), nodes.FDABiNode()])
flow.train([[samples], [samples]], [None, [{"labels": labels}]])
