@Test public void testKolmogorov() { logger.info("Kolmogorov"); double z = 2.0; double expResult = 0.99932907474422; double result = ContinuousDistributions.kolmogorov(z); assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH); }
public static double kolmogorov(double z) { if (z<0.27) { return 0.0; } else if (z>3.2) { return 1.1; } double ks=0; double y=-2*z*z; for(int i=27;i>=1;i=i-2) { ks=Math.exp(i*y)*(1-ks); } return 1.0-2.0*ks; }
ContinuousDistributions { public static double kolmogorov(double z) { if (z<0.27) { return 0.0; } else if (z>3.2) { return 1.1; } double ks=0; double y=-2*z*z; for(int i=27;i>=1;i=i-2) { ks=Math.exp(i*y)*(1-ks); } return 1.0-2.0*ks; } }
ContinuousDistributions { public static double kolmogorov(double z) { if (z<0.27) { return 0.0; } else if (z>3.2) { return 1.1; } double ks=0; double y=-2*z*z; for(int i=27;i>=1;i=i-2) { ks=Math.exp(i*y)*(1-ks); } return 1.0-2.0*ks; }  }
ContinuousDistributions { public static double kolmogorov(double z) { if (z<0.27) { return 0.0; } else if (z>3.2) { return 1.1; } double ks=0; double y=-2*z*z; for(int i=27;i>=1;i=i-2) { ks=Math.exp(i*y)*(1-ks); } return 1.0-2.0*ks; }  static double chisquareCdf(double x, int df); static double gaussCdf(double z); static double gamma(double x); static double logGamma(double Z); static double studentsCdf(double x, int df); static double exponentialCdf(double x, double lamda); static double betaCdf(double x, double a, double b); static double fCdf(double x, int f1, int f2); static double gammaCdf(double x, double a, double b); static double uniformCdf(double x, double a, double b); static double kolmogorov(double z); static double gaussInverseCdf(double p); static double chisquareInverseCdf(double p, int df); static double normalQuantile(double p, double mu, double sigma); static double dirichletPdf(double[] pi, double[] ai); static double dirichletPdf(double[] pi, double a); static double[] multinomialGaussianSample(double[] mean, double[][] covariance); static double multinomialGaussianPdf(double[] mean, double[][] covariance, double[] x); }
ContinuousDistributions { public static double kolmogorov(double z) { if (z<0.27) { return 0.0; } else if (z>3.2) { return 1.1; } double ks=0; double y=-2*z*z; for(int i=27;i>=1;i=i-2) { ks=Math.exp(i*y)*(1-ks); } return 1.0-2.0*ks; }  static double chisquareCdf(double x, int df); static double gaussCdf(double z); static double gamma(double x); static double logGamma(double Z); static double studentsCdf(double x, int df); static double exponentialCdf(double x, double lamda); static double betaCdf(double x, double a, double b); static double fCdf(double x, int f1, int f2); static double gammaCdf(double x, double a, double b); static double uniformCdf(double x, double a, double b); static double kolmogorov(double z); static double gaussInverseCdf(double p); static double chisquareInverseCdf(double p, int df); static double normalQuantile(double p, double mu, double sigma); static double dirichletPdf(double[] pi, double[] ai); static double dirichletPdf(double[] pi, double a); static double[] multinomialGaussianSample(double[] mean, double[][] covariance); static double multinomialGaussianPdf(double[] mean, double[][] covariance, double[] x);  }