scope trainVAD.

sclass TrainVAD {
  replace Recognizer with F1<BWImage, Bool>.
    
  new Best<Recognizer> best;
  LPair<BWImage, Bool> trainingList;
  int clipLength;
    
  Recognizer go() {
    final BWImage img1 = frequencyImage(voiceMegaMix());
    final BWImage img2 = frequencyImage(nonVoiceMegaMix());
    clipLength = iround(2.0*spectro_pixelsPerSecond());
    int stepSize = clipLength/4;
    print(+clipLength);
    L<BWImage> images1 = map(func(IntRange r) -> BWImage { bwHorizontalClip(img1, r) }, stepIntRange(clipLength, intRange(0, img1.getWidth()), stepSize));
    L<BWImage> images2 = map(func(IntRange r) -> BWImage { bwHorizontalClip(img2, r) }, stepIntRange(clipLength, intRange(0, img2.getWidth()), stepSize));
    print(allImageSizes(concatLists(images1, images2)));
    print(l(images1) + " + " + l(images2) + " images");
    
    trainingList = trueFalseBPairs(images1, images2);
    
    // dummy recognizers first
    
    test(func(BWImage img) -> bool { false });
    test(func(BWImage img) -> bool { true });
    
    // now actual ones
    for (int streakLength = 1; streakLength <= clipLength/2; streakLength++)
      test(nu(Rec1, +streakLength));
    
    ret best!;
  }
  
  void test(Recognizer r) {
    scorePreciseRecognizer(best, r, trainingList);
  }
  
  void showErrors(Recognizer r) {
    if (r == null) ret;
    new L<BWImage> errorImages;
    for (Pair<BWImage, Bool> p : trainingList)
      if (recognizer.get(p.a) != p.b)
        errorImages.add(p.a);
    if (nempty(errorImages))
      showImage("Errors", mergeBWImagesVertically(errorImages));
  }
}

sclass #Rec1 extends F1<BWImage, Bool> {
  int streakLength;
  
  public Bool get(BWImage img) {
    ret hasIntRangesLongerThan(streakLength, audio_streaksUsingBand_v1(img,
      audio_bestBandForEntryPoints()));
  }
  
  toString { ret struct(this); }
}

static F1<BWImage, Bool> trainVAD() {
  new TrainVAD() t;
  F1<BWImage, Bool> best = t.go();
  t.printErrors(best);
  ret best;
}

end scope