def __init__(self):
print("\nChecking confidence tests analytic and shuffle")
self.conf_lev = .9
self.conf_samples = 1000
self.T = 500
self.links_coeffs = {0: [],
1: [],
}
ax = 0.8
ay = 0.9
cxy = .7
links_coeffs = {0: [((0, -1), ax)],
1: [((1, -1), ay), ((0, -1), -cxy)],
}
self.rtol = .1
self.measure_params = {'knn': 10}
numpy.random.seed(42)
data, links = pp.var_process(links_coeffs, T=self.T,
use='inno_cov', verbosity=verbosity)
self.array, self.xyz = te._construct_array(X=[(0, -1)], Y=[(1, 0)],
Z=[(0, -2), (1, -1)],
tau_max=2,
data=data, mask=False)
def __init__(self):
print("\nChecking confidence tests analytic and shuffle")
self.conf_lev = .9
self.conf_samples = 1000
self.T = 500
self.links_coeffs = {
0: [],
1: [],
}
ax = 0.8
ay = 0.9
cxy = .7
links_coeffs = {
0: [((0, -1), ax)],
1: [((1, -1), ay), ((0, -1), -cxy)],
}
self.rtol = .1
self.measure_params = {'knn': 10}
numpy.random.seed(42)
data, links = pp.var_process(links_coeffs,
T=self.T,
use='inno_cov',
verbosity=verbosity)
self.array, self.xyz = te._construct_array(X=[(0, -1)],
Y=[(1, 0)],
Z=[(0, -2), (1, -1)],
tau_max=2,
data=data,
selector=False)
def test_construct_array():
print("\nTesting function '_construct_array' to check whether array is "
"correctly constructed from data.")
data = numpy.array([[0, 10, 20, 30],
[1, 11, 21, 31],
[2, 12, 22, 32],
[3, 13, 23, 33],
[4, 14, 24, 34]])
data_mask = numpy.array([[1, 0, 0, 1],
[1, 1, 1, 1],
[0, 1, 1, 1],
[1, 1, 0, 0],
[1, 1, 1, 1]], dtype='bool')
X = [(1, -1)]
Y = [(0, 0)]
Z = [(0, -1), (1, -2), (2, 0)]
tau_max = 2
# No masking
res = te._construct_array(
X=X, Y=Y, Z=Z,
tau_max=tau_max,
mask=False,
data=data,
data_mask=data_mask,
mask_type=None, verbosity=verbosity)
numpy.testing.assert_almost_equal(res[0],
numpy.array([[11., 12., 13.],
[2., 3., 4.],
[1., 2., 3.],
[10., 11., 12.],
[22., 23., 24.]]))
numpy.testing.assert_almost_equal(res[1], numpy.array([0, 1, 2, 2, 2]))
# masking y
res = te._construct_array(
X=X, Y=Y, Z=Z,
tau_max=tau_max,
mask=True,
data=data,
data_mask=data_mask,
mask_type=['y'], verbosity=verbosity)
numpy.testing.assert_almost_equal(res[0],
numpy.array([[12., 13.],
[3., 4.],
[2., 3.],
[11., 12.],
[23., 24.]]))
numpy.testing.assert_almost_equal(res[1], numpy.array([0, 1, 2, 2, 2]))
# masking all
res = te._construct_array(
X=X, Y=Y, Z=Z,
tau_max=tau_max,
mask=True,
data=data,
data_mask=data_mask,
mask_type=['x', 'y', 'z'], verbosity=verbosity)
numpy.testing.assert_almost_equal(res[0],
numpy.array([[13.],
[4.],
[3.],
[12.],
[24.]]))
numpy.testing.assert_almost_equal(res[1], numpy.array([0, 1, 2, 2, 2]))
def test_construct_array():
print("\nTesting function '_construct_array' to check whether array is "
"correctly constructed from data.")
data = numpy.array([[0, 10, 20, 30], [1, 11, 21, 31], [2, 12, 22, 32],
[3, 13, 23, 33], [4, 14, 24, 34]])
sample_selector = numpy.array(
[[1, 0, 0, 1], [1, 1, 1, 1], [0, 1, 1, 1], [1, 1, 0, 0], [1, 1, 1, 1]],
dtype='bool')
X = [(1, -1)]
Y = [(0, 0)]
Z = [(0, -1), (1, -2), (2, 0)]
tau_max = 2
# No masking
res = te._construct_array(X=X,
Y=Y,
Z=Z,
tau_max=tau_max,
selector=False,
data=data,
sample_selector=sample_selector,
selector_type=None,
verbosity=verbosity)
numpy.testing.assert_almost_equal(
res[0],
numpy.array([[11., 12., 13.], [2., 3., 4.], [1., 2., 3.],
[10., 11., 12.], [22., 23., 24.]]))
numpy.testing.assert_almost_equal(res[1], numpy.array([0, 1, 2, 2, 2]))
# masking y
res = te._construct_array(X=X,
Y=Y,
Z=Z,
tau_max=tau_max,
selector=True,
data=data,
sample_selector=sample_selector,
selector_type=['y'],
verbosity=verbosity)
numpy.testing.assert_almost_equal(
res[0],
numpy.array([[12., 13.], [3., 4.], [2., 3.], [11., 12.], [23., 24.]]))
numpy.testing.assert_almost_equal(res[1], numpy.array([0, 1, 2, 2, 2]))
# masking all
res = te._construct_array(X=X,
Y=Y,
Z=Z,
tau_max=tau_max,
selector=True,
data=data,
sample_selector=sample_selector,
selector_type=['x', 'y', 'z'],
verbosity=verbosity)
numpy.testing.assert_almost_equal(
res[0], numpy.array([[13.], [4.], [3.], [12.], [24.]]))
numpy.testing.assert_almost_equal(res[1], numpy.array([0, 1, 2, 2, 2]))
