tomwalters@1: #!/usr/bin/env python
tomwalters@1: # encoding: utf-8
tomwalters@1: #
tomwalters@1: # AIM-C: A C++ implementation of the Auditory Image Model
tomwalters@1: # http://www.acousticscale.org/AIMC
tomwalters@1: #
tomwalters@1: # This program is free software: you can redistribute it and/or modify
tomwalters@1: # it under the terms of the GNU General Public License as published by
tomwalters@1: # the Free Software Foundation, either version 3 of the License, or
tomwalters@1: # (at your option) any later version.
tomwalters@1: #
tomwalters@1: # This program is distributed in the hope that it will be useful,
tomwalters@1: # but WITHOUT ANY WARRANTY; without even the implied warranty of
tomwalters@1: # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
tomwalters@1: # GNU General Public License for more details.
tomwalters@1: #
tomwalters@1: # You should have received a copy of the GNU General Public License
tomwalters@1: # along with this program.  If not, see <http://www.gnu.org/licenses/>.
tomwalters@1: """
tomwalters@1: ModuleGaussians_test.py
tomwalters@1: 
tomwalters@1: Created by Thomas Walters on 2010-02-15.
tomwalters@1: Copyright 2010 Thomas Walters <tom@acousticscale.org>
tomwalters@1: Test for the Gaussians module. Runs a number of pre-computed SAI profiles
tomwalters@3: through the module, and tests them against the saved output from the
tomwalters@1: MATLAB rubber_GMM code.
tomwalters@1: """
tomwalters@1: 
tomwalters@1: import aimc
tomwalters@2: from scipy import io
tomwalters@1: 
tomwalters@1: def main():
tomwalters@1:   data_file = "src/Modules/Features/testdata/aa153.0p108.1s100.0t+000itd.mat"
tomwalters@2:   data = io.loadmat(data_file)
tomwalters@2:   
tomwalters@2:   # The margin of error allowed between the returned values from AIM-C and
tomwalters@2:   # the stored MATLAB values.
tomwalters@11:   epsilon = 0.00001;
tomwalters@1:   
tomwalters@1:   given_profiles = data["Templates"]
tomwalters@1:   matlab_features = data["feature"]
tomwalters@1:   
tomwalters@2:   (profile_count, channel_count) = given_profiles.shape
tomwalters@1:   
tomwalters@2:   profile_sig = aimc.SignalBank()
tomwalters@2:   profile_sig.Initialize(channel_count, 1, 44100)
tomwalters@2:   parameters = aimc.Parameters()
tomwalters@2:   mod_gauss = aimc.ModuleGaussians(parameters)
tomwalters@2:   mod_gauss.Initialize(profile_sig)
tomwalters@1:   
tomwalters@2:   correct_count = 0;
tomwalters@2:   incorrect_count  = 0;
tomwalters@2:   for p in range(0, profile_count):
tomwalters@2:     profile = given_profiles[p]
tomwalters@2:     features = matlab_features[p]
tomwalters@2:     for i in range(0, channel_count):
tomwalters@2:       profile_sig.set_sample(i, 0, profile[i])
tomwalters@2:     mod_gauss.Process(profile_sig)
tomwalters@2:     out_sig = mod_gauss.GetOutputBank()
tomwalters@2:     error = False;
tomwalters@2:     for j in range(0, out_sig.channel_count()):
tomwalters@2:       if (abs(out_sig.sample(j, 0) - features[j]) > epsilon):
tomwalters@2:         error = True;
tomwalters@2:         incorrect_count += 1;
tomwalters@2:       else:
tomwalters@2:         correct_count += 1;
tomwalters@2:     if error:
tomwalters@2:       print("Mismatch at profile %d" % (p))
tomwalters@2:       print("AIM-C values: %f %f %f %f" % (out_sig.sample(0, 0), out_sig.sample(1, 0), out_sig.sample(2, 0), out_sig.sample(3, 0)))
tomwalters@2:       print("MATLAB values: %f %f %f %f" % (features[0], features[1], features[2], features[3]))
tomwalters@2:       print("")
tomwalters@2:     percent_correct = 100 * correct_count / (correct_count + incorrect_count)
tomwalters@2:   print("Total correct: %f percent" % (percent_correct))
tomwalters@2:   if percent_correct == 100:
tomwalters@2:     print("=== TEST PASSED ===")
tomwalters@2:   else:
tomwalters@2:     print("=== TEST FAILED! ===")
tomwalters@2: 
tomwalters@1:   pass
tomwalters@1: 
tomwalters@1: 
tomwalters@1: if __name__ == '__main__':
tomwalters@1:   main()