3 files changed, 0 insertions, 119 deletions
diff --git a/test/test_regression/regression_random_results_cSPIKY.mat b/test/test_regression/regression_random_results_cSPIKY.mat
deleted file mode 100644
index 47c9b50..0000000
--- a/test/test_regression/regression_random_results_cSPIKY.mat
+++ /dev/null
diff --git a/test/test_regression/regression_random_spikes.mat b/test/test_regression/regression_random_spikes.mat
deleted file mode 100644
index e5ebeb1..0000000
--- a/test/test_regression/regression_random_spikes.mat
+++ /dev/null
diff --git a/test/test_regression/test_regression_random_spikes.py b/test/test_regression/test_regression_random_spikes.py
deleted file mode 100644
index dfd5488..0000000
--- a/test/test_regression/test_regression_random_spikes.py
+++ /dev/null
@@ -1,119 +0,0 @@
-""" 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/test_regression/regression_random_spikes.mat"
-    spikes_name = "spikes"
-    result_name = "Distances"
-    result_file = "test/test_regression/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)
-        spike = spk.spike_distance_multi(spike_trains)
-        # spike_sync = spk.spike_sync_multi(spike_trains)
-
-        assert_almost_equal(isi, 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)
-
-
-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 = []
-    for spikes in spike_train_data[0]:
-        print("Spikes:", spikes.flatten())
-        spike_trains.append(spk.SpikeTrain(spikes.flatten(), 100.0))
-
-    print(spk.spike_distance_multi(spike_trains))
-
-    print(results_cSPIKY[index][1])
-
-    print(spike_trains[1].spikes)
-
-
-if __name__ == "__main__":
-
-    test_regression_random()
-    # check_regression_dataset()
-    # check_single_spike_train_set(7633)