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""" regression benchmark
Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net>
Distributed under the BSD License
"""
from __future__ import print_function
import os
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
max_trr_trials = 100 # speed things up
def test_regression_random():
spike_file = os.path.join("test", "numeric", "regression_random_spikes.mat")
spikes_name = "spikes"
result_name = "Distances"
result_file = os.path.join("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):
if i >= max_trr_trials:
break
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):
if i >= max_trr_trials:
break
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 = os.path.join("test", "numeric", "regression_random_spikes.mat")
spikes_name = "spikes"
result_name = "Distances"
result_file = os.path.join("test", "numeric", "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)
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