@Test public void testNormalQuantile() { logger.info("normalQuantile"); double p = 0.32; double mu = 1.0; double sigma = 1.0; double expResult = 0.53230120088549; double result = ContinuousDistributions.normalQuantile(p, mu, sigma); assertEquals(expResult, result, Constants.DOUBLE_ACCURACY_HIGH); }
public static double normalQuantile(double p, double mu, double sigma) { if(sigma<0) { throw new IllegalArgumentException("The sigma parameter must be positive."); } else if(sigma==0) { return mu; } double r; double val; double q=p - 0.5; if (0.075 <= p && p <= 0.925) { r = 0.180625 - q * q; val = q * (((((((r * 2509.0809287301226727 + 33430.575583588128105) * r + 67265.770927008700853) * r + 45921.953931549871457) * r + 13731.693765509461125) * r + 1971.5909503065514427) * r + 133.14166789178437745) * r + 3.387132872796366608) / (((((((r * 5226.495278852854561 + 28729.085735721942674) * r + 39307.89580009271061) * r + 21213.794301586595867) * r + 5394.1960214247511077) * r + 687.1870074920579083) * r + 42.313330701600911252) * r + 1); } else { if (q > 0) { r = 1 - p; } else { r = p; } r = Math.sqrt(-Math.log(r)); if (r <= 5.0) { r += -1.6; val = (((((((r * 7.7454501427834140764e-4 + 0.0227238449892691845833) * r + 0.24178072517745061177) * r + 1.27045825245236838258) * r + 3.64784832476320460504) * r + 5.7694972214606914055) * r + 4.6303378461565452959) * r + 1.42343711074968357734) / (((((((r * 1.05075007164441684324e-9 + 5.475938084995344946e-4) * r + 0.0151986665636164571966) * r + 0.14810397642748007459) * r + 0.68976733498510000455) * r + 1.6763848301838038494) * r + 2.05319162663775882187) * r + 1.0); } else { r += -5.0; val = (((((((r * 2.01033439929228813265e-7 + 2.71155556874348757815e-5) * r + 0.0012426609473880784386) * r + 0.026532189526576123093) * r + 0.29656057182850489123) * r + 1.7848265399172913358) * r + 5.4637849111641143699) * r + 6.6579046435011037772) / (((((((r * 2.04426310338993978564e-15 + 1.4215117583164458887e-7)* r + 1.8463183175100546818e-5) * r + 7.868691311456132591e-4) * r + 0.0148753612908506148525) * r + 0.13692988092273580531) * r + 0.59983220655588793769) * r + 1.0); } if (q < 0.0) { val = -val; } } return mu + sigma * val; }
ContinuousDistributions { public static double normalQuantile(double p, double mu, double sigma) { if(sigma<0) { throw new IllegalArgumentException("The sigma parameter must be positive."); } else if(sigma==0) { return mu; } double r; double val; double q=p - 0.5; if (0.075 <= p && p <= 0.925) { r = 0.180625 - q * q; val = q * (((((((r * 2509.0809287301226727 + 33430.575583588128105) * r + 67265.770927008700853) * r + 45921.953931549871457) * r + 13731.693765509461125) * r + 1971.5909503065514427) * r + 133.14166789178437745) * r + 3.387132872796366608) / (((((((r * 5226.495278852854561 + 28729.085735721942674) * r + 39307.89580009271061) * r + 21213.794301586595867) * r + 5394.1960214247511077) * r + 687.1870074920579083) * r + 42.313330701600911252) * r + 1); } else { if (q > 0) { r = 1 - p; } else { r = p; } r = Math.sqrt(-Math.log(r)); if (r <= 5.0) { r += -1.6; val = (((((((r * 7.7454501427834140764e-4 + 0.0227238449892691845833) * r + 0.24178072517745061177) * r + 1.27045825245236838258) * r + 3.64784832476320460504) * r + 5.7694972214606914055) * r + 4.6303378461565452959) * r + 1.42343711074968357734) / (((((((r * 1.05075007164441684324e-9 + 5.475938084995344946e-4) * r + 0.0151986665636164571966) * r + 0.14810397642748007459) * r + 0.68976733498510000455) * r + 1.6763848301838038494) * r + 2.05319162663775882187) * r + 1.0); } else { r += -5.0; val = (((((((r * 2.01033439929228813265e-7 + 2.71155556874348757815e-5) * r + 0.0012426609473880784386) * r + 0.026532189526576123093) * r + 0.29656057182850489123) * r + 1.7848265399172913358) * r + 5.4637849111641143699) * r + 6.6579046435011037772) / (((((((r * 2.04426310338993978564e-15 + 1.4215117583164458887e-7)* r + 1.8463183175100546818e-5) * r + 7.868691311456132591e-4) * r + 0.0148753612908506148525) * r + 0.13692988092273580531) * r + 0.59983220655588793769) * r + 1.0); } if (q < 0.0) { val = -val; } } return mu + sigma * val; } }
ContinuousDistributions { public static double normalQuantile(double p, double mu, double sigma) { if(sigma<0) { throw new IllegalArgumentException("The sigma parameter must be positive."); } else if(sigma==0) { return mu; } double r; double val; double q=p - 0.5; if (0.075 <= p && p <= 0.925) { r = 0.180625 - q * q; val = q * (((((((r * 2509.0809287301226727 + 33430.575583588128105) * r + 67265.770927008700853) * r + 45921.953931549871457) * r + 13731.693765509461125) * r + 1971.5909503065514427) * r + 133.14166789178437745) * r + 3.387132872796366608) / (((((((r * 5226.495278852854561 + 28729.085735721942674) * r + 39307.89580009271061) * r + 21213.794301586595867) * r + 5394.1960214247511077) * r + 687.1870074920579083) * r + 42.313330701600911252) * r + 1); } else { if (q > 0) { r = 1 - p; } else { r = p; } r = Math.sqrt(-Math.log(r)); if (r <= 5.0) { r += -1.6; val = (((((((r * 7.7454501427834140764e-4 + 0.0227238449892691845833) * r + 0.24178072517745061177) * r + 1.27045825245236838258) * r + 3.64784832476320460504) * r + 5.7694972214606914055) * r + 4.6303378461565452959) * r + 1.42343711074968357734) / (((((((r * 1.05075007164441684324e-9 + 5.475938084995344946e-4) * r + 0.0151986665636164571966) * r + 0.14810397642748007459) * r + 0.68976733498510000455) * r + 1.6763848301838038494) * r + 2.05319162663775882187) * r + 1.0); } else { r += -5.0; val = (((((((r * 2.01033439929228813265e-7 + 2.71155556874348757815e-5) * r + 0.0012426609473880784386) * r + 0.026532189526576123093) * r + 0.29656057182850489123) * r + 1.7848265399172913358) * r + 5.4637849111641143699) * r + 6.6579046435011037772) / (((((((r * 2.04426310338993978564e-15 + 1.4215117583164458887e-7)* r + 1.8463183175100546818e-5) * r + 7.868691311456132591e-4) * r + 0.0148753612908506148525) * r + 0.13692988092273580531) * r + 0.59983220655588793769) * r + 1.0); } if (q < 0.0) { val = -val; } } return mu + sigma * val; }  }
ContinuousDistributions { public static double normalQuantile(double p, double mu, double sigma) { if(sigma<0) { throw new IllegalArgumentException("The sigma parameter must be positive."); } else if(sigma==0) { return mu; } double r; double val; double q=p - 0.5; if (0.075 <= p && p <= 0.925) { r = 0.180625 - q * q; val = q * (((((((r * 2509.0809287301226727 + 33430.575583588128105) * r + 67265.770927008700853) * r + 45921.953931549871457) * r + 13731.693765509461125) * r + 1971.5909503065514427) * r + 133.14166789178437745) * r + 3.387132872796366608) / (((((((r * 5226.495278852854561 + 28729.085735721942674) * r + 39307.89580009271061) * r + 21213.794301586595867) * r + 5394.1960214247511077) * r + 687.1870074920579083) * r + 42.313330701600911252) * r + 1); } else { if (q > 0) { r = 1 - p; } else { r = p; } r = Math.sqrt(-Math.log(r)); if (r <= 5.0) { r += -1.6; val = (((((((r * 7.7454501427834140764e-4 + 0.0227238449892691845833) * r + 0.24178072517745061177) * r + 1.27045825245236838258) * r + 3.64784832476320460504) * r + 5.7694972214606914055) * r + 4.6303378461565452959) * r + 1.42343711074968357734) / (((((((r * 1.05075007164441684324e-9 + 5.475938084995344946e-4) * r + 0.0151986665636164571966) * r + 0.14810397642748007459) * r + 0.68976733498510000455) * r + 1.6763848301838038494) * r + 2.05319162663775882187) * r + 1.0); } else { r += -5.0; val = (((((((r * 2.01033439929228813265e-7 + 2.71155556874348757815e-5) * r + 0.0012426609473880784386) * r + 0.026532189526576123093) * r + 0.29656057182850489123) * r + 1.7848265399172913358) * r + 5.4637849111641143699) * r + 6.6579046435011037772) / (((((((r * 2.04426310338993978564e-15 + 1.4215117583164458887e-7)* r + 1.8463183175100546818e-5) * r + 7.868691311456132591e-4) * r + 0.0148753612908506148525) * r + 0.13692988092273580531) * r + 0.59983220655588793769) * r + 1.0); } if (q < 0.0) { val = -val; } } return mu + sigma * val; }  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 normalQuantile(double p, double mu, double sigma) { if(sigma<0) { throw new IllegalArgumentException("The sigma parameter must be positive."); } else if(sigma==0) { return mu; } double r; double val; double q=p - 0.5; if (0.075 <= p && p <= 0.925) { r = 0.180625 - q * q; val = q * (((((((r * 2509.0809287301226727 + 33430.575583588128105) * r + 67265.770927008700853) * r + 45921.953931549871457) * r + 13731.693765509461125) * r + 1971.5909503065514427) * r + 133.14166789178437745) * r + 3.387132872796366608) / (((((((r * 5226.495278852854561 + 28729.085735721942674) * r + 39307.89580009271061) * r + 21213.794301586595867) * r + 5394.1960214247511077) * r + 687.1870074920579083) * r + 42.313330701600911252) * r + 1); } else { if (q > 0) { r = 1 - p; } else { r = p; } r = Math.sqrt(-Math.log(r)); if (r <= 5.0) { r += -1.6; val = (((((((r * 7.7454501427834140764e-4 + 0.0227238449892691845833) * r + 0.24178072517745061177) * r + 1.27045825245236838258) * r + 3.64784832476320460504) * r + 5.7694972214606914055) * r + 4.6303378461565452959) * r + 1.42343711074968357734) / (((((((r * 1.05075007164441684324e-9 + 5.475938084995344946e-4) * r + 0.0151986665636164571966) * r + 0.14810397642748007459) * r + 0.68976733498510000455) * r + 1.6763848301838038494) * r + 2.05319162663775882187) * r + 1.0); } else { r += -5.0; val = (((((((r * 2.01033439929228813265e-7 + 2.71155556874348757815e-5) * r + 0.0012426609473880784386) * r + 0.026532189526576123093) * r + 0.29656057182850489123) * r + 1.7848265399172913358) * r + 5.4637849111641143699) * r + 6.6579046435011037772) / (((((((r * 2.04426310338993978564e-15 + 1.4215117583164458887e-7)* r + 1.8463183175100546818e-5) * r + 7.868691311456132591e-4) * r + 0.0148753612908506148525) * r + 0.13692988092273580531) * r + 0.59983220655588793769) * r + 1.0); } if (q < 0.0) { val = -val; } } return mu + sigma * val; }  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);  }