diff options
Diffstat (limited to 'test/numeric')
-rw-r--r-- | test/numeric/regression_random_results_cSPIKY.mat | bin | 0 -> 149104 bytes | |||
-rw-r--r-- | test/numeric/regression_random_spikes.mat | bin | 0 -> 16241579 bytes | |||
-rw-r--r-- | test/numeric/test_regression_random_spikes.py | 138 |
3 files changed, 138 insertions, 0 deletions
diff --git a/test/numeric/regression_random_results_cSPIKY.mat b/test/numeric/regression_random_results_cSPIKY.mat Binary files differnew file mode 100644 index 0000000..26f29ff --- /dev/null +++ b/test/numeric/regression_random_results_cSPIKY.mat diff --git a/test/numeric/regression_random_spikes.mat b/test/numeric/regression_random_spikes.mat Binary files differnew file mode 100644 index 0000000..e5ebeb1 --- /dev/null +++ b/test/numeric/regression_random_spikes.mat diff --git a/test/numeric/test_regression_random_spikes.py b/test/numeric/test_regression_random_spikes.py new file mode 100644 index 0000000..73d39a6 --- /dev/null +++ b/test/numeric/test_regression_random_spikes.py @@ -0,0 +1,138 @@ +""" regression benchmark + +Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License +""" +from __future__ import print_function + +import numpy as np +from scipy.io import loadmat +import pyspike as spk + +from numpy.testing import assert_almost_equal + +spk.disable_backend_warning = True + + +def test_regression_random(): + + spike_file = "test/numeric/regression_random_spikes.mat" + spikes_name = "spikes" + result_name = "Distances" + result_file = "test/numeric/regression_random_results_cSPIKY.mat" + + spike_train_sets = loadmat(spike_file)[spikes_name][0] + results_cSPIKY = loadmat(result_file)[result_name] + + for i, spike_train_data in enumerate(spike_train_sets): + spike_trains = [] + for spikes in spike_train_data[0]: + spike_trains.append(spk.SpikeTrain(spikes.flatten(), 100.0)) + + isi = spk.isi_distance_multi(spike_trains) + isi_prof = spk.isi_profile_multi(spike_trains).avrg() + + spike = spk.spike_distance_multi(spike_trains) + spike_prof = spk.spike_profile_multi(spike_trains).avrg() + + spike_sync = spk.spike_sync_multi(spike_trains) + spike_sync_prof = spk.spike_sync_profile_multi(spike_trains).avrg() + + assert_almost_equal(isi, results_cSPIKY[i][0], decimal=14, + err_msg="Index: %d, ISI" % i) + assert_almost_equal(isi_prof, results_cSPIKY[i][0], decimal=14, + err_msg="Index: %d, ISI" % i) + + assert_almost_equal(spike, results_cSPIKY[i][1], decimal=14, + err_msg="Index: %d, SPIKE" % i) + assert_almost_equal(spike_prof, results_cSPIKY[i][1], decimal=14, + err_msg="Index: %d, SPIKE" % i) + + assert_almost_equal(spike_sync, spike_sync_prof, decimal=14, + err_msg="Index: %d, SPIKE-Sync" % i) + + +def check_regression_dataset(spike_file="benchmark.mat", + spikes_name="spikes", + result_file="results_cSPIKY.mat", + result_name="Distances"): + """ Debuging function """ + np.set_printoptions(precision=15) + + spike_train_sets = loadmat(spike_file)[spikes_name][0] + + results_cSPIKY = loadmat(result_file)[result_name] + + err_max = 0.0 + err_max_ind = -1 + err_count = 0 + + for i, spike_train_data in enumerate(spike_train_sets): + spike_trains = [] + for spikes in spike_train_data[0]: + spike_trains.append(spk.SpikeTrain(spikes.flatten(), 100.0)) + + isi = spk.isi_distance_multi(spike_trains) + spike = spk.spike_distance_multi(spike_trains) + # spike_sync = spk.spike_sync_multi(spike_trains) + + if abs(isi - results_cSPIKY[i][0]) > 1E-14: + print("Error in ISI:", i, isi, results_cSPIKY[i][0]) + print("Spike trains:") + for st in spike_trains: + print(st.spikes) + + err = abs(spike - results_cSPIKY[i][1]) + if err > 1E-14: + err_count += 1 + if err > err_max: + err_max = err + err_max_ind = i + + print("Total Errors:", err_count) + + if err_max_ind > -1: + print("Max SPIKE distance error:", err_max, "at index:", err_max_ind) + spike_train_data = spike_train_sets[err_max_ind] + for spikes in spike_train_data[0]: + print(spikes.flatten()) + + +def check_single_spike_train_set(index): + """ Debuging function """ + np.set_printoptions(precision=15) + spike_file = "regression_random_spikes.mat" + spikes_name = "spikes" + result_name = "Distances" + result_file = "regression_random_results_cSPIKY.mat" + + spike_train_sets = loadmat(spike_file)[spikes_name][0] + + results_cSPIKY = loadmat(result_file)[result_name] + + spike_train_data = spike_train_sets[index] + + spike_trains = [] + N = 0 + for spikes in spike_train_data[0]: + N += len(spikes.flatten()) + print("Spikes:", len(spikes.flatten())) + spikes_array = spikes.flatten() + if len(spikes_array > 0) and (spikes_array[-1] > 100.0): + spikes_array[-1] = 100.0 + spike_trains.append(spk.SpikeTrain(spikes_array, 100.0)) + print(spike_trains[-1].spikes) + + print(N) + + print(spk.spike_sync_multi(spike_trains)) + + print(spk.spike_sync_profile_multi(spike_trains).integral()) + + +if __name__ == "__main__": + + # test_regression_random() + # check_regression_dataset() + check_single_spike_train_set(4) |