How to use the dtaidistance.dtw_c.distance_nogil function in dtaidistance

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github wannesm / dtaidistance / tests / test_benchmark.py View on Github external
def d():
        return dtw_c.distance_nogil(s1, s2)
github wannesm / dtaidistance / tests / test_bugs.py View on Github external
def test_distance2_aa():
    dist_opts = {'max_dist': 0.1}
    s1 = np.array([0.0, 0.0, 2.0, 1.0, 1.0, 0.0, 0.0])
    s2 = np.array([0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0])
    d1 = dtw.distance(s1, s2, **dist_opts)
    d2 = dtw_c.distance_nogil(s1, s2, **dist_opts)
    print(d1, d2)
    assert d1 == d2
    assert d1 == pytest.approx(np.inf)
github wannesm / dtaidistance / tests / test_bugs.py View on Github external
def test_distance3_a():
    dist_opts = {"penalty": 0.005, "max_step": 0.011, "window": 3}
    s = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.005, 0.01, 0.015, 0.02, 0.01, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
    p = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.005, 0.01, 0.015, 0.02, 0.01, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
    d1 = dtw.distance(s, p, **dist_opts)
    d2 = dtw_c.distance_nogil(s, p, **dist_opts)
    assert d1 == pytest.approx(d2)
github wannesm / dtaidistance / tests / test_bugs.py View on Github external
def test_distance1_a():
    # dist_opts = {'max_dist': 0.201, 'max_step': 0.011, 'max_length_diff': 8, 'window': 3}
    dist_opts = {'window': 3}
    s1 = np.array([ 0., 0.01, 0.,   0.01, 0., 0.,   0.,   0.01, 0.01, 0.02, 0.,  0.])
    s2 = np.array([ 0., 0.02, 0.02, 0.,   0., 0.01, 0.01, 0.,   0.,   0.,   0.])
    d1 = dtw.distance(s1, s2, **dist_opts)
    d2 = dtw_c.distance_nogil(s1, s2, **dist_opts)
    assert d1 == d2
    assert d1 == pytest.approx(0.02)
github wannesm / dtaidistance / tests / test_bugs.py View on Github external
def test_distance1_b():
    dist_opts = {}
    s1 = np.array([ 0., 0.01, 0.,   0.01, 0., 0.,   0.,   0.01, 0.01, 0.02, 0.,  0.])
    s2 = np.array([ 0., 0.02, 0.02, 0.,   0., 0.01, 0.01, 0.,   0.,   0.,   0.])
    d1 = dtw.distance(s1, s2, **dist_opts)
    d2 = dtw_c.distance_nogil(s1, s2, **dist_opts)
    assert d1 == d2
    assert d1 == pytest.approx(0.02)
github wannesm / dtaidistance / tests / test_bugs.py View on Github external
def test_distance2_bb():
    dist_opts = {'max_step': 0.1}
    s1 = np.array([0.0, 0.0, 2.0, 1.0, 1.0, 0.0, 0.0])
    s2 = np.array([0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0])
    d1 = dtw.distance(s1, s2, **dist_opts)
    d2 = dtw_c.distance_nogil(s1, s2, **dist_opts)
    print(d1, d2)
    assert d1 == d2
    assert d1 == pytest.approx(np.inf)
github wannesm / dtaidistance / tests / test_bugs.py View on Github external
def test_distance4():
    try:
        import pandas as pd
    except ImportError:
        # If no pandas, ignore test (not a required dependency)
        return
    s = [[0.,    0.,   0.,    0.,   0.,   0., 0., 0., 0., 0., 0., 0., 0.],
         [0.005, 0.01, 0.015, 0.02, 0.01, 0., 0., 0., 0., 0., 0., 0., 0.],
         [0.,    0.,   0.,    0.,   0.,   0., 0., 0., 0., 0., 0., 0., 0.]]
    p = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
    df = pd.DataFrame(data=s)
    s = df.values
    for i in range(s.shape[0]):
        ss = s[i]  # ss will not be C contiguous memory layout
        d = dtw_c.distance_nogil(ss, p)
        # print(d)