Package ru.autosome.commons.backgroundModel.mono

Examples of ru.autosome.commons.backgroundModel.mono.WordwiseBackground


  protected void extract_background(String str) {
    background = Background.fromString(str);
  }
  @Override
  protected void initialize_default_background() {
    background = new WordwiseBackground();
  }
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  public PCM2PWM() {
    super();
  }

  protected BackgroundModel defaultBackground() {
    return new WordwiseBackground();
  }
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    // But precalculation step should be done once
    Discretizer discretizer = new Discretizer(10000.0);
    // Wordwise background means that we calculate number of words instead of probabilities, this is a default mode
    // If one need to work with certain nucleotide background probabilities he should use:
    // BackgroundModel background = new Background(new double[]{pA, pC, pG, pT}); where {pA,... pT} are probabilities of respective nucleotides
    BackgroundModel background = new WordwiseBackground();
    // A actual pvalue will be less than requested. Not very important setting in this task
    BoundaryType pvalue_boundary = BoundaryType.LOWER;
    // Setting max_hash_size allows one to restrict memory exhausting during Pvalue calculation (using APE algorithm, not binary search)
    // Reasonable value is about 10 millions elements. But actually only specially constructed matrices can reach such high hash sizes.
    // So in standalone calculation we use null (not to check hash size) and in web version we use 1e7
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    return Background.fromString(str);
  }

  @Override
  protected void initialize_default_background() {
    queryBackground = new WordwiseBackground();
    collectionBackground = new WordwiseBackground();
  }
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    return "java ru.autosome.macroape.CollectDistanceMatrix";
  }

  @Override
  protected void initialize_default_background() {
    background = new WordwiseBackground();
  }
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    return "java ru.autosome.macroape.EvalSimilarity";
  }

  @Override
  protected void initialize_default_background() {
    firstBackground = new WordwiseBackground();
    secondBackground = new WordwiseBackground();
  }
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    return score(word.sequence, background);
  }

  @Override
  public double score(Sequence word) throws IllegalArgumentException {
    return score(word, new WordwiseBackground());
  }
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      Discretizer discretizer = new Discretizer(100.0);

//      CountingPWM firstPWMCounting = new CountingPWM(firstPWM.discrete(discretizer), new WordwiseBackground(), null);
//      CountingPWM secondPWMCounting = new CountingPWM(secondPWM.discrete(discretizer), new WordwiseBackground(), null);

      BackgroundModel background = new WordwiseBackground();
//      BackgroundModel background = new Background(new double[]{0.25,0.25,0.25,0.25});

      CompareModels comparator = new CompareModels(firstPWM, secondPWM, background, background, discretizer, null, null);
      CompareModelsCountsGiven.SimilarityInfo similarityInfo = comparator.jaccard_by_weak_pvalue(0.0005);
      System.out.println("\n----------\n" + similarityInfo.similarity());
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    return cache_calculator;
  }

  @Override
  protected void initialize_default_background() {
    background = new WordwiseBackground();
  }
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  protected String DOC_run_string() {
    return "java ru.autosome.ape.FindThreshold";
  }
  @Override
  protected void initialize_default_background() {
    background = new WordwiseBackground();
  }
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