Java Beta.regularizedBeta方法代码示例

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
public static double cdf(double x, double mean, double alpha) {
        double theta = 1.0 / alpha;
        double p = theta / (theta + mean);
        try {
            return Beta.regularizedBeta(p, theta, x+1);
        } catch (MathException e) {
            // AR - throwing exceptions deep in numerical code causes trouble. Catching runtime
            // exceptions is bad. Better to return NaN and let the calling code deal with it.
            return Double.NaN;
//                throw MathRuntimeException.createIllegalArgumentException(
//                "Couldn't calculate beta cdf for alpha = " + alpha + ", beta = " + beta + ": " +e.getMessage());
        }
    }
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * For this disbution, X, this method returns P(X &lt; <code>x</code>).
 * @param x the value at which the CDF is evaluated.
 * @return CDF evaluted at <code>x</code>. 
 * @throws MathException if the cumulative probability can not be
 *            computed due to convergence or other numerical errors.
 */
public double cumulativeProbability(double x) throws MathException{
    double ret;
    if (x == 0.0) {
        ret = 0.5;
    } else {
        double t =
            Beta.regularizedBeta(
                getDegreesOfFreedom() / (getDegreesOfFreedom() + (x * x)),
                0.5 * getDegreesOfFreedom(),
                0.5);
        if (x < 0.0) {
            ret = 0.5 * t;
        } else {
            ret = 1.0 - 0.5 * t;
        }
    }

    return ret;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * For this distribution, X, this method returns P(X ≤ x).
 * @param x the value at which the PDF is evaluated.
 * @return PDF for this distribution. 
 * @throws MathException if the cumulative probability can not be
 *            computed due to convergence or other numerical errors.
 */
public double cumulativeProbability(int x) throws MathException {
    double ret;
    if (x < 0) {
        ret = 0.0;
    } else if (x >= getNumberOfTrials()) {
        ret = 1.0;
    } else {
        ret =
            1.0 - Beta.regularizedBeta(
                    getProbabilityOfSuccess(),
                    x + 1.0,
                    getNumberOfTrials() - x);
    }
    return ret;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
public double cdf(double x)  {
        if (x <= 0) {
            return 0;
        } else if (x >= 1) {
            return 1;
        } else {
            try {
                return Beta.regularizedBeta(x, alpha, beta);
            } catch (MathException e) {
                // AR - throwing exceptions deep in numerical code causes trouble. Catching runtime
                // exceptions is bad. Better to return NaN and let the calling code deal with it.
                return Double.NaN;
//                throw MathRuntimeException.createIllegalArgumentException(
//                "Couldn't calculate beta cdf for alpha = " + alpha + ", beta = " + beta + ": " +e.getMessage());
            }
        }

    }
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
@Override
public Double getDescendingCumulativeProbabilityAt(double x) throws MathException {

    int k = (int) x;
    if (k > n) {
        return 0.0;
    } else if (k < 0) {
        return 1.0;
    }
    Double result = pCache.get(k);
    if (result == null) {
        // adapted from http://commons.apache.org/proper/commons-math/apidocs/src-html/org/apache/commons/math3/distribution/BinomialDistribution.html#line.130
        result = Beta.regularizedBeta(p, x + 1.0, n - x);
        addDescendingCumulativePToCache(k, result);
    }

    return result;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * For this distribution, X, this method returns P(X &lt; <code>x</code>).
 * @param x the value at which the CDF is evaluated.
 * @return CDF evaluted at <code>x</code>. 
 * @throws MathException if the cumulative probability can not be
 *            computed due to convergence or other numerical errors.
 */
public double cumulativeProbability(double x) throws MathException{
    double ret;
    if (x == 0.0) {
        ret = 0.5;
    } else {
        double t =
            Beta.regularizedBeta(
                getDegreesOfFreedom() / (getDegreesOfFreedom() + (x * x)),
                0.5 * getDegreesOfFreedom(),
                0.5);
        if (x < 0.0) {
            ret = 0.5 * t;
        } else {
            ret = 1.0 - 0.5 * t;
        }
    }

    return ret;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * For this distribution, X, this method returns P(X &lt; <code>x</code>).
 * @param x the value at which the CDF is evaluated.
 * @return CDF evaluted at <code>x</code>.
 * @throws MathException if the cumulative probability can not be
 *            computed due to convergence or other numerical errors.
 */
public double cumulativeProbability(double x) throws MathException{
    double ret;
    if (x == 0.0) {
        ret = 0.5;
    } else {
        double t =
            Beta.regularizedBeta(
                degreesOfFreedom / (degreesOfFreedom + (x * x)),
                0.5 * degreesOfFreedom,
                0.5);
        if (x < 0.0) {
            ret = 0.5 * t;
        } else {
            ret = 1.0 - 0.5 * t;
        }
    }

    return ret;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * For this distribution, X, this method returns P(X ≤ x).
 * @param x the value at which the PDF is evaluated.
 * @return PDF for this distribution. 
 * @throws MathException if the cumulative probability can not be
 *            computed due to convergence or other numerical errors.
 */
@Override
public double cumulativeProbability(int x) throws MathException {
    double ret;
    if (x < 0) {
        ret = 0.0;
    } else if (x >= getNumberOfTrials()) {
        ret = 1.0;
    } else {
        ret =
            1.0 - Beta.regularizedBeta(
                    getProbabilityOfSuccess(),
                    x + 1.0,
                    getNumberOfTrials() - x);
    }
    return ret;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * For this distribution, X, this method returns {@code P(X < x}).
 *
 * @param x Value at which the CDF is evaluated.
 * @return CDF evaluated at {@code x}.
 * @throws MathException if the cumulative probability can not be
 * computed due to convergence or other numerical errors.
 */
public double cumulativeProbability(double x) throws MathException{
    double ret;
    if (x == 0) {
        ret = 0.5;
    } else {
        double t =
            Beta.regularizedBeta(
                degreesOfFreedom / (degreesOfFreedom + (x * x)),
                0.5 * degreesOfFreedom,
                0.5);
        if (x < 0.0) {
            ret = 0.5 * t;
        } else {
            ret = 1.0 - 0.5 * t;
        }
    }

    return ret;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * For this distribution, X, this method returns P(X &lt; <code>x</code>).
 * @param x the value at which the CDF is evaluated.
 * @return CDF evaluted at <code>x</code>.
 * @throws MathException if the cumulative probability can not be
 *            computed due to convergence or other numerical errors.
 */
public double cumulativeProbability(double x) throws MathException{
    double ret;
    if (x == 0.0) {
        ret = 0.5;
    } else {
        double t =
            Beta.regularizedBeta(
                getDegreesOfFreedom() / (getDegreesOfFreedom() + (x * x)),
                0.5 * getDegreesOfFreedom(),
                0.5);
        if (x < 0.0) {
            ret = 0.5 * t;
        } else {
            ret = 1.0 - 0.5 * t;
        }
    }

    return ret;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * For this distribution, X, this method returns P(X &lt; <code>x</code>).
 *
 * @param x the value at which the CDF is evaluated.
 * @return CDF evaluted at <code>x</code>.
 * @throws MathException if the cumulative probability can not be
 *                       computed due to convergence or other numerical errors.
 */
public double cumulativeProbability(double x) throws MathException {
    double ret;
    if (x == 0.0) {
        ret = 0.5;
    } else {
        double t =
                Beta.regularizedBeta(
                        degreesOfFreedom / (degreesOfFreedom + (x * x)),
                        0.5 * degreesOfFreedom,
                        0.5);
        if (x < 0.0) {
            ret = 0.5 * t;
        } else {
            ret = 1.0 - 0.5 * t;
        }
    }

    return ret;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * @param x x
 * @return the value of the function
 * @throws IllegalArgumentException If $x < 0$ or $x > 1$
 */
@Override
public Double evaluate(final Double x) {
  Validate.isTrue(x >= 0 && x <= 1, "x must be in the range 0 to 1");
  try {
    return Beta.regularizedBeta(x, _a, _b, _eps, _maxIter);
  } catch (final org.apache.commons.math.MathException e) {
    throw new MathException(e);
  }
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
/**
 * For this distribution, X, this method returns P(X ≤ x).
 * @param x the value at which the PDF is evaluated
 * @return PDF for this distribution
 * @throws MathException if the cumulative probability can not be computed
 *         due to convergence or other numerical errors
 */
public double cumulativeProbability(int x) throws MathException {
    double ret;
    if (x < 0) {
        ret = 0.0;
    } else {
        ret = Beta.regularizedBeta(getProbabilityOfSuccess(),
            getNumberOfSuccesses(), x + 1);
    }
    return ret;
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
public double cumulativeProbability(double x) throws MathException {
    if (x <= 0) {
        return 0;
    } else if (x >= 1) {
        return 1;
    } else {
        return Beta.regularizedBeta(x, alpha, beta);
    }
}
 

import org.apache.commons.math.special.Beta; //导入方法依赖的package包/类
public double quantile(double y) {
	// TB - I'm having trouble implementing this
	// LM - A first stab using simple minimisation to invert the function (under absolute loss)
	// Implementation based on the qnbinom.c function used in R
	 final double stdev = Math.sqrt(mean + (mean * mean * alpha));
	 final double r = -1 * (mean*mean) / (mean - stdev*stdev);
     final double p = mean / (stdev*stdev);
     final double prob = y;
     
     final double Q = 1.0 / p;
     final double P = (1.0 - p) * Q;
     final double gamma = (Q + P)/stdev;
     final double z = Math.sqrt(2.0) * ErrorFunction.inverseErf(2.0 * y - 1.0);
     final double crudeY = mean + stdev * (z + gamma * (z*z - 1) / 6);
     
	UnivariateFunction f = new UnivariateFunction() {
		double tent = Double.NaN;
		public double evaluate(final double argument) {
			try {
	            tent = Beta.regularizedBeta(p, r, argument+1);
	        } catch (MathException e) {
	            return Double.NaN;
	        }
			double score = Math.abs(tent-prob);
			return score;
		}
		public int getNumArguments() {
			return 1;
		}
		public double getLowerBound() { // 20% window should cut it. Probably too large even...
			return Math.min(crudeY - .2*crudeY, 0);
		}
		public double getUpperBound() {
			return crudeY + .2*crudeY;
		}
	};
	UnivariateMinimum minimum = new UnivariateMinimum();
	double q = minimum.findMinimum(f);
 	return Math.ceil(q);
}