def test_stoptrain_result1(self):
"""Test that stop_result is handled correctly."""
stop_result = ({"test": 0}, 1)
bi_sfa_node = SFABiNode(stop_result=stop_result, node_id="testing binode")
assert bi_sfa_node.is_trainable()
x = n.random.random((100, 10))
train_result = bi_sfa_node.train(x)
assert train_result == None
assert bi_sfa_node.is_training()
result = bi_sfa_node.stop_training()
assert result == (None,) + stop_result
assert bi_sfa_node.input_dim == 10
assert bi_sfa_node.output_dim == 10
assert bi_sfa_node.dtype == "float64"
def test_clonelayer(self):
"""Test a simple clonelayer with three SFA Nodes."""
sfa_node = SFABiNode(input_dim=3, output_dim=2)
clonelayer = CloneBiLayer(sfa_node, 3)
x = n.random.random((100,9))
clonelayer.train(x)
clonelayer.stop_training()
clonelayer.execute(x)
def test_stop_message_attribute(self):
"""Test that the stop_result attribute is present in forked node."""
stop_result = ({"test": "blabla"}, "node123")
x = n.random.random([100, 10])
node = SFABiNode(stop_result=stop_result)
try:
mdp.activate_extension("parallel")
node2 = node.fork()
node2.train(x)
forked_result = node2.stop_training()
assert forked_result == (None, ) + stop_result
# same with derived sfa2 node
node = SFA2BiNode(stop_result=stop_result)
mdp.activate_extension("parallel")
node2 = node.fork()
node2.train(x)
forked_result = node2.stop_training()
assert forked_result == (None, ) + stop_result
finally:
mdp.deactivate_extension("parallel")
def test_stop_message_attribute(self):
"""Test that the stop_result attribute is present in forked node."""
stop_result = ({"test": "blabla"}, "node123")
x = n.random.random([100,10])
node = SFABiNode(stop_result=stop_result)
try:
mdp.activate_extension("parallel")
node2 = node.fork()
node2.train(x)
forked_result = node2.stop_training()
assert forked_result == (None,) + stop_result
# same with derived sfa2 node
node = SFA2BiNode(stop_result=stop_result)
mdp.activate_extension("parallel")
node2 = node.fork()
node2.train(x)
forked_result = node2.stop_training()
assert forked_result == (None,) + stop_result
finally:
mdp.deactivate_extension("parallel")
def test_use_copies_msg(self):
"""Test the correct reaction to an outgoing use_copies message."""
stop_result = ({"clonelayer" + MSG_ID_SEP + "use_copies": True}, 1)
stop_sfa_node = SFABiNode(stop_result=stop_result,
input_dim=10, output_dim=3)
clonelayer = CloneBiLayer(node=stop_sfa_node,
n_nodes=3,
use_copies=False,
node_id="clonelayer")
x = n.random.random((100,30))
clonelayer.train(x)
clonelayer.stop_training()
assert clonelayer.use_copies is True
def test_use_copies_msg_flownode(self):
"""Test the correct reaction to an outgoing use_copies message."""
stop_result = ({"clonelayer" + MSG_ID_SEP + "use_copies": True},
EXIT_TARGET)
stop_sfa_node = SFABiNode(stop_result=stop_result,
input_dim=10, output_dim=3)
biflownode = BiFlowNode(BiFlow([stop_sfa_node]))
clonelayer = CloneBiLayer(node=biflownode,
n_nodes=3,
use_copies=False,
node_id="clonelayer")
biflow = clonelayer + IdentityBiNode()
x = n.random.random((100,30))
biflow.train(x)
assert clonelayer.use_copies is True
def test_mixed_parallel_flow(self):
"""Test a ParallelBiFlow with both standard and BiNodes."""
flow = ParallelBiFlow([
mdp.nodes.PCANode(output_dim=8),
SFABiNode(output_dim=5),
SFA2BiNode(output_dim=20)
])
data_iterables = [[n.random.random((20, 10)) for _ in range(6)]] * 3
scheduler = mdp.parallel.Scheduler()
flow.train(data_iterables, scheduler=scheduler)
x = n.random.random([100, 10])
flow.execute(x)
iterator = [n.random.random((20, 10)) for _ in range(6)]
flow.execute(iterator, scheduler=scheduler)
scheduler.shutdown()
def test_use_copies_msg(self):
"""Test the correct reaction to an outgoing use_copies message."""
stop_result = ({
"clonelayer" + MSG_ID_SEP + "use_copies": True
}, EXIT_TARGET)
stop_sfa_node = SFABiNode(stop_result=stop_result,
input_dim=10,
output_dim=3)
biflownode = BiFlowNode(BiFlow([stop_sfa_node]))
clonelayer = ParallelCloneBiLayer(node=biflownode,
n_nodes=3,
use_copies=False,
node_id="clonelayer")
data = [[n.random.random((100, 30)) for _ in range(5)]]
biflow = ParallelBiFlow([clonelayer])
biflow.train(data, scheduler=mdp.parallel.Scheduler())
assert clonelayer.use_copies is True
def test_stoptrain_result1(self):
"""Test that stop_result is handled correctly."""
stop_result = ({"test": 0}, 1)
bi_sfa_node = SFABiNode(stop_result=stop_result,
node_id="testing binode")
assert bi_sfa_node.is_trainable()
x = n.random.random((100,10))
train_result = bi_sfa_node.train(x)
assert train_result is None
assert bi_sfa_node.is_training()
result = bi_sfa_node.stop_training()
assert result == (None,) + stop_result
assert bi_sfa_node.input_dim == 10
assert bi_sfa_node.output_dim == 10
assert bi_sfa_node.dtype == "float64"
