List of inverseCumulativeProbability() Examples

Examples of inverseCumulativeProbability()

org.apache.commons.math.distribution.ChiSquaredDistribution.inverseCumulativeProbability()
org.apache.commons.math.distribution.ChiSquaredDistributionImpl.inverseCumulativeProbability()
For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
Returns 0 for p=0 and Double.POSITIVE_INFINITY for p=1.
@param p the desired probability @return x, such that P(X < x) = p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not a validprobability.
org.apache.commons.math.distribution.IntegerDistribution.inverseCumulativeProbability()
For this distribution, X, this method returns the largest x such that P(X ≤ x) <= p.
Note that this definition implies:
- If there is a minimum value, m, with positive probability under (the density of) X, then m - 1 is returned by inverseCumulativeProbability(0). If there is no such value m, Integer.MIN_VALUE is returned.
- If there is a maximum value, M, such that P(X ≤ M) =1, then M is returned by inverseCumulativeProbability(1). If there is no such value, M, Integer.MAX_VALUE is returned.
@param p the cumulative probability. @return the largest x such that P(X ≤ x) <= p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not between 0 and 1 (inclusive)
org.apache.commons.math.distribution.NormalDistributionImpl.inverseCumulativeProbability()
For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
Returns Double.NEGATIVE_INFINITY for p=0 and Double.POSITIVE_INFINITY for p=1.
@param p the desired probability @return x, such that P(X < x) = p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not a validprobability.
org.apache.commons.math.distribution.PoissonDistribution.inverseCumulativeProbability()
org.apache.commons.math.distribution.PoissonDistributionImpl.inverseCumulativeProbability()
org.apache.commons.math.distribution.TDistribution.inverseCumulativeProbability()
org.apache.commons.math.distribution.TDistributionImpl.inverseCumulativeProbability()
For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
Returns Double.NEGATIVE_INFINITY for p=0 and Double.POSITIVE_INFINITY for p=1.
@param p the desired probability @return x, such that P(X < x) = p @throws MathException if the inverse cumulative probability can not becomputed due to convergence or other numerical errors. @throws IllegalArgumentException if p is not a validprobability.
org.apache.commons.math.distribution.ZipfDistributionImpl.inverseCumulativeProbability()
org.apache.commons.math3.distribution.BinomialDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.ExponentialDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.FDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()
{@inheritDoc} @since 3.2
org.apache.commons.math3.distribution.RealDistribution.inverseCumulativeProbability()
Computes the quantile function of this distribution. For a random variable {@code X} distributed according to this distribution, thereturned value is
- inf{x in R | P(X<=x)>= p} for {@code 0 < p <= 1},
- inf{x in R | P(X<=x)> 0} for {@code p = 0}.
@param p the cumulative probability @return the smallest {@code p}-quantile of this distribution (largest 0-quantile for {@code p = 0}) @throws OutOfRangeException if {@code p < 0} or {@code p> 1}
org.apache.commons.math3.distribution.TDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p= = 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.
org.apache.commons.math3.distribution.WeibullDistribution.inverseCumulativeProbability()
{@inheritDoc}Returns {@code 0} when {@code p == 0} and{@code Double.POSITIVE_INFINITY} when {@code p == 1}.

Examples of org.apache.commons.math.distribution.ChiSquaredDistributionImpl.inverseCumulativeProbability()




    public static double criticalVal(int hashCount, double alpha) {
        ChiSquaredDistributionImpl d = new ChiSquaredDistributionImpl(hashCount - 1);
        try {
            return d.inverseCumulativeProbability(1 - alpha);
        } catch (MathException e) {
            return Double.MIN_VALUE;
        }
    }

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Examples of org.apache.commons.math.distribution.IntegerDistribution.inverseCumulativeProbability()

  private Vector generateRandomDoc(int numWords, double sparsity) throws MathException {
    Vector v = new DenseVector(numWords);
    IntegerDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.setQuick(i, dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }
  
  private LDAState generateRandomState(int numWords, int numTopics) {

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Examples of org.apache.commons.math.distribution.IntegerDistribution.inverseCumulativeProbability()

  private RandomAccessSparseVector generateRandomDoc(int numWords, double sparsity) throws MathException {
    RandomAccessSparseVector v = new RandomAccessSparseVector(numWords,(int)(numWords * sparsity));
    IntegerDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.set(i,dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }


  private LDAState generateRandomState(int numWords, int numTopics) {

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Examples of org.apache.commons.math.distribution.IntegerDistribution.inverseCumulativeProbability()

  private RandomAccessSparseVector generateRandomDoc(int numWords, double sparsity) throws MathException {
    RandomAccessSparseVector v = new RandomAccessSparseVector(numWords,(int)(numWords * sparsity));
    IntegerDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.set(i,dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }


  private LDAState generateRandomState(int numWords, int numTopics) {

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Examples of org.apache.commons.math.distribution.NormalDistributionImpl.inverseCumulativeProbability()

  }
  
  public static double normalInverseCDF(double pValue, double mean, double sigma) throws MathException
  {
    NormalDistributionImpl normDist = new NormalDistributionImpl(mean, sigma);
    double ret = normDist.inverseCumulativeProbability(pValue);
    return ret;
  }
  
  public static double normalPDF(double x, double mean, double sigma) throws MathException
  {

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Examples of org.apache.commons.math.distribution.PoissonDistribution.inverseCumulativeProbability()

  private Vector generateRandomDoc(int numWords, double sparsity) throws MathException {
    Vector v = new DenseVector(numWords);
    PoissonDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.setQuick(i, dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }
  
  private LDAState generateRandomState(int numWords, int numTopics) {

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Examples of org.apache.commons.math.distribution.PoissonDistribution.inverseCumulativeProbability()

  private RandomAccessSparseVector generateRandomDoc(int numWords, double sparsity) throws MathException {
    RandomAccessSparseVector v = new RandomAccessSparseVector(numWords,(int)(numWords * sparsity));
    PoissonDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.set(i,dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }


  private LDAState generateRandomState(int numWords, int numTopics) {

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Examples of org.apache.commons.math.distribution.PoissonDistribution.inverseCumulativeProbability()

  private SparseVector generateRandomDoc(int numWords, double sparsity) throws MathException {
    SparseVector v = new SparseVector(numWords,(int)(numWords * sparsity));
    PoissonDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.set(i,dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }


  private LDAState generateRandomState(int numWords, int numTopics) {

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Examples of org.apache.commons.math.distribution.PoissonDistribution.inverseCumulativeProbability()

  private Vector generateRandomDoc(int numWords, double sparsity) throws MathException {
    Vector v = new DenseVector(numWords);
    PoissonDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.setQuick(i, dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }


  private LDAState generateRandomState(int numWords, int numTopics) {

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Examples of org.apache.commons.math.distribution.PoissonDistributionImpl.inverseCumulativeProbability()

  private Vector generateRandomDoc(int numWords, double sparsity) throws MathException {
    Vector v = new DenseVector(numWords);
    PoissonDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.setQuick(i, dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }
  
  private LDAState generateRandomState(int numWords, int numTopics) {

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