mathnet-numerics/src/UnitTests/StatisticsTests/CorrelationTests.cs

// <copyright file="CorrelationTests.cs" company="Math.NET">
// Math.NET Numerics, part of the Math.NET Project
// http://numerics.mathdotnet.com
// http://github.com/mathnet/mathnet-numerics
// http://mathnetnumerics.codeplex.com
//
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namespace MathNet.Numerics.UnitTests.StatisticsTests
{
#if !PORTABLE
    using System;
    using System.Collections.Generic;
    using System.Linq;
    using NUnit.Framework;
    using Statistics;

    /// <summary>
    /// Correlation tests
    /// </summary>
    /// <remarks>NOTE: this class is not included into Silverlight version, because it uses data from local files. 
    /// In Silverlight access to local files is forbidden, except several cases.</remarks>
    [TestFixture, Category("Statistics")]
    public class CorrelationTests
    {
        /// <summary>
        /// Statistics data.
        /// </summary>
        readonly IDictionary<string, StatTestData> _data = new Dictionary<string, StatTestData>();

        /// <summary>
        /// Initializes a new instance of the CorrelationTests class.
        /// </summary>
        public CorrelationTests()
        {
            var lottery = new StatTestData("./data/NIST/Lottery.dat");
            _data.Add("lottery", lottery);
            var lew = new StatTestData("./data/NIST/Lew.dat");
            _data.Add("lew", lew);
        }

        /// <summary>
        /// Pearson correlation test.
        /// </summary>
        [Test]
        public void PearsonCorrelationTest()
        {
            var dataA = _data["lottery"].Data.Take(200);
            var dataB = _data["lew"].Data.Take(200);

            var corr = Correlation.Pearson(dataA, dataB);
            AssertHelpers.AlmostEqual(-0.029470861580726, corr, 14);
        }

        /// <summary>
        /// Pearson correlation test.
        /// </summary>
        [Test]
        public void PearsonCorrelationConsistentWithCovariance()
        {
            var dataA = _data["lottery"].Data.Take(200).ToArray();
            var dataB = _data["lew"].Data.Take(200).ToArray();

            var direct = Correlation.Pearson(dataA, dataB);
            var covariance = dataA.Covariance(dataB)/(dataA.StandardDeviation()*dataB.StandardDeviation());

            AssertHelpers.AlmostEqual(covariance, direct, 14);
        }


        /// <summary>
        /// Constant-weighted Pearson correlation test.
        /// </summary>
        [Test]
        public void ConstantWeightedPearsonCorrelationTest()
        {
            var dataA = _data["lottery"].Data.Take(200);
            var dataB = _data["lew"].Data.Take(200);
            var weights = Generate.Repeat(200, 2.0);

            var corr = Correlation.Pearson(dataA, dataB);
            var corr2 = Correlation.WeightedPearson(dataA, dataB, weights);
            AssertHelpers.AlmostEqual(corr, corr2, 14);
        }

        /// <summary>
        /// Pearson correlation test fail.
        /// </summary>
        [Test]
        public void PearsonCorrelationTestFail()
        {
            var dataA = _data["lottery"].Data;
            var dataB = _data["lew"].Data;
            Assert.That(() => Correlation.Pearson(dataA, dataB), Throws.TypeOf<ArgumentOutOfRangeException>());
        }

        /// <summary>
        /// Spearman correlation test.
        /// </summary>
        [Test]
        public void SpearmanCorrelationTest()
        {
            var dataA = _data["lottery"].Data.Take(200);
            var dataB = _data["lew"].Data.Take(200);

            var corr = Correlation.Spearman(dataA, dataB);
            AssertHelpers.AlmostEqual(-0.0382856977898528, corr, 14);
        }

        /// <summary>
        /// Spearman correlation test fail.
        /// </summary>
        [Test]
        public void SpearmanCorrelationTestFail()
        {
            var dataA = _data["lottery"].Data;
            var dataB = _data["lew"].Data;
            Assert.That(() => Correlation.Spearman(dataA, dataB), Throws.TypeOf<ArgumentOutOfRangeException>());
        }
    }
#endif
}