def test_mpdist_vect_k(T_A, T_B, k):
m = 3
n_A = T_A.shape[0]
n_B = T_B.shape[0]
j = n_A - m + 1 # `k` is reserved for `P_ABBA` selection
P_ABBA = np.empty(2 * j, dtype=np.float64)
ref_mpdist_vect = np.empty(n_B - n_A + 1)
k = min(int(k), P_ABBA.shape[0] - 1)
for i in range(n_B - n_A + 1):
P_ABBA[:j] = naive.stump(T_A, m, T_B[i:i + n_A])[:, 0]
P_ABBA[j:] = naive.stump(T_B[i:i + n_A], m, T_A)[:, 0]
P_ABBA.sort()
ref_mpdist_vect[i] = P_ABBA[min(k, P_ABBA.shape[0] - 1)]
comp_mpdist_vect = _mpdist_vect(T_A, T_B, m, k=k)
npt.assert_almost_equal(ref_mpdist_vect, comp_mpdist_vect)
def test_mpdist_vect(T_A, T_B):
m = 3
n_A = T_A.shape[0]
n_B = T_B.shape[0]
j = n_A - m + 1 # `k` is reserved for `P_ABBA` selection
P_ABBA = np.empty(2 * j, dtype=np.float64)
ref_mpdist_vect = np.empty(n_B - n_A + 1)
percentage = 0.05
k = min(math.ceil(percentage * (2 * n_A)), 2 * j - 1)
k = min(int(k), P_ABBA.shape[0] - 1)
for i in range(n_B - n_A + 1):
P_ABBA[:j] = naive.stump(T_A, m, T_B[i:i + n_A])[:, 0]
P_ABBA[j:] = naive.stump(T_B[i:i + n_A], m, T_A)[:, 0]
P_ABBA.sort()
ref_mpdist_vect[i] = P_ABBA[k]
comp_mpdist_vect = _mpdist_vect(T_A, T_B, m)
npt.assert_almost_equal(ref_mpdist_vect, comp_mpdist_vect)
def test_mpdist_vect_k(T_A, T_B, k):
m = 3
ref_mpdist_vect = naive.mpdist_vect(T_A, T_B, m, k=k)
comp_mpdist_vect = _mpdist_vect(T_A, T_B, m, k=k)
npt.assert_almost_equal(ref_mpdist_vect, comp_mpdist_vect)
def test_mpdist_vect_percentage(T_A, T_B, percentage):
m = 3
ref_mpdist_vect = naive.mpdist_vect(T_A, T_B, m, percentage=percentage)
comp_mpdist_vect = _mpdist_vect(T_A, T_B, m, percentage=percentage)
npt.assert_almost_equal(ref_mpdist_vect, comp_mpdist_vect)
