/*
 Copyright (C) 2008 Richard Gomes

 This source code is release under the BSD License.

 This file is part of JQuantLib, a free-software/open-source library
 for financial quantitative analysts and developers - http://jquantlib.org/

 JQuantLib is free software: you can redistribute it and/or modify it
 under the terms of the JQuantLib license.  You should have received a
 copy of the license along with this program; if not, please email
 . The license is also available online at
 .

 This program is distributed in the hope that it will be useful, but WITHOUT
 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
 FOR A PARTICULAR PURPOSE.  See the license for more details.

 JQuantLib is based on QuantLib. http://quantlib.org/
 When applicable, the original copyright notice follows this notice.
 */

package org.jquantlib.testsuite.math.distributions;

import static org.junit.Assert.fail;

import org.jquantlib.QL;
import org.jquantlib.math.distributions.MoroInverseCumulativeNormal;
import org.junit.Test;

/**
 *
 * @author Dominik Holenstein
 *
 */


public class MoroInverseCumulativeNormalTest {

    public MoroInverseCumulativeNormalTest() {
        QL.info("::::: " + this.getClass().getSimpleName() + " :::::");
    }

    // test with values generated by quantlib's (0.8.1) moro algorithm implementation
    @Test
    public void testInverseCumulativNormal() {
        // normal values generated by quantlib - digits down to 1e-16
        final double[][] normal_testvalues = {
                { 0.01, -2.3263478739449690 },
                { 0.1,-1.2815515632770349 },
                { 0.2, -0.84162123489799423 },
                { 0.3, -0.52440051190665271 },
                { 0.4, -0.25334710332144356 },
                { 0.5, 0.00000000000000000 },
                { 0.6, 0.25334710332144356 },
                { 0.7, 0.52440051190665249 },
                { 0.8, 0.84162123489799467 },
                { 0.9, 1.2815515632770349 },
                { 0.99, 2.3263478739449690 } };

        final MoroInverseCumulativeNormal icn = new MoroInverseCumulativeNormal();

        // test the normal values
        for (final double[] normalTestvalue : normal_testvalues) {
            final double x_position = normalTestvalue[0];
            final double tolerance = 15.0e-3;//(Math.abs(x_position)<3.01) ? 1.0e-15: 1.0e-10;

            final double normal_expected = normalTestvalue[1];
            final double computed_normal = icn.op(x_position);
            if (Math.abs(normal_expected - computed_normal) > tolerance) {
                fail("x_position " + x_position + " normal_expected: "
                        + normal_expected + " normal_computed: "
                        + computed_normal);
            }
        }
    }

}