/usr/share/pyshared/mdp/test/test_hinet.py is in python-mdp 3.3-1.
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"""These are test functions for hinet.
"""
import py.test
import StringIO
import mdp.hinet as mh
from _tools import *
_get_new_flow = lambda: mdp.Flow([
mdp.nodes.NoiseNode(),
mdp.nodes.SFANode()])
_get_new_nodes = lambda: [
mdp.nodes.CuBICANode(input_dim=1, whitened=True),
mdp.nodes.CuBICANode(input_dim=2, whitened=True),
mdp.nodes.CuBICANode(input_dim=1, whitened=True),
]
_get_sigle_node = lambda: mdp.nodes.CuBICANode(input_dim=2, whitened=True)
NODES = [(mh.FlowNode, [_get_new_flow], None),
(mh.Layer, [_get_new_nodes], None),
(mh.CloneLayer, [_get_sigle_node, 2], None),
]
def test_FlowNode_training():
flow = mdp.Flow([mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.PCANode(output_dim=15, reduce=True),
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.PCANode(output_dim=3, reduce=True)])
flownode = mh.FlowNode(flow)
x = numx_rand.random([300,20])
while flownode.get_remaining_train_phase() > 0:
flownode.train(x)
flownode.stop_training()
flownode.execute(x)
def test_FlowNode_trainability():
flow = mdp.Flow([mdp.nodes.PolynomialExpansionNode(degree=2)])
flownode = mh.FlowNode(flow)
assert flownode.is_trainable() is False
flow = mdp.Flow([mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.PCANode(output_dim=15),
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.PCANode(output_dim=3)])
flownode = mh.FlowNode(flow)
assert flownode.is_trainable() is True
def test_FlowNode_invertibility():
flow = mdp.Flow([mdp.nodes.PolynomialExpansionNode(degree=2)])
flownode = mh.FlowNode(flow)
assert flownode.is_invertible() is False
flow = mdp.Flow([mdp.nodes.PCANode(output_dim=15),
mdp.nodes.SFANode(),
mdp.nodes.PCANode(output_dim=3)])
flownode = mh.FlowNode(flow)
assert flownode.is_invertible() is True
def test_FlowNode_pretrained_node():
x = numx_rand.random([100,10])
pretrained_node = mdp.nodes.PCANode(output_dim=6)
pretrained_node.train(x)
pretrained_node.stop_training()
flow = mdp.Flow([pretrained_node,
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.PCANode(output_dim=3)])
flownode = mh.FlowNode(flow)
while flownode.get_remaining_train_phase() > 0:
flownode.train(x)
flownode.stop_training()
flownode.execute(x)
def test_FlowNode_fix_nodes_dimensions1():
x = numx_rand.random([100,10])
last_node = mdp.nodes.IdentityNode()
flow = mdp.Flow([mdp.nodes.PCANode(output_dim=3),
mdp.nodes.IdentityNode(),
last_node])
flownode = mh.FlowNode(flow)
flownode.train(x)
flownode.stop_training()
# check that the dimensions of NoiseNode and FlowNode where all set
# by calling _fix_nodes_dimensions
assert flownode.output_dim == 3
assert last_node.input_dim == 3
assert last_node.output_dim == 3
def test_FlowNode_fix_nodes_dimensions2():
flow = mdp.Flow([mdp.nodes.IdentityNode(),
mdp.nodes.IdentityNode()])
flownode = mh.FlowNode(flow)
# this should fail, since the internal nodes don't have fixed dims
py.test.raises(mdp.InconsistentDimException,
lambda: flownode.set_output_dim(10))
x = numx_rand.random([100,10])
flownode.execute(x)
assert flownode.output_dim == 10
def test_FlowNode_fix_nodes_dimensions3():
flow = mdp.Flow([mdp.nodes.IdentityNode()])
flownode = mh.FlowNode(flow)
# for a single node this should not raise an Exception
flownode.set_output_dim(10)
x = numx_rand.random([100,10])
flownode.execute(x)
def test_FlowNode_pretrained_flow():
flow = mdp.Flow([mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.PCANode(output_dim=15, reduce=True),
mdp.nodes.PolynomialExpansionNode(degree=2),
mdp.nodes.PCANode(output_dim=3, reduce=True)])
flownode = mh.FlowNode(flow)
x = numx_rand.random([300,20])
while flownode.get_remaining_train_phase() > 0:
flownode.train(x)
flownode.stop_training()
# build new flownode with the trained nodes
flownode = mh.FlowNode(flow)
assert not flownode.is_training()
flownode.execute(x)
def test_FlowNode_copy1():
flow = mdp.Flow([mdp.nodes.PCANode(), mdp.nodes.SFANode()])
flownode = mh.FlowNode(flow)
flownode.copy()
def test_FlowNode_copy2():
# Test that the FlowNode copy method delegates to internal nodes.
class CopyFailException(Exception):
pass
class CopyFailNode(mdp.Node):
def copy(self, protocol=None):
raise CopyFailException()
flow = mdp.Flow([mdp.Node(), CopyFailNode()])
flownode = mh.FlowNode(flow)
py.test.raises(CopyFailException, flownode.copy)
def _pca_nodes(input_dims, output_dims):
return [mdp.nodes.PCANode(input_dim=ind, output_dim=outd)
for ind, outd in zip(input_dims, output_dims)]
def test_Layer():
layer = mh.Layer(_pca_nodes([10, 17, 3], [5, 3, 1]))
x = numx_rand.random([100,30]).astype('f')
layer.train(x)
y = layer.execute(x)
assert layer.dtype == numx.dtype('f')
assert y.dtype == layer.dtype
def test_Layer_invertibility():
layer = mh.Layer(_pca_nodes([10, 17, 3], [10, 17, 3]))
x = numx_rand.random([100,30]).astype('f')
layer.train(x)
y = layer.execute(x)
x_inverse = layer.inverse(y)
assert numx.all(numx.absolute(x - x_inverse) < 0.001)
def test_Layer_invertibility2():
# reduce the dimensions, so input_dim != output_dim
layer = mh.Layer(_pca_nodes([10, 17, 3], [8, 12, 3]))
x = numx_rand.random([100,30]).astype('f')
layer.train(x)
y = layer.execute(x)
layer.inverse(y)
def test_SameInputLayer():
layer = mh.SameInputLayer(_pca_nodes([10, 10, 10], [5, 3, 1]))
x = numx_rand.random([100,10]).astype('f')
layer.train(x)
y = layer.execute(x)
assert layer.dtype == numx.dtype('f')
assert y.dtype == layer.dtype
def test_CloneLayer():
node = mdp.nodes.PCANode(input_dim=10, output_dim=5)
x = numx_rand.random([10,70]).astype('f')
layer = mh.CloneLayer(node, 7)
layer.train(x)
y = layer.execute(x)
assert layer.dtype == numx.dtype('f')
assert y.dtype == layer.dtype
def test_SwitchboardInverse1():
sboard = mh.Switchboard(input_dim=3,
connections=[2,0,1])
assert sboard.is_invertible()
y = numx.array([[2,3,4],[5,6,7]])
x = sboard.inverse(y)
assert numx.all(x == numx.array([[3,4,2],[6,7,5]]))
def testSwitchboardInverse2():
sboard = mh.Switchboard(input_dim=3,
connections=[2,1,1])
assert not sboard.is_invertible()
## Tests for MeanInverseSwitchboard ##
def test_MeanInverseSwitchboard1():
sboard = mh.MeanInverseSwitchboard(input_dim=3,
connections=[0,0,2])
assert sboard.is_invertible()
y = numx.array([[2,4,3],[1,1,7]])
x = sboard.inverse(y)
assert numx.all(x == numx.array([[3,0,3],[1,0,7]]))
def test_MeanInverseSwitchboard2():
sboard = mh.MeanInverseSwitchboard(input_dim=3,
connections=[1,1,1,2,2])
assert sboard.is_invertible()
y = numx.array([[2,4,0,1,1],[3,3,3,2,4]])
x = sboard.inverse(y)
assert numx.all(x == numx.array([[0,2,1],[0,3,3]]))
## Tests for ChannelSwitchboard ##
def testOutChannelInput():
sboard = mh.ChannelSwitchboard(input_dim=6,
connections=[5,5,
0,1],
out_channel_dim=2,
in_channel_dim=2)
assert numx.all(sboard.get_out_channel_input(0) ==
numx.array([5,5]))
assert numx.all(sboard.get_out_channel_input(1) ==
numx.array([0,1]))
def testOutChannelsInputChannels():
sboard = mh.ChannelSwitchboard(input_dim=6,
connections=[5,5, # out chan 1
0,1], # out chan 2
out_channel_dim=2,
in_channel_dim=2)
# note that there are 3 input channels
assert numx.all(sboard.get_out_channels_input_channels(0) ==
numx.array([2]))
assert numx.all(sboard.get_out_channels_input_channels(1) ==
numx.array([0]))
assert numx.all(sboard.get_out_channels_input_channels([0,1]) ==
numx.array([0,2]))
## Tests for Rectangular2dSwitchboard ##
def testRect2dRouting1():
sboard = mh.Rectangular2dSwitchboard(in_channels_xy=(3,2),
in_channel_dim=2,
field_channels_xy=(2,1),
field_spacing_xy=1)
assert numx.all(sboard.connections ==
numx.array([0, 1, 2, 3, 2, 3, 4, 5, 6, 7,
8, 9, 8, 9, 10, 11]))
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
# test generated switchboard
channel_sboard = sboard.get_out_channel_node(0)
channel_sboard.execute(x)
def testRect2dRouting2():
sboard = mh.Rectangular2dSwitchboard(in_channels_xy=(2,4),
in_channel_dim=1,
field_channels_xy=(1,2),
field_spacing_xy=(1,2))
assert numx.all(sboard.connections ==
numx.array([0, 2, 1, 3, 4, 6, 5, 7]))
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
# test generated switchboard
channel_sboard = sboard.get_out_channel_node(0)
channel_sboard.execute(x)
def testRect2dRouting3():
sboard = mh.Rectangular2dSwitchboard(in_channels_xy=(2,4),
in_channel_dim=1,
field_channels_xy=2,
field_spacing_xy=(1,2))
assert (sboard.connections ==
numx.array([0, 1, 2, 3, 4, 5, 6, 7])).all()
def testRect2dRouting4():
sboard = mh.Rectangular2dSwitchboard(in_channels_xy=4,
in_channel_dim=1,
field_channels_xy=(3,2),
field_spacing_xy=(1,2))
assert (sboard.connections ==
numx.array([0, 1, 2, 4, 5, 6,
1, 2, 3, 5, 6, 7,
8, 9, 10, 12, 13, 14,
9, 10, 11, 13, 14, 15])).all()
def testRect2d_get_out_channel_node():
sboard = mh.Rectangular2dSwitchboard(in_channels_xy=(5,4),
in_channel_dim=2,
field_channels_xy=(3,2),
field_spacing_xy=(1,2))
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
y = sboard.execute(x)
# routing layer
nodes = [sboard.get_out_channel_node(index)
for index in xrange(sboard.output_channels)]
layer = mh.SameInputLayer(nodes)
layer_y = layer.execute(x)
assert (y == layer_y).all()
def test_Rect2d_exception_1():
bad_args = dict(in_channels_xy=(12,8),
# 3 is the problematic value:
field_channels_xy=(4,3),
field_spacing_xy=2,
in_channel_dim=3,
ignore_cover=False)
with py.test.raises(mh.Rectangular2dSwitchboardException):
mh.Rectangular2dSwitchboard(**bad_args)
def test_Rect2d_exception_2():
bad_args = dict(in_channels_xy=(12,8),
# 9 is the problematic value:
field_channels_xy=(4,9),
field_spacing_xy=2,
in_channel_dim=3,
ignore_cover=False)
with py.test.raises(mh.Rectangular2dSwitchboardException):
mh.Rectangular2dSwitchboard(**bad_args)
def test_Rect2d_exception_3():
bad_args = dict(in_channels_xy=(12,8),
# 9 is the problematic value:
field_channels_xy=(4,9),
field_spacing_xy=2,
in_channel_dim=3,
ignore_cover=True)
with py.test.raises(mh.Rectangular2dSwitchboardException):
mh.Rectangular2dSwitchboard(**bad_args)
## Tests for DoubleRect2dSwitchboard ##
def test_Rect_double_routing_1():
sboard = mh.DoubleRect2dSwitchboard(in_channels_xy=4,
field_channels_xy=2,
in_channel_dim=1)
assert (sboard.connections ==
numx.array([0,1,4,5, 2,3,6,7, 8,9,12,13, 10,11,14,15,
# uneven fields
5,6,9,10])).all()
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_Rect_double_routing_2():
sboard = mh.DoubleRect2dSwitchboard(in_channels_xy=(6,4),
field_channels_xy=(2,2),
in_channel_dim=1)
assert (sboard.connections ==
numx.array([0,1,6,7, 2,3,8,9, 4,5,10,11, 12,13,18,19,
14,15,20,21, 16,17,22,23,
# uneven fields
7,8,13,14, 9,10,15,16])).all()
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_Rect_double_routing_3():
sboard = mh.DoubleRect2dSwitchboard(in_channels_xy=(4,6),
field_channels_xy=2,
in_channel_dim=1)
assert (sboard.connections ==
numx.array([0,1,4,5, 2,3,6,7, 8,9,12,13, 10,11,14,15,
16,17,20,21, 18,19,22,23,
# uneven fields
5,6,9,10, 13,14,17,18])).all()
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
## Tests for DoubleRhomb2dSwitchboard ##
def test_DoubleRhomb_routing_1():
sboard = mh.DoubleRhomb2dSwitchboard(long_in_channels_xy=(3,2),
diag_field_channels=2,
in_channel_dim=1)
assert (sboard.connections ==
numx.array([1,6,7,4])).all()
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_DoubleRhomd_routing_2():
sboard = mh.DoubleRhomb2dSwitchboard(long_in_channels_xy=(2,3),
diag_field_channels=2,
in_channel_dim=1)
assert (sboard.connections ==
numx.array([6,2,3,7])).all()
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_DoubleRhomd_routing_3():
sboard = mh.DoubleRhomb2dSwitchboard(long_in_channels_xy=(4,2),
diag_field_channels=2,
in_channel_dim=1)
assert (sboard.connections ==
numx.array([1,8,9,5, 2,9,10,6])).all()
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_DoubleRhomd_routing_4():
sboard = mh.DoubleRhomb2dSwitchboard(long_in_channels_xy=(2,4),
diag_field_channels=2,
in_channel_dim=1)
assert (sboard.connections ==
numx.array([8,2,3,9, 9,4,5,10])).all()
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_DoubleRhomd_routing_5():
sboard = mh.DoubleRhomb2dSwitchboard(long_in_channels_xy=4,
diag_field_channels=2,
in_channel_dim=1)
assert (sboard.connections ==
numx.array([1,16,17,5,
2,17,18,6,
5,19,20,9,
6,20,21,10,
9,22,23,13,
10,23,24,14])).all()
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_DoubleRhomd_routing_6():
sboard = mh.DoubleRhomb2dSwitchboard(long_in_channels_xy=(7,4),
diag_field_channels=4,
in_channel_dim=1)
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_DoubleRhomd_routing_7():
sboard = mh.DoubleRhomb2dSwitchboard(long_in_channels_xy=(4,7),
diag_field_channels=4,
in_channel_dim=1)
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_DoubleRhomd_routing_8():
sboard = mh.DoubleRhomb2dSwitchboard(long_in_channels_xy=(6,7),
diag_field_channels=4,
in_channel_dim=1)
x = numx.array([range(0, sboard.input_dim),
range(101, 101+sboard.input_dim)])
sboard.execute(x)
def test_hinet_simple_net():
switchboard = mh.Rectangular2dSwitchboard(in_channels_xy=(12,8),
field_channels_xy=4,
field_spacing_xy=2,
in_channel_dim=3)
node = mdp.nodes.PCANode(input_dim=4*4*3, output_dim=5)
flownode = mh.FlowNode(mdp.Flow([node,]))
layer = mh.CloneLayer(flownode, switchboard.output_channels)
flow = mdp.Flow([switchboard, layer])
x = numx_rand.random([5, switchboard.input_dim])
flow.train(x)
def pytest_funcarg__noisenode(request):
return mdp.nodes.NoiseNode(input_dim=20*20,
noise_args=(0, 0.0001))
def test_SFA_net(noisenode):
sfa_node = mdp.nodes.SFANode(input_dim=20*20, output_dim=10, dtype='f')
switchboard = mh.Rectangular2dSwitchboard(in_channels_xy=100,
field_channels_xy=20,
field_spacing_xy=10)
flownode = mh.FlowNode(mdp.Flow([noisenode, sfa_node]))
sfa_layer = mh.CloneLayer(flownode, switchboard.output_channels)
flow = mdp.Flow([switchboard, sfa_layer])
train_gen = numx.cast['f'](numx_rand.random((3, 10, 100*100)))
flow.train([None, train_gen])
def testHiNetHTML(noisenode):
# create some flow for testing
sfa_node = mdp.nodes.SFANode(input_dim=20*20, output_dim=10)
switchboard = mh.Rectangular2dSwitchboard(in_channels_xy=100,
field_channels_xy=20,
field_spacing_xy=10)
flownode = mh.FlowNode(mdp.Flow([noisenode, sfa_node]))
sfa_layer = mh.CloneLayer(flownode, switchboard.output_channels)
flow = mdp.Flow([switchboard, sfa_layer])
# create dummy file to write the HTML representation
html_file = StringIO.StringIO()
hinet_html = mdp.hinet.HiNetHTMLVisitor(html_file)
hinet_html.convert_flow(flow)
html_file.close()
def testHiNetXHTML():
# create some flow for testing
sfa_node = mdp.nodes.SFANode(input_dim=20*20, output_dim=10)
flow = mdp.Flow([sfa_node])
# create dummy file to write the HTML representation
html_file = StringIO.StringIO()
hinet_html = mdp.hinet.HiNetXHTMLVisitor(html_file)
hinet_html.convert_flow(flow)
html_file.close()
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