The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory.
def test_random_data():
"""Smoke kNN test with big random dataset."""
n_points = 1000
n_dims = 5
pointset1 = np.random.randn(n_points, n_dims).astype('float32')
pointset2 = pointset1
# Call MI estimator
mi, dist1, npoints_x, npoints_y = EST_MI.estimate(
pointset1, pointset2, n_chunks=1)
# Call CMI estimator with pointset2 as conditional (otherwise the MI
# estimator is called internally and the CMI estimator is never tested).
cmi, dist2, npoints_x, npoints_y, npoints_c = EST_CMI.estimate(
pointset1, pointset2, pointset2, n_chunks=1)
assert np.all(np.isclose(dist1, dist2)), (
'High- and low-level calls returned different distances.')
E AssertionError: High- and low-level calls returned different distances.
E assert False
E + where False = <function all at 0x7f411888cf28>(array([ True, True, True, ..., True, True, True]))
E + where <function all at 0x7f411888cf28> = np.all
E + and array([ True, True, True, ..., True, True, True]) = <function isclose at 0x7f41188781e0>(array([0.58077002, 0.91198087, 0.26068008, ..., 0.0625 , 0.0625 ,\n 0.0625 ]), array([0.58077002, 0.91198087, 0.26068008, ..., 0.0625 , 0.0625 ,\n 0.0625 ]))
E + where <function isclose at 0x7f41188781e0> = np.isclose
With a fresh install of IDTxl on: Ubuntu 16.04.5 AMD Carrizo APU amdgpu-pro 18.30 I get the following failure when running the pytests:
================================================================ FAILURES ================================================================ ____ test_random_data ____
test_neighbour_search_opencl.py:157: AssertionError