import numpy as np
import scipr
from scipr.matching import Closest, Hungarian, Greedy, MNN
from scipr.transform import Affine, Rigid, StackedAutoEncoder
np.random.seed(1817)
A = np.random.random((10, 100))
B = np.random.random((20, 100))
matchers = [Closest(), Greedy(), Hungarian(), MNN()]
for match in matchers:
transform = Affine()
model = scipr.SCIPR(match, transform, n_iter=2)
model.fit(A, B)
A_new = model.transform(A)
assert(np.all(np.not_equal(A, A_new)))
def test_009_greedy_D2C_alpha_05(self):
"""Test greedy matching strategy D -> C with alpha=0.5"""
match = Greedy(alpha=0.5, beta=2)
d_idx, c_idx, distances = match(self.D, self.C, self.kd_C)
self.assertTrue(np.array_equal(d_idx, [0, 1]))
self.assertTrue(np.array_equal(c_idx, [0, 0]))
def test_007_greedy_D2C_beta_1(self):
"""Test greedy matching strategy D -> C with beta=1"""
match = Greedy(alpha=1, beta=1)
d_idx, c_idx, distances = match(self.D, self.C, self.kd_C)
self.assertTrue(np.array_equal(d_idx, [0, 1, 2]))
self.assertTrue(np.array_equal(c_idx, [0, 2, 1]))
def test_008_greedy_C2D_alpha_05(self):
"""Test greedy matching strategy C -> D with alpha=0.5"""
match = Greedy(alpha=0.5, beta=2)
c_idx, d_idx, distances = match(self.C, self.D, self.kd_D)
self.assertTrue(np.array_equal(c_idx, [0, 1]))
self.assertTrue(np.array_equal(d_idx, [0, 2]))
def test_006_greedy_C2D_beta_1(self):
"""Test greedy matching strategy C -> D with beta=1"""
match = Greedy(alpha=1, beta=1)
c_idx, d_idx, distances = match(self.C, self.D, self.kd_D)
self.assertTrue(np.array_equal(c_idx, [0, 1, 2]))
self.assertTrue(np.array_equal(d_idx, [0, 2, 1]))