def test_ccipcanode_v1():
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))
utils.rotate(mat, uniform() * 2 * numx.pi)
mat += uniform(2)
mat -= mat.mean(axis=0)
pca = CCIPCANode()
for i in range(5):
pca.train(mat)
bpca = PCANode()
bpca.train(mat)
bpca.stop_training()
v = pca.get_projmatrix()
bv = bpca.get_projmatrix()
dcosines = numx.zeros(v.shape[1])
for dim in range(v.shape[1]):
dcosines[dim] = numx.fabs(numx.dot(v[:, dim], bv[:, dim].T)) / (
numx.linalg.norm(v[:, dim]) * numx.linalg.norm(bv[:, dim]))
assert_almost_equal(numx.ones(v.shape[1]), dcosines)
def test_ccipcanode_v2():
iterval = 30
t = numx.linspace(0, 4 * numx.pi, 500)
x = numx.zeros([t.shape[0], 2])
x[:, 0] = numx.real(numx.sin(t) + numx.power(numx.cos(11 * t), 2))
x[:, 1] = numx.cos(11 * t)
expnode = PolynomialExpansionNode(2)
input_data = expnode(x)
input_data = input_data - input_data.mean(axis=0)
##Setup node/trainer
output_dim = 4
node = CCIPCANode(output_dim=output_dim)
bpcanode = PCANode(output_dim=output_dim)
bpcanode(input_data)
# bv = bpcanode.v / numx.linalg.norm(bpcanode.v, axis=0)
bv = bpcanode.v / mdp.numx.sum(bpcanode.v ** 2, axis=0) ** 0.5
v = []
_tcnt = time.time()
for i in range(iterval * input_data.shape[0]):
node.train(input_data[i % input_data.shape[0]:i % input_data.shape[0] + 1])
if (node.get_current_train_iteration() % 100 == 0):
v.append(node.v)
dcosines = numx.zeros([len(v), output_dim])
for i in range(len(v)):
for dim in range(output_dim):
dcosines[i, dim] = numx.fabs(numx.dot(v[i][:, dim], bv[:, dim].T)) / (
numx.linalg.norm(v[i][:, dim]) * numx.linalg.norm(bv[:, dim]))
print('\nTotal Time for {} iterations: {}'.format(iterval, time.time() - _tcnt))
assert_almost_equal(numx.ones(output_dim), dcosines[-1], decimal=3)
def test_ccipcanode_v1():
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))
utils.rotate(mat, uniform() * 2 * numx.pi)
mat += uniform(2)
mat -= mat.mean(axis=0)
pca = CCIPCANode()
for i in xrange(5):
pca.train(mat)
bpca = PCANode()
bpca.train(mat)
bpca.stop_training()
v = pca.get_projmatrix()
bv = bpca.get_projmatrix()
dcosines = numx.zeros(v.shape[1])
for dim in xrange(v.shape[1]):
dcosines[dim] = numx.fabs(numx.dot(v[:, dim], bv[:, dim].T)) / (
numx.linalg.norm(v[:, dim]) * numx.linalg.norm(bv[:, dim]))
assert_almost_equal(numx.ones(v.shape[1]), dcosines)
def test_ccipcanode_numx_rng():
x = mdp.numx_rand.randn(100, 5)
numx_rng = mdp.numx_rand.RandomState(seed=10)
node1 = CCIPCANode(numx_rng=numx_rng)
node1.train(x)
init_v1 = node1.init_eigen_vectors
v1 = node1.get_projmatrix()
numx_rng = mdp.numx_rand.RandomState(seed=10)
node2 = CCIPCANode(numx_rng=numx_rng)
node2.train(x)
init_v2 = node2.init_eigen_vectors
v2 = node2.get_projmatrix()
assert_array_equal(init_v1, init_v2)
assert_array_equal(v1, v2)
def test_ccipcanode_v2():
iterval = 30
t = numx.linspace(0, 4 * numx.pi, 500)
x = numx.zeros([t.shape[0], 2])
x[:, 0] = numx.real(numx.sin(t) + numx.power(numx.cos(11 * t), 2))
x[:, 1] = numx.cos(11 * t)
expnode = PolynomialExpansionNode(2)
input_data = expnode(x)
input_data = input_data - input_data.mean(axis=0)
##Setup node/trainer
output_dim = 4
node = CCIPCANode(output_dim=output_dim)
bpcanode = PCANode(output_dim=output_dim)
bpcanode(input_data)
# bv = bpcanode.v / numx.linalg.norm(bpcanode.v, axis=0)
bv = bpcanode.v / mdp.numx.sum(bpcanode.v**2, axis=0)**0.5
v = []
_tcnt = time.time()
for i in xrange(iterval * input_data.shape[0]):
node.train(input_data[i % input_data.shape[0]:i % input_data.shape[0] +
1])
if (node.get_current_train_iteration() % 100 == 0):
v.append(node.v)
dcosines = numx.zeros([len(v), output_dim])
for i in xrange(len(v)):
for dim in xrange(output_dim):
dcosines[i,
dim] = numx.fabs(numx.dot(v[i][:, dim], bv[:, dim].T)) / (
numx.linalg.norm(v[i][:, dim]) *
numx.linalg.norm(bv[:, dim]))
print
print 'Total Time for %d iterations: ' % (iterval), time.time() - _tcnt
assert_almost_equal(numx.ones(output_dim), dcosines[-1], decimal=3)
def test_ccipcanode_numx_rng():
x = mdp.numx_rand.randn(100, 5)
numx_rng = mdp.numx_rand.RandomState(seed=10)
node1 = CCIPCANode(numx_rng=numx_rng)
node1.train(x)
init_v1 = node1.init_eigen_vectors
v1 = node1.get_projmatrix()
numx_rng = mdp.numx_rand.RandomState(seed=10)
node2 = CCIPCANode(numx_rng=numx_rng)
node2.train(x)
init_v2 = node2.init_eigen_vectors
v2 = node2.get_projmatrix()
assert_array_equal(init_v1, init_v2)
assert_array_equal(v1, v2)