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package org.apache.commons.math3.stat.inference;

import org.apache.commons.math3.distribution.BinomialDistribution;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.MathInternalError;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;

Implements binomial test statistics.

Exact test for the statistical significance of deviations from a
theoretically expected distribution of observations into two categories.
See Also:
Binomial test (Wikipedia)
Since:
3.3/**
 * Implements binomial test statistics.
 * <p>
 * Exact test for the statistical significance of deviations from a
 * theoretically expected distribution of observations into two categories.
 *
 * @see <a href="http://en.wikipedia.org/wiki/Binomial_test">Binomial test (Wikipedia)</a>
 * @since 3.3
 */
public class BinomialTest {

    
Returns whether the null hypothesis can be rejected with the given confidence level.

Preconditions:

Number of trials must be ≥ 0.
Number of successes must be ≥ 0.
Number of successes must be ≤ number of trials.
Probability must be ≥ 0 and ≤ 1.

Params:
numberOfTrials – number of trials performed
numberOfSuccesses – number of successes observed
probability – assumed probability of a single trial under the null hypothesis
alternativeHypothesis – type of hypothesis being evaluated (one- or two-sided)
alpha – significance level of the test
Throws:
NotPositiveException – if numberOfTrials or numberOfSuccesses is negative
OutOfRangeException – if probability is not between 0 and 1
MathIllegalArgumentException – if numberOfTrials < numberOfSuccesses or if alternateHypothesis is null.
See Also:
AlternativeHypothesis
Returns:
true if the null hypothesis can be rejected with confidence 1 - alpha/**
     * Returns whether the null hypothesis can be rejected with the given confidence level.
     * <p>
     * <strong>Preconditions</strong>:
     * <ul>
     * <li>Number of trials must be &ge; 0.</li>
     * <li>Number of successes must be &ge; 0.</li>
     * <li>Number of successes must be &le; number of trials.</li>
     * <li>Probability must be &ge; 0 and &le; 1.</li>
     * </ul>
     *
     * @param numberOfTrials number of trials performed
     * @param numberOfSuccesses number of successes observed
     * @param probability assumed probability of a single trial under the null hypothesis
     * @param alternativeHypothesis type of hypothesis being evaluated (one- or two-sided)
     * @param alpha significance level of the test
     * @return true if the null hypothesis can be rejected with confidence {@code 1 - alpha}
     * @throws NotPositiveException if {@code numberOfTrials} or {@code numberOfSuccesses} is negative
     * @throws OutOfRangeException if {@code probability} is not between 0 and 1
     * @throws MathIllegalArgumentException if {@code numberOfTrials} &lt; {@code numberOfSuccesses} or
     * if {@code alternateHypothesis} is null.
     * @see AlternativeHypothesis
     */
    public boolean binomialTest(int numberOfTrials, int numberOfSuccesses, double probability,
                                AlternativeHypothesis alternativeHypothesis, double alpha) {
        double pValue = binomialTest(numberOfTrials, numberOfSuccesses, probability, alternativeHypothesis);
        return pValue < alpha;
    }

    
Returns the observed significance level, or
p-value,
associated with a  Binomial test.
 The number returned is the smallest significance level at which one can reject the null hypothesis. The form of the hypothesis depends on alternativeHypothesis.
 The p-Value represents the likelihood of getting a result at least as extreme as the sample, given the provided probability of success on a single trial. For single-sided tests, this value can be directly derived from the Binomial distribution. For the two-sided test, the implementation works as follows: we start by looking at the most extreme cases (0 success and n success where n is the number of trials from the sample) and determine their likelihood. The lower value is added to the p-Value (if both values are equal, both are added). Then we continue with the next extreme value, until we added the value for the actual observed sample.

Preconditions:

Number of trials must be ≥ 0.
Number of successes must be ≥ 0.
Number of successes must be ≤ number of trials.
Probability must be ≥ 0 and ≤ 1.

Params:
numberOfTrials – number of trials performed
numberOfSuccesses – number of successes observed
probability – assumed probability of a single trial under the null hypothesis
alternativeHypothesis – type of hypothesis being evaluated (one- or two-sided)
Throws:
NotPositiveException – if numberOfTrials or numberOfSuccesses is negative
OutOfRangeException – if probability is not between 0 and 1
MathIllegalArgumentException – if numberOfTrials < numberOfSuccesses or if alternateHypothesis is null.
See Also:
AlternativeHypothesis
Returns:
p-value/**
     * Returns the <i>observed significance level</i>, or
     * <a href="http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">p-value</a>,
     * associated with a <a href="http://en.wikipedia.org/wiki/Binomial_test"> Binomial test</a>.
     * <p>
     * The number returned is the smallest significance level at which one can reject the null hypothesis.
     * The form of the hypothesis depends on {@code alternativeHypothesis}.</p>
     * <p>
     * The p-Value represents the likelihood of getting a result at least as extreme as the sample,
     * given the provided {@code probability} of success on a single trial. For single-sided tests,
     * this value can be directly derived from the Binomial distribution. For the two-sided test,
     * the implementation works as follows: we start by looking at the most extreme cases
     * (0 success and n success where n is the number of trials from the sample) and determine their likelihood.
     * The lower value is added to the p-Value (if both values are equal, both are added). Then we continue with
     * the next extreme value, until we added the value for the actual observed sample.</p>
     * <p>
     * <strong>Preconditions</strong>:
     * <ul>
     * <li>Number of trials must be &ge; 0.</li>
     * <li>Number of successes must be &ge; 0.</li>
     * <li>Number of successes must be &le; number of trials.</li>
     * <li>Probability must be &ge; 0 and &le; 1.</li>
     * </ul></p>
     *
     * @param numberOfTrials number of trials performed
     * @param numberOfSuccesses number of successes observed
     * @param probability assumed probability of a single trial under the null hypothesis
     * @param alternativeHypothesis type of hypothesis being evaluated (one- or two-sided)
     * @return p-value
     * @throws NotPositiveException if {@code numberOfTrials} or {@code numberOfSuccesses} is negative
     * @throws OutOfRangeException if {@code probability} is not between 0 and 1
     * @throws MathIllegalArgumentException if {@code numberOfTrials} &lt; {@code numberOfSuccesses} or
     * if {@code alternateHypothesis} is null.
     * @see AlternativeHypothesis
     */
    public double binomialTest(int numberOfTrials, int numberOfSuccesses, double probability,
                               AlternativeHypothesis alternativeHypothesis) {
        if (numberOfTrials < 0) {
            throw new NotPositiveException(numberOfTrials);
        }
        if (numberOfSuccesses < 0) {
            throw new NotPositiveException(numberOfSuccesses);
        }
        if (probability < 0 || probability > 1) {
            throw new OutOfRangeException(probability, 0, 1);
        }
        if (numberOfTrials < numberOfSuccesses) {
            throw new MathIllegalArgumentException(
                LocalizedFormats.BINOMIAL_INVALID_PARAMETERS_ORDER,
                numberOfTrials, numberOfSuccesses);
        }
        if (alternativeHypothesis == null) {
            throw new NullArgumentException();
        }

        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final BinomialDistribution distribution = new BinomialDistribution(null, numberOfTrials, probability);
        switch (alternativeHypothesis) {
        case GREATER_THAN:
            return 1 - distribution.cumulativeProbability(numberOfSuccesses - 1);
        case LESS_THAN:
            return distribution.cumulativeProbability(numberOfSuccesses);
        case TWO_SIDED:
            int criticalValueLow = 0;
            int criticalValueHigh = numberOfTrials;
            double pTotal = 0;

            while (true) {
                double pLow = distribution.probability(criticalValueLow);
                double pHigh = distribution.probability(criticalValueHigh);

                if (pLow == pHigh) {
                    pTotal += 2 * pLow;
                    criticalValueLow++;
                    criticalValueHigh--;
                } else if (pLow < pHigh) {
                    pTotal += pLow;
                    criticalValueLow++;
                } else {
                    pTotal += pHigh;
                    criticalValueHigh--;
                }

                if (criticalValueLow > numberOfSuccesses || criticalValueHigh < numberOfSuccesses) {
                    break;
                }
            }
            return pTotal;
        default:
            throw new MathInternalError(LocalizedFormats. OUT_OF_RANGE_SIMPLE, alternativeHypothesis,
                      AlternativeHypothesis.TWO_SIDED, AlternativeHypothesis.LESS_THAN);
        }
    }
}