List of usage examples for org.apache.commons.math3.distribution NormalDistribution NormalDistribution
public NormalDistribution(RandomGenerator rng, double mean, double sd, double inverseCumAccuracy) throws NotStrictlyPositiveException
From source file:com.anhth12.distributions.Distributions.java
public static RealDistribution normal(RandomGenerator rng, double std) { if (normalDistributions.get(rng, std) == null) { RealDistribution ret = new NormalDistribution(rng, 0, std, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); normalDistributions.put(rng, std, ret); return ret; }/*from w w w .j ava 2 s . c o m*/ return normalDistributions.get(rng, std); }
From source file:com.anhth12.nn.conf.NeuralNetworkConfiguration.java
public NeuralNetworkConfiguration(NeuralNetworkConfiguration conf) { this.layerFactory = conf.layerFactory; this.batchSize = conf.batchSize; this.sparsity = conf.sparsity; this.useAdaGrad = conf.useAdaGrad; this.lr = conf.lr; this.momentum = conf.momentum; this.l2 = conf.l2; this.numIterations = conf.numIterations; this.k = conf.k; this.corruptionLevel = conf.corruptionLevel; this.visibleUnit = conf.visibleUnit; this.hiddenUnit = conf.hiddenUnit; this.useRegularization = conf.useRegularization; this.momentumAfter = conf.momentumAfter; this.resetAdaGradIterations = conf.resetAdaGradIterations; this.dropOut = conf.dropOut; // this.applySparsity = conf.applySparsity; this.weightInit = conf.weightInit; this.optimizationAlgo = conf.optimizationAlgo; this.lossFunction = conf.lossFunction; // this.renderWeightsEveryNumEpochs = neuralNetConfiguration.renderWeightsEveryNumEpochs; this.concateBias = conf.concateBias; this.constrainGradientToUnitNorm = conf.constrainGradientToUnitNorm; this.rng = conf.rng; this.dist = conf.dist; // this.seed = conf.seed; this.nIn = conf.nIn; this.nOut = conf.nOut; this.activationFunction = conf.activationFunction; this.visibleUnit = conf.visibleUnit; // this.weightShape = neuralNetConfiguration.weightShape; // this.stride = neuralNetConfiguration.stride; // this.numFeatureMaps = neuralNetConfiguration.numFeatureMaps; // this.filterSize = neuralNetConfiguration.filterSize; // this.featureMapSize = neuralNetConfiguration.featureMapSize; if (dist == null) { this.dist = new NormalDistribution(rng, 0, .01, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); }//from w w w. j ava 2 s . c o m this.hiddenUnit = conf.hiddenUnit; }
From source file:org.apache.mahout.clustering.dirichlet.UncommonDistributions.java
/** * Return a random value from a normal distribution with the given mean and standard deviation * //from w ww . j ava2s . c om * @param mean * a double mean value * @param sd * a double standard deviation * @return a double sample */ public static double rNorm(double mean, double sd) { RealDistribution dist = new NormalDistribution(RANDOM.getRandomGenerator(), mean, sd, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); return dist.sample(); }
From source file:org.apache.mahout.math.random.NormalTest.java
@Test public void testSample() throws Exception { double[] data = new double[10001]; Sampler<Double> sampler = new Normal(); for (int i = 0; i < data.length; i++) { data[i] = sampler.sample();// ww w. j av a2s . c o m } Arrays.sort(data); NormalDistribution reference = new NormalDistribution(RandomUtils.getRandom().getRandomGenerator(), 0, 1, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); assertEquals("Median", reference.inverseCumulativeProbability(0.5), data[5000], 0.04); }
From source file:org.asoem.greyfish.utils.math.RandomGenerators.java
/** * Generates a random value for the normal distribution with the mean equal to {@code mu} and standard deviation * equal to {@code sigma}./*w w w.j a v a 2s.c o m*/ * * @param mu the mean of the distribution * @param sigma the standard deviation of the distribution * @return a random value for the given normal distribution */ public static double nextNormal(final RandomGenerator rng, final double mu, final double sigma) { final NormalDistribution normalDistribution = new NormalDistribution(rng, mu, sigma, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); while (true) { final double sample = normalDistribution.sample(); if (!Doubles.isFinite(sample)) { logger.warn("Discarding non finite sample from normal distribution (mu={}, sigma={}): {}", mu, sigma, sample); continue; } return sample; } }
From source file:org.briljantframework.data.dataframe.transform.TransformationTests.java
@Before public void setUp() throws Exception { NormalDistribution distribution = new NormalDistribution(new Well1024a(100), 10, 2, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); train = getBuilder().set("a", Vector.fromSupplier(distribution::sample, 100)) .set("b", Vector.fromSupplier(distribution::sample, 100)) .set("c", Vector.fromSupplier(distribution::sample, 100)).build(); test = getBuilder().set("a", Vector.fromSupplier(distribution::sample, 100)) .set("c", Vector.fromSupplier(distribution::sample, 100)) .set("b", Vector.fromSupplier(distribution::sample, 80)).build(); }
From source file:org.nmdp.ngs.tools.GenerateBed.java
/** * Main.//from w ww . j a v a 2 s .com * * @param args command line arguments */ public static void main(final String[] args) { Switch about = new Switch("a", "about", "display about message"); Switch help = new Switch("h", "help", "display help message"); FileArgument bedFile = new FileArgument("o", "bed-file", "output BED file, default stdout", false); IntegerArgument n = new IntegerArgument("n", "n", "number of BED records to generate, default " + DEFAULT_N, false); IntegerArgument size = new IntegerArgument("s", "size", "chromosome size, default " + DEFAULT_SIZE, false); StringArgument chrom = new StringArgument("c", "chrom", "chromosome name, default " + DEFAULT_CHROM, false); DoubleArgument meanLength = new DoubleArgument("l", "mean-length", "mean length, default " + DEFAULT_MEAN_LENGTH, false); DoubleArgument lengthVariation = new DoubleArgument("v", "length-variation", "length variation, default " + DEFAULT_LENGTH_VARIATION, false); IntegerArgument seed = new IntegerArgument("z", "seed", "random number seed, default relates to current time", false); ArgumentList arguments = new ArgumentList(about, help, bedFile, n, size, chrom, meanLength, lengthVariation, seed); CommandLine commandLine = new CommandLine(args); GenerateBed generateBed = null; try { CommandLineParser.parse(commandLine, arguments); if (about.wasFound()) { About.about(System.out); System.exit(0); } if (help.wasFound()) { Usage.usage(USAGE, null, commandLine, arguments, System.out); System.exit(0); } RandomGenerator random = seed.wasFound() ? new MersenneTwister(seed.getValue()) : new MersenneTwister(); double lv = Math.max(NO_VARIATION, lengthVariation.getValue(DEFAULT_LENGTH_VARIATION)); RealDistribution length = new NormalDistribution(random, meanLength.getValue(DEFAULT_MEAN_LENGTH), lv, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); generateBed = new GenerateBed(bedFile.getValue(), n.getValue(DEFAULT_N), size.getValue(DEFAULT_SIZE), chrom.getValue(DEFAULT_CHROM), random, length); } catch (CommandLineParseException e) { Usage.usage(USAGE, e, commandLine, arguments, System.err); System.exit(-1); } try { System.exit(generateBed.call()); } catch (Exception e) { e.printStackTrace(); System.exit(1); } }
From source file:org.nmdp.ngs.tools.GeneratePairedEndReads.java
/** * Main./*from w w w . ja v a2 s . c o m*/ * * @param args command line arguments */ public static void main(final String[] args) { Switch about = new Switch("a", "about", "display about message"); Switch help = new Switch("h", "help", "display help message"); FileArgument referenceFile = new FileArgument("r", "reference", "reference input file, in fasta format, default stdin", false); FileArgument firstReadFile = new FileArgument("1", "first-read", "first read output file, in fastq format", true); FileArgument secondReadFile = new FileArgument("2", "second-read", "second read output file, in fastq format", true); DoubleArgument meanLength = new DoubleArgument("l", "mean-length", "mean length, default " + DEFAULT_MEAN_LENGTH, false); DoubleArgument lengthVariation = new DoubleArgument("v", "length-variation", "length variation, default " + DEFAULT_LENGTH_VARIATION, false); DoubleArgument meanInsertSize = new DoubleArgument("j", "mean-insert-size", "mean insert size, default " + DEFAULT_MEAN_INSERT_SIZE, false); DoubleArgument insertSizeVariation = new DoubleArgument("k", "insert-size-variation", "insert size variation, default " + DEFAULT_INSERT_SIZE_VARIATION, false); IntegerArgument minimumCoverage = new IntegerArgument("c", "minimum-coverage", "minimum coverage, default " + DEFAULT_MINIMUM_COVERAGE, false); IntegerArgument meanCoverage = new IntegerArgument("g", "mean-coverage", "mean coverage", false); StringArgument qualityType = new StringArgument("u", "quality", "quality strategy type { illumina, normal }, default normal", false); DoubleArgument meanQualityWeight = new DoubleArgument("w", "mean-quality-weight", "mean quality weight, default " + DEFAULT_MEAN_QUALITY_WEIGHT, false); DoubleArgument qualityWeightVariation = new DoubleArgument("t", "quality-weight-variation", "quality weight variation, default " + DEFAULT_QUALITY_WEIGHT_VARIATION, false); DoubleArgument meanQuality = new DoubleArgument("q", "mean-quality", "mean quality, default " + DEFAULT_MEAN_QUALITY, false); DoubleArgument qualityVariation = new DoubleArgument("f", "quality-variation", "quality variation, default " + DEFAULT_QUALITY_VARIATION, false); StringArgument mutationType = new StringArgument("m", "mutation", "mutation strategy type { substitution, insertion, deletion, ambiguous, indel, composite }, default identity", false); DoubleArgument extendInsertionRate = new DoubleArgument("x", "extend-insertion-rate", "extend insertion rate, default " + DEFAULT_EXTEND_INSERTION_RATE, false); IntegerArgument maximumInsertionLength = new IntegerArgument("e", "maximum-insertion-length", "maximum insertion length, default " + DEFAULT_MAXIMUM_INSERTION_LENGTH, false); DoubleArgument insertionRate = new DoubleArgument("i", "insertion-rate", "insertion rate, default " + DEFAULT_INSERTION_RATE, false); DoubleArgument deletionRate = new DoubleArgument("d", "deletion-rate", "deletion rate, default " + DEFAULT_DELETION_RATE, false); DoubleArgument substitutionRate = new DoubleArgument("s", "substitution-rate", "substitution rate, default " + DEFAULT_SUBSTITUTION_RATE, false); DoubleArgument indelRate = new DoubleArgument("y", "indel-rate", "indel rate, default " + DEFAULT_INDEL_RATE, false); DoubleArgument ambiguousRate = new DoubleArgument("b", "ambiguous-rate", "ambiguous substitution rate, default " + DEFAULT_AMBIGUOUS_RATE, false); DoubleArgument mutationRate = new DoubleArgument("n", "mutation-rate", "mutation rate, default " + DEFAULT_MUTATION_RATE, false); IntegerArgument seed = new IntegerArgument("z", "seed", "random number seed, default relates to current time", false); ArgumentList arguments = new ArgumentList(about, help, referenceFile, firstReadFile, secondReadFile, meanLength, lengthVariation, meanInsertSize, insertSizeVariation, minimumCoverage, meanCoverage, qualityType, meanQualityWeight, qualityWeightVariation, meanQuality, qualityVariation, mutationType, extendInsertionRate, maximumInsertionLength, insertionRate, deletionRate, substitutionRate, indelRate, ambiguousRate, mutationRate, seed); CommandLine commandLine = new CommandLine(args); GeneratePairedEndReads generatePairedEndReads = null; try { CommandLineParser.parse(commandLine, arguments); if (about.wasFound()) { About.about(System.out); System.exit(0); } if (help.wasFound()) { Usage.usage(USAGE, null, commandLine, arguments, System.out); System.exit(0); } RandomGenerator random = seed.wasFound() ? new MersenneTwister(seed.getValue()) : new MersenneTwister(); double lv = Math.max(NO_VARIATION, lengthVariation.getValue(DEFAULT_LENGTH_VARIATION)); RealDistribution length = new NormalDistribution(random, meanLength.getValue(DEFAULT_MEAN_LENGTH), lv, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); double isv = Math.max(NO_VARIATION, insertSizeVariation.getValue(DEFAULT_INSERT_SIZE_VARIATION)); RealDistribution insertSize = new NormalDistribution(random, meanInsertSize.getValue(DEFAULT_MEAN_INSERT_SIZE), isv, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); CoverageStrategy coverage = DEFAULT_COVERAGE; if (minimumCoverage.wasFound()) { coverage = new MinimumCoverageStrategy(minimumCoverage.getValue()); } else if (meanCoverage.wasFound()) { coverage = new MeanCoverageStrategy(meanCoverage.getValue()); } QualityStrategy quality = null; if ("illumina".equals(qualityType.getValue())) { RealDistribution realDistribution = new NormalDistribution(random, meanQualityWeight.getValue(DEFAULT_MEAN_QUALITY_WEIGHT), qualityWeightVariation.getValue(DEFAULT_QUALITY_WEIGHT_VARIATION), NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); quality = new ScoreFunctionQualityStrategy(realDistribution, ScoreFunctions.illumina()); } else { RealDistribution realDistribution = new NormalDistribution(random, meanQuality.getValue(DEFAULT_MEAN_QUALITY), qualityVariation.getValue(DEFAULT_QUALITY_VARIATION), NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); quality = new RealDistributionQualityStrategy(realDistribution); } MutationStrategy mutation = DEFAULT_MUTATION; if (mutationType.wasFound()) { if ("substitution".equals(mutationType.getValue())) { mutation = new SubstitutionMutationStrategy(random); } else if ("ambiguous".equals(mutationType.getValue())) { mutation = new AmbiguousSubstitutionMutationStrategy(); } else if ("insertion".equals(mutationType.getValue())) { mutation = new InsertionMutationStrategy(random, extendInsertionRate.getValue(DEFAULT_EXTEND_INSERTION_RATE), maximumInsertionLength.getValue(DEFAULT_MAXIMUM_INSERTION_LENGTH)); } else if ("deletion".equals(mutationType.getValue())) { mutation = new DeletionMutationStrategy(); } else if ("indel".equals(mutationType.getValue())) { InsertionMutationStrategy insertion = new InsertionMutationStrategy(random, insertionRate.getValue(DEFAULT_INSERTION_RATE), maximumInsertionLength.getValue(DEFAULT_MAXIMUM_INSERTION_LENGTH)); DeletionMutationStrategy deletion = new DeletionMutationStrategy(); mutation = new IndelMutationStrategy(random, insertion, insertionRate.getValue(DEFAULT_INSERTION_RATE), deletion, deletionRate.getValue(DEFAULT_DELETION_RATE)); } else if ("composite".equals(mutationType.getValue())) { SubstitutionMutationStrategy substitution = new SubstitutionMutationStrategy(random); InsertionMutationStrategy insertion = new InsertionMutationStrategy(random, insertionRate.getValue(DEFAULT_INSERTION_RATE), maximumInsertionLength.getValue(DEFAULT_MAXIMUM_INSERTION_LENGTH)); DeletionMutationStrategy deletion = new DeletionMutationStrategy(); IndelMutationStrategy indel = new IndelMutationStrategy(random, insertion, insertionRate.getValue(DEFAULT_INSERTION_RATE), deletion, deletionRate.getValue(DEFAULT_DELETION_RATE)); AmbiguousSubstitutionMutationStrategy ambiguous = new AmbiguousSubstitutionMutationStrategy(); mutation = new CompositeMutationStrategy(random, substitution, substitutionRate.getValue(DEFAULT_SUBSTITUTION_RATE), indel, indelRate.getValue(DEFAULT_INDEL_RATE), ambiguous, ambiguousRate.getValue(DEFAULT_AMBIGUOUS_RATE)); } } generatePairedEndReads = new GeneratePairedEndReads(referenceFile.getValue(), firstReadFile.getValue(), secondReadFile.getValue(), random, length, insertSize, quality, coverage, mutationRate.getValue(DEFAULT_MUTATION_RATE), mutation); } catch (CommandLineParseException e) { if (about.wasFound()) { About.about(System.out); System.exit(0); } if (help.wasFound()) { Usage.usage(USAGE, null, commandLine, arguments, System.out); System.exit(0); } Usage.usage(USAGE, e, commandLine, arguments, System.err); System.exit(-1); } try { System.exit(generatePairedEndReads.call()); } catch (Exception e) { e.printStackTrace(); System.exit(1); } }
From source file:org.nmdp.ngs.tools.GenerateReads.java
/** * Main.//from w w w . j a v a 2 s. c om * * @param args command line arguments */ public static void main(final String[] args) { Switch about = new Switch("a", "about", "display about message"); Switch help = new Switch("h", "help", "display help message"); FileArgument referenceFile = new FileArgument("r", "reference", "reference input file, in fasta format, default stdin", false); FileArgument readFile = new FileArgument("o", "read", "read output file, in fastq format, default stdout", false); DoubleArgument meanLength = new DoubleArgument("l", "mean-length", "mean length, default " + DEFAULT_MEAN_LENGTH, false); DoubleArgument lengthVariation = new DoubleArgument("v", "length-variation", "length variation, default " + DEFAULT_LENGTH_VARIATION, false); IntegerArgument minimumCoverage = new IntegerArgument("c", "minimum-coverage", "minimum coverage, default " + DEFAULT_MINIMUM_COVERAGE, false); IntegerArgument meanCoverage = new IntegerArgument("g", "mean-coverage", "mean coverage", false); StringArgument qualityType = new StringArgument("u", "quality", "quality strategy type { illumina, normal }, default normal", false); DoubleArgument meanQualityWeight = new DoubleArgument("w", "mean-quality-weight", "mean quality weight, default " + DEFAULT_MEAN_QUALITY_WEIGHT, false); DoubleArgument qualityWeightVariation = new DoubleArgument("t", "quality-weight-variation", "quality weight variation, default " + DEFAULT_QUALITY_WEIGHT_VARIATION, false); DoubleArgument meanQuality = new DoubleArgument("q", "mean-quality", "mean quality, default " + DEFAULT_MEAN_QUALITY, false); DoubleArgument qualityVariation = new DoubleArgument("f", "quality-variation", "quality variation, default " + DEFAULT_QUALITY_VARIATION, false); StringArgument mutationType = new StringArgument("m", "mutation", "mutation strategy type { substitution, insertion, deletion, ambiguous, indel, composite }, default identity", false); DoubleArgument extendInsertionRate = new DoubleArgument("x", "extend-insertion-rate", "extend insertion rate, default " + DEFAULT_EXTEND_INSERTION_RATE, false); IntegerArgument maximumInsertionLength = new IntegerArgument("e", "maximum-insertion-length", "maximum insertion length, default " + DEFAULT_MAXIMUM_INSERTION_LENGTH, false); DoubleArgument insertionRate = new DoubleArgument("i", "insertion-rate", "insertion rate, default " + DEFAULT_INSERTION_RATE, false); DoubleArgument deletionRate = new DoubleArgument("d", "deletion-rate", "deletion rate, default " + DEFAULT_DELETION_RATE, false); DoubleArgument substitutionRate = new DoubleArgument("s", "substitution-rate", "substitution rate, default " + DEFAULT_SUBSTITUTION_RATE, false); DoubleArgument indelRate = new DoubleArgument("y", "indel-rate", "indel rate, default " + DEFAULT_INDEL_RATE, false); DoubleArgument ambiguousRate = new DoubleArgument("b", "ambiguous-rate", "ambiguous substitution rate, default " + DEFAULT_AMBIGUOUS_RATE, false); DoubleArgument mutationRate = new DoubleArgument("n", "mutation-rate", "mutation rate, default " + DEFAULT_MUTATION_RATE, false); IntegerArgument seed = new IntegerArgument("z", "seed", "random number seed, default relates to current time", false); ArgumentList arguments = new ArgumentList(about, help, referenceFile, readFile, meanLength, lengthVariation, minimumCoverage, meanCoverage, qualityType, meanQualityWeight, qualityWeightVariation, meanQuality, qualityVariation, mutationType, extendInsertionRate, maximumInsertionLength, insertionRate, deletionRate, substitutionRate, indelRate, ambiguousRate, mutationRate, seed); CommandLine commandLine = new CommandLine(args); GenerateReads generateReads = null; try { CommandLineParser.parse(commandLine, arguments); if (about.wasFound()) { About.about(System.out); System.exit(0); } if (help.wasFound()) { Usage.usage(USAGE, null, commandLine, arguments, System.out); System.exit(0); } RandomGenerator random = seed.wasFound() ? new MersenneTwister(seed.getValue()) : new MersenneTwister(); double lv = Math.max(NO_VARIATION, lengthVariation.getValue(DEFAULT_LENGTH_VARIATION)); RealDistribution length = new NormalDistribution(random, meanLength.getValue(DEFAULT_MEAN_LENGTH), lv, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); CoverageStrategy coverage = DEFAULT_COVERAGE; if (minimumCoverage.wasFound()) { coverage = new MinimumCoverageStrategy(minimumCoverage.getValue()); } else if (meanCoverage.wasFound()) { coverage = new MeanCoverageStrategy(meanCoverage.getValue()); } QualityStrategy quality = null; if ("illumina".equals(qualityType.getValue())) { RealDistribution realDistribution = new NormalDistribution(random, meanQualityWeight.getValue(DEFAULT_MEAN_QUALITY_WEIGHT), qualityWeightVariation.getValue(DEFAULT_QUALITY_WEIGHT_VARIATION), NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); quality = new ScoreFunctionQualityStrategy(realDistribution, ScoreFunctions.illumina()); } else { RealDistribution realDistribution = new NormalDistribution(random, meanQuality.getValue(DEFAULT_MEAN_QUALITY), qualityVariation.getValue(DEFAULT_QUALITY_VARIATION), NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); quality = new RealDistributionQualityStrategy(realDistribution); } MutationStrategy mutation = DEFAULT_MUTATION; if (mutationType.wasFound()) { if ("substitution".equals(mutationType.getValue())) { mutation = new SubstitutionMutationStrategy(random); } else if ("ambiguous".equals(mutationType.getValue())) { mutation = new AmbiguousSubstitutionMutationStrategy(); } else if ("insertion".equals(mutationType.getValue())) { mutation = new InsertionMutationStrategy(random, extendInsertionRate.getValue(DEFAULT_EXTEND_INSERTION_RATE), maximumInsertionLength.getValue(DEFAULT_MAXIMUM_INSERTION_LENGTH)); } else if ("deletion".equals(mutationType.getValue())) { mutation = new DeletionMutationStrategy(); } else if ("indel".equals(mutationType.getValue())) { InsertionMutationStrategy insertion = new InsertionMutationStrategy(random, insertionRate.getValue(DEFAULT_INSERTION_RATE), maximumInsertionLength.getValue(DEFAULT_MAXIMUM_INSERTION_LENGTH)); DeletionMutationStrategy deletion = new DeletionMutationStrategy(); mutation = new IndelMutationStrategy(random, insertion, insertionRate.getValue(DEFAULT_INSERTION_RATE), deletion, deletionRate.getValue(DEFAULT_DELETION_RATE)); } else if ("composite".equals(mutationType.getValue())) { SubstitutionMutationStrategy substitution = new SubstitutionMutationStrategy(random); InsertionMutationStrategy insertion = new InsertionMutationStrategy(random, insertionRate.getValue(DEFAULT_INSERTION_RATE), maximumInsertionLength.getValue(DEFAULT_MAXIMUM_INSERTION_LENGTH)); DeletionMutationStrategy deletion = new DeletionMutationStrategy(); IndelMutationStrategy indel = new IndelMutationStrategy(random, insertion, insertionRate.getValue(DEFAULT_INSERTION_RATE), deletion, deletionRate.getValue(DEFAULT_DELETION_RATE)); AmbiguousSubstitutionMutationStrategy ambiguous = new AmbiguousSubstitutionMutationStrategy(); mutation = new CompositeMutationStrategy(random, substitution, substitutionRate.getValue(DEFAULT_SUBSTITUTION_RATE), indel, indelRate.getValue(DEFAULT_INDEL_RATE), ambiguous, ambiguousRate.getValue(DEFAULT_AMBIGUOUS_RATE)); } } generateReads = new GenerateReads(referenceFile.getValue(), readFile.getValue(), random, length, quality, coverage, mutationRate.getValue(DEFAULT_MUTATION_RATE), mutation); } catch (CommandLineParseException e) { if (about.wasFound()) { About.about(System.out); System.exit(0); } if (help.wasFound()) { Usage.usage(USAGE, null, commandLine, arguments, System.out); System.exit(0); } Usage.usage(USAGE, e, commandLine, arguments, System.err); System.exit(-1); } try { System.exit(generateReads.call()); } catch (Exception e) { e.printStackTrace(); System.exit(1); } }
From source file:tools.descartes.dlim.generator.util.FunctionValueCalculator.java
/** * Create a new FunctionValueCalculator. * * @param rndGenerator// w w w . j a v a 2 s . c o m * The random number generator for Noises. * @param noiseMode * Set this to IGeneratorConstants.CALIBRATION if Noises are to * return 0. */ public FunctionValueCalculator(JDKRandomGenerator rndGenerator, String noiseMode) { this.rndGenerator = rndGenerator; nDistribution = new NormalDistribution(rndGenerator, 0, 1, NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY); this.noiseMode = noiseMode; }