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author | Mario Mulansky <mario.mulansky@gmx.net> | 2014-10-16 14:50:26 +0200 |
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committer | Mario Mulansky <mario.mulansky@gmx.net> | 2014-10-16 14:50:26 +0200 |
commit | 5970a9cfdbecc1af232b7ffe485bdc057591a2b8 (patch) | |
tree | 4ec6c23cd624bb33b0e87821541689874e659983 /test/test_distance.py | |
parent | d869d4d822c651ea3d094eaf17ba7732bf91136f (diff) |
added spike_matrix, refactoring dist matrix functs
Diffstat (limited to 'test/test_distance.py')
-rw-r--r-- | test/test_distance.py | 62 |
1 files changed, 50 insertions, 12 deletions
diff --git a/test/test_distance.py b/test/test_distance.py index 2a6bf4e..7be0d9b 100644 --- a/test/test_distance.py +++ b/test/test_distance.py @@ -130,7 +130,7 @@ def test_spike(): decimal=16) -def check_multi_distance(dist_func, dist_func_multi): +def check_multi_profile(profile_func, profile_func_multi): # generate spike trains: t1 = spk.add_auxiliary_spikes(np.array([0.2, 0.4, 0.6, 0.7]), 1.0) t2 = spk.add_auxiliary_spikes(np.array([0.3, 0.45, 0.8, 0.9, 0.95]), 1.0) @@ -138,21 +138,21 @@ def check_multi_distance(dist_func, dist_func_multi): t4 = spk.add_auxiliary_spikes(np.array([0.1, 0.4, 0.5, 0.6]), 1.0) spike_trains = [t1, t2, t3, t4] - f12 = dist_func(t1, t2) - f13 = dist_func(t1, t3) - f14 = dist_func(t1, t4) - f23 = dist_func(t2, t3) - f24 = dist_func(t2, t4) - f34 = dist_func(t3, t4) + f12 = profile_func(t1, t2) + f13 = profile_func(t1, t3) + f14 = profile_func(t1, t4) + f23 = profile_func(t2, t3) + f24 = profile_func(t2, t4) + f34 = profile_func(t3, t4) - f_multi = dist_func_multi(spike_trains, [0, 1]) + f_multi = profile_func_multi(spike_trains, [0, 1]) assert f_multi.almost_equal(f12, decimal=14) f = copy(f12) f.add(f13) f.add(f23) f.mul_scalar(1.0/3) - f_multi = dist_func_multi(spike_trains, [0, 1, 2]) + f_multi = profile_func_multi(spike_trains, [0, 1, 2]) assert f_multi.almost_equal(f, decimal=14) f.mul_scalar(3) # revert above normalization @@ -160,16 +160,54 @@ def check_multi_distance(dist_func, dist_func_multi): f.add(f24) f.add(f34) f.mul_scalar(1.0/6) - f_multi = dist_func_multi(spike_trains) + f_multi = profile_func_multi(spike_trains) assert f_multi.almost_equal(f, decimal=14) def test_multi_isi(): - check_multi_distance(spk.isi_profile, spk.isi_profile_multi) + check_multi_profile(spk.isi_profile, spk.isi_profile_multi) def test_multi_spike(): - check_multi_distance(spk.spike_profile, spk.spike_profile_multi) + check_multi_profile(spk.spike_profile, spk.spike_profile_multi) + + +def check_dist_matrix(dist_func, dist_matrix_func): + # generate spike trains: + t1 = spk.add_auxiliary_spikes(np.array([0.2, 0.4, 0.6, 0.7]), 1.0) + t2 = spk.add_auxiliary_spikes(np.array([0.3, 0.45, 0.8, 0.9, 0.95]), 1.0) + t3 = spk.add_auxiliary_spikes(np.array([0.2, 0.4, 0.6]), 1.0) + t4 = spk.add_auxiliary_spikes(np.array([0.1, 0.4, 0.5, 0.6]), 1.0) + spike_trains = [t1, t2, t3, t4] + + f12 = dist_func(t1, t2) + f13 = dist_func(t1, t3) + f14 = dist_func(t1, t4) + f23 = dist_func(t2, t3) + f24 = dist_func(t2, t4) + f34 = dist_func(t3, t4) + + f_matrix = dist_matrix_func(spike_trains) + # check zero diagonal + for i in xrange(4): + assert_equal(0.0, f_matrix[i, i]) + for i in xrange(4): + for j in xrange(i+1, 4): + assert_equal(f_matrix[i, j], f_matrix[j, i]) + assert_equal(f12, f_matrix[1, 0]) + assert_equal(f13, f_matrix[2, 0]) + assert_equal(f14, f_matrix[3, 0]) + assert_equal(f23, f_matrix[2, 1]) + assert_equal(f24, f_matrix[3, 1]) + assert_equal(f34, f_matrix[3, 2]) + + +def test_isi_matrix(): + check_dist_matrix(spk.isi_distance, spk.isi_distance_matrix) + + +def test_spike_matrix(): + check_dist_matrix(spk.spike_distance, spk.spike_distance_matrix) def test_regression_spiky(): |