view src/Modules/Features/ModuleGaussians_test.py @ 611:0fbaf443ec82

Carfac C++ revision 3, indluding more style improvements. The output structs are now classes again, and have separate storage methods for each output structure along with flags in the Run and RunSegment methods to allow for only storing NAPs if desired.

#!/usr/bin/env python
# encoding: utf-8
#
# AIM-C: A C++ implementation of the Auditory Image Model
# http://www.acousticscale.org/AIMC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
ModuleGaussians_test.py

Created by Thomas Walters on 2010-02-15.
Copyright 2010 Thomas Walters <[e-mail]>
Test for the Gaussians module. Runs a number of pre-computed SAI profiles
through the module, and tests them against the saved output from the
MATLAB rubber_GMM code.
"""

import aimc
from scipy import io

def main():
  data_file = "src/Modules/Features/testdata/aa153.0p108.1s100.0t+000itd.mat"
  data = io.loadmat(data_file)
  
  # The margin of error allowed between the returned values from AIM-C and
  # the stored MATLAB values.
  epsilon = 0.00001;
  
  given_profiles = data["Templates"]
  matlab_features = data["feature"]
  
  (profile_count, channel_count) = given_profiles.shape
  
  profile_sig = aimc.SignalBank()
  profile_sig.Initialize(channel_count, 1, 44100)
  parameters = aimc.Parameters()
  mod_gauss = aimc.ModuleGaussians(parameters)
  mod_gauss.Initialize(profile_sig)
  
  correct_count = 0;
  incorrect_count  = 0;
  for p in range(0, profile_count):
    profile = given_profiles[p]
    features = matlab_features[p]
    for i in range(0, channel_count):
      profile_sig.set_sample(i, 0, profile[i])
    mod_gauss.Process(profile_sig)
    out_sig = mod_gauss.GetOutputBank()
    error = False;
    for j in range(0, out_sig.channel_count()):
      if (abs(out_sig.sample(j, 0) - features[j]) > epsilon):
        error = True;
        incorrect_count += 1;
      else:
        correct_count += 1;
    if error:
      print("Mismatch at profile %d" % (p))
      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)))
      print("MATLAB values: %f %f %f %f" % (features[0], features[1], features[2], features[3]))
      print("")
    percent_correct = 100 * correct_count / (correct_count + incorrect_count)
  print("Total correct: %f percent" % (percent_correct))
  if percent_correct == 100:
    print("=== TEST PASSED ===")
  else:
    print("=== TEST FAILED! ===")

  pass


if __name__ == '__main__':
  main()