scope trainVAD.

sclass TrainVAD {
  replace Recognizer with F1<BWImage, Bool>.
    
  Best<Recognizer> best = bestWithPrintStruct();
  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);
  }
}

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

static F1<BWImage, Bool> trainVAD() {
  ret TrainVAD().go();
}

end scope