tomwalters@273: #!/usr/bin/env python
tomwalters@273: # encoding: utf-8
tomwalters@273: #
tomwalters@273: # AIM-C: A C++ implementation of the Auditory Image Model
tomwalters@273: # http://www.acousticscale.org/AIMC
tomwalters@273: #
tomwalters@318: # Licensed under the Apache License, Version 2.0 (the "License");
tomwalters@318: # you may not use this file except in compliance with the License.
tomwalters@318: # You may obtain a copy of the License at
tomwalters@273: #
tomwalters@318: #     http://www.apache.org/licenses/LICENSE-2.0
tomwalters@273: #
tomwalters@318: # Unless required by applicable law or agreed to in writing, software
tomwalters@318: # distributed under the License is distributed on an "AS IS" BASIS,
tomwalters@318: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
tomwalters@318: # See the License for the specific language governing permissions and
tomwalters@318: # limitations under the License.
tomwalters@273: """
tomwalters@273: ModuleGaussians_test.py
tomwalters@273: 
tomwalters@273: Created by Thomas Walters on 2010-02-15.
tomwalters@273: Copyright 2010 Thomas Walters <tom@acousticscale.org>
tomwalters@273: Test for the Gaussians module. Runs a number of pre-computed SAI profiles
tomwalters@275: through the module, and tests them against the saved output from the
tomwalters@273: MATLAB rubber_GMM code.
tomwalters@273: """
tomwalters@273: 
tomwalters@273: import aimc
tomwalters@274: from scipy import io
tomwalters@273: 
tomwalters@273: def main():
tomwalters@273:   data_file = "src/Modules/Features/testdata/aa153.0p108.1s100.0t+000itd.mat"
tomwalters@274:   data = io.loadmat(data_file)
tomwalters@274:   
tomwalters@274:   # The margin of error allowed between the returned values from AIM-C and
tomwalters@274:   # the stored MATLAB values.
tomwalters@283:   epsilon = 0.00001;
tomwalters@273:   
tomwalters@273:   given_profiles = data["Templates"]
tomwalters@273:   matlab_features = data["feature"]
tomwalters@273:   
tomwalters@274:   (profile_count, channel_count) = given_profiles.shape
tomwalters@273:   
tomwalters@274:   profile_sig = aimc.SignalBank()
tomwalters@274:   profile_sig.Initialize(channel_count, 1, 44100)
tomwalters@274:   parameters = aimc.Parameters()
tomwalters@274:   mod_gauss = aimc.ModuleGaussians(parameters)
tomwalters@274:   mod_gauss.Initialize(profile_sig)
tomwalters@273:   
tomwalters@274:   correct_count = 0;
tomwalters@274:   incorrect_count  = 0;
tomwalters@274:   for p in range(0, profile_count):
tomwalters@274:     profile = given_profiles[p]
tomwalters@274:     features = matlab_features[p]
tomwalters@274:     for i in range(0, channel_count):
tomwalters@274:       profile_sig.set_sample(i, 0, profile[i])
tomwalters@274:     mod_gauss.Process(profile_sig)
tomwalters@274:     out_sig = mod_gauss.GetOutputBank()
tomwalters@274:     error = False;
tomwalters@274:     for j in range(0, out_sig.channel_count()):
tomwalters@274:       if (abs(out_sig.sample(j, 0) - features[j]) > epsilon):
tomwalters@274:         error = True;
tomwalters@274:         incorrect_count += 1;
tomwalters@274:       else:
tomwalters@274:         correct_count += 1;
tomwalters@274:     if error:
tomwalters@274:       print("Mismatch at profile %d" % (p))
tomwalters@274:       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@274:       print("MATLAB values: %f %f %f %f" % (features[0], features[1], features[2], features[3]))
tomwalters@274:       print("")
tomwalters@274:     percent_correct = 100 * correct_count / (correct_count + incorrect_count)
tomwalters@274:   print("Total correct: %f percent" % (percent_correct))
tomwalters@274:   if percent_correct == 100:
tomwalters@274:     print("=== TEST PASSED ===")
tomwalters@274:   else:
tomwalters@274:     print("=== TEST FAILED! ===")
tomwalters@274: 
tomwalters@273:   pass
tomwalters@273: 
tomwalters@273: 
tomwalters@273: if __name__ == '__main__':
tomwalters@273:   main()