/usr/share/pyshared/mdp/test/test_contrib.py is in python-mdp 3.3-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 | """These are test functions for MDP contributed nodes.
"""
from _tools import *
from test_ICANode import verify_ICANode, verify_ICANodeMatrices
requires_joblib = skip_on_condition(
"not mdp.config.has_caching",
"This test requires the 'joblib' module.")
def _s_shape(theta):
"""
returns x,y
a 2-dimensional S-shaped function
for theta ranging from 0 to 1
"""
t = 3*numx.pi * (theta-0.5)
x = numx.sin(t)
y = numx.sign(t)*(numx.cos(t)-1)
return x,y
def _s_shape_1D(n):
t = numx.linspace(0., 1., n)
x, z = _s_shape(t)
y = numx.linspace(0., 5., n)
return x, y, z, t
def _s_shape_2D(nt, ny):
t, y = numx.meshgrid(numx.linspace(0., 1., nt),
numx.linspace(0., 2., ny))
t = t.flatten()
y = y.flatten()
x, z = _s_shape(t)
return x, y, z, t
def _compare_neighbors(orig, proj, k):
n = orig.shape[0]
err = numx.zeros((n,))
# compare neighbors indices
for i in xrange(n):
# neighbors in original space
dist = orig - orig[i,:]
orig_nbrs = numx.argsort((dist**2).sum(1))[1:k+1]
orig_nbrs.sort()
# neighbors in projected space
dist = proj - proj[i,:]
proj_nbrs = numx.argsort((dist**2).sum(1))[1:k+1]
proj_nbrs.sort()
for idx in orig_nbrs:
if idx not in proj_nbrs:
err[i] += 1
return err
def test_JADENode():
trials = 3
for i in xrange(trials):
try:
ica = mdp.nodes.JADENode(limit = 10**(-decimal))
ica2 = ica.copy()
verify_ICANode(ica, rand_func=numx_rand.exponential)
verify_ICANodeMatrices(ica2)
return
except Exception:
if i == trials - 1:
raise
def test_NIPALSNode():
line_x = numx.zeros((1000,2),"d")
line_y = numx.zeros((1000,2),"d")
line_x[:,0] = numx.linspace(-1,1,num=1000,endpoint=1)
line_y[:,1] = numx.linspace(-0.2,0.2,num=1000,endpoint=1)
mat = numx.concatenate((line_x,line_y))
des_var = std(mat,axis=0)
utils.rotate(mat,uniform()*2*numx.pi)
mat += uniform(2)
pca = mdp.nodes.NIPALSNode(conv=1E-15, max_it=1000)
pca.train(mat)
act_mat = pca.execute(mat)
assert_array_almost_equal(mean(act_mat,axis=0),\
[0,0],decimal)
assert_array_almost_equal(std(act_mat,axis=0),\
des_var,decimal)
# test a bug in v.1.1.1, should not crash
pca.inverse(act_mat[:,:1])
# try standard PCA on the same data and compare the eigenvalues
pca2 = mdp.nodes.PCANode()
pca2.train(mat)
pca2.stop_training()
assert_array_almost_equal(pca2.d, pca.d, decimal)
def test_NIPALSNode_desired_variance():
mat, mix, inp = get_random_mix(mat_dim=(1000, 3))
# first make them white
pca = mdp.nodes.WhiteningNode()
pca.train(mat)
mat = pca.execute(mat)
# set the variances
mat *= [0.6,0.3,0.1]
#mat -= mat.mean(axis=0)
pca = mdp.nodes.NIPALSNode(output_dim=0.8)
pca.train(mat)
out = pca.execute(mat)
# check that we got exactly two output_dim:
assert pca.output_dim == 2
assert out.shape[1] == 2
# check that explained variance is > 0.8 and < 1
assert (pca.explained_variance > 0.8 and pca.explained_variance < 1)
def test_LLENode():
# 1D S-shape in 3D
n, k = 50, 2
x, y, z, t = _s_shape_1D(n)
data = numx.asarray([x,y,z]).T
res = mdp.nodes.LLENode(k, output_dim=1, svd=False)(data)
# check that the neighbors are the same
err = _compare_neighbors(data, res, k)
assert err.max() == 0
# with svd=True
res = mdp.nodes.LLENode(k, output_dim=1, svd=True)(data)
err = _compare_neighbors(data, res, k)
assert err.max() == 0
return
#TODO: fix this test!
# 2D S-shape in 3D
nt, ny = 40, 15
n, k = nt*ny, 8
x, y, z, t = _s_shape_2D(nt, ny)
data = numx.asarray([x,y,z]).T
res = mdp.nodes.LLENode(k, output_dim=2, svd=True)(data)
res[:,0] /= res[:,0].std()
res[:,1] /= res[:,1].std()
# test alignment
yval = y[::nt]
tval = t[:ny]
for yv in yval:
idx = numx.nonzero(y==yv)[0]
err = abs(res[idx,1]-res[idx[0],1]).max()
assert err<0.01,\
'Projection should be aligned as original space: %s'%(str(err))
for tv in tval:
idx = numx.nonzero(t==tv)[0]
err = abs(res[idx,0]-res[idx[0],0]).max()
assert err<0.01,\
'Projection should be aligned as original space: %s'%(str(err))
def test_LLENode_outputdim_float_bug():
# 1D S-shape in 3D, output_dim
n, k = 50, 2
x, y, z, t = _s_shape_1D(n)
data = numx.asarray([x,y,z]).T
res = mdp.nodes.LLENode(k, output_dim=0.9, svd=True)(data)
# check that the neighbors are the same
err = _compare_neighbors(data, res, k)
assert err.max() == 0
def test_HLLENode():
# 1D S-shape in 3D
n, k = 250, 4
x, y, z, t = _s_shape_1D(n)
data = numx.asarray([x,y,z]).T
res = mdp.nodes.HLLENode(k, r=0.001, output_dim=1, svd=False)(data)
# check that the neighbors are the same
err = _compare_neighbors(data, res, k)
assert err.max() == 0
# with svd=True
res = mdp.nodes.HLLENode(k, r=0.001, output_dim=1, svd=True)(data)
err = _compare_neighbors(data, res, k)
assert err.max() == 0
# 2D S-shape in 3D
nt, ny = 40, 15
n, k = nt*ny, 8
x, y, z, t = _s_shape_2D(nt, ny)
data = numx.asarray([x,y,z]).T
res = mdp.nodes.HLLENode(k, r=0.001, output_dim=2, svd=False)(data)
res[:,0] /= res[:,0].std()
res[:,1] /= res[:,1].std()
# test alignment
yval = y[::nt]
tval = t[:ny]
for yv in yval:
idx = numx.nonzero(y==yv)[0]
assert numx.all(res[idx,1]-res[idx[0],1]<1e-2),\
'Projection should be aligned as original space'
for tv in tval:
idx = numx.nonzero(t==tv)[0]
assert numx.all(res[idx,0]-res[idx[0],0]<1e-2),\
'Projection should be aligned as original space'
def test_XSFANode():
T = 5000
N = 3
src = numx_rand.random((T, N))*2-1
# create three souces with different speeds
fsrc = numx_fft.rfft(src, axis=0)
for i in xrange(N):
fsrc[(i+1)*(T/10):, i] = 0.
src = numx_fft.irfft(fsrc,axis=0)
src -= src.mean(axis=0)
src /= src.std(axis=0)
#mix = sigmoid(numx.dot(src, mdp.utils.random_rot(3)))
mix = src
flow = mdp.Flow([mdp.nodes.XSFANode()])
# let's test also chunk-mode training
flow.train([[mix[:T/2, :], mix[T/2:, :]]])
out = flow(mix)
#import bimdp
#tr_filename = bimdp.show_training(flow=flow,
# data_iterators=[[mix[:T/2, :], mix[T/2:, :]]])
#ex_filename, out = bimdp.show_execution(flow, x=mix)
corrs = mdp.utils.cov_maxima(mdp.utils.cov2(out, src))
assert min(corrs) > 0.8, ('source/estimate minimal'
' covariance: %g' % min(corrs))
|