Merge pull request #590 from TobiasGlaubach/fixing-auto-corr-bug598

Fixing Correlation.Auto Signatures (Issue #598)
8 years ago · 694fa080fa
6 changed files with 613 additions and 579 deletions
--- a/data/numpy/CorrNumpyData_pwm.csv
+++ b/data/numpy/CorrNumpyData_pwm.csv
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--- a/data/numpy/CorrNumpyData_rnd.csv
+++ b/data/numpy/CorrNumpyData_rnd.csv
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--- a/data/numpy/CorrNumpyData_sqr.csv
+++ b/data/numpy/CorrNumpyData_sqr.csv
@ -1,3 +1,4 @@
 1.000000000000000000, 1.000000000000000000
 1.000000000000000000, 0.925781250000000000
 1.000000000000000000, 0.851562500000000000
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@ -23,7 +24,7 @@
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--- a/src/Numerics.Tests/StatisticsTests/CorrelationTests.cs
+++ b/src/Numerics.Tests/StatisticsTests/CorrelationTests.cs
@ -78,12 +78,48 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
            var resNumpy = data.Select(tuple => Double.Parse(tuple.Item2, CultureInfo.InvariantCulture)).ToArray();
            var resMathNet = Correlation.Auto(series);
            Assert.AreEqual(resNumpy.Length, resMathNet.Length);
            for (int i = 0; i < resMathNet.Length; i++)
            {
                Assert.AreEqual(resNumpy[i], resMathNet[i], tol);
            }
        }
        [TestCase()]
        public void AutoCorrelationTest2()
        {
            var tol = 1e-14;
            var n = 10;
            // make some dummy data
            var a = Generate.LinearSpacedMap(n, 0, 2*Constants.Pi, Math.Sin);
            var idxs = new int[] { 2, 1, 7 };
            var resFull = Correlation.Auto(a);
            var resLim = Correlation.Auto(a, 4);
            Assert.AreEqual(4+1, resLim.Length);
            for (int i = 0; i < resLim.Length; i++)
            {
                Assert.AreEqual(resFull[i], resLim[i], tol);
            }
            var resRange = Correlation.Auto(a, 3, 6);   // -> Order will be set in function anyways
            Assert.AreEqual(6-3+1, resRange.Length);
            for (int i = 0; i < resRange.Length; i++)
            {
                Assert.AreEqual(resFull[i], resLim[i], tol);
            }
            var resIdxs = Correlation.Auto(a, idxs);
            Assert.AreEqual(idxs.Length, resIdxs.Length);
            Assert.AreEqual(resFull[2], resIdxs[0], tol);
            Assert.AreEqual(resFull[1], resIdxs[1], tol);
            Assert.AreEqual(resFull[7], resIdxs[2], tol);
        }
        /// <summary>
        /// Pearson correlation test.
        /// </summary>
--- a/src/Numerics/Statistics/Correlation.cs
+++ b/src/Numerics/Statistics/Correlation.cs
@ -44,7 +44,6 @@ namespace MathNet.Numerics.Statistics
    {
        /// <summary>
        /// Autocorrelation function (ACF) based on FFT for all possible lags k.
        /// The first element is hidden since ACF(k = 0) = 1.
        /// </summary>
        /// <param name="x">Data array to calculate auto correlation for.</param>
        /// <returns>An array with the ACF as a function of the lags k.</returns>
@ -55,11 +54,10 @@ namespace MathNet.Numerics.Statistics
        /// <summary>
        /// Autocorrelation function (ACF) based on FFT for lags between kMin and kMax.
        /// The first element is hidden since ACF(k = 0) = 1.
        /// </summary>
        /// <param name="x">The data array to calculate auto correlation for.</param>
-        /// <param name="kMax">Max lag to calculate ACF for must be positive and smaller than x.Length-1.</param>
+        /// <param name="kMax">Max lag to calculate ACF for must be positive and smaller than x.Length.</param>
-        /// <param name="kMin">Min lag to calculate ACF for (0 = no shift with acf=1) must be zero or positive and smaller than x.Length-1.</param>
+        /// <param name="kMin">Min lag to calculate ACF for (0 = no shift with acf=1) must be zero or positive and smaller than x.Length.</param>
        /// <returns>An array with the ACF as a function of the lags k.</returns>
        public static double[] Auto(IEnumerable<double> x, int kMax, int kMin = 0)
        {
@ -72,7 +70,6 @@ namespace MathNet.Numerics.Statistics
        /// <summary>
        /// Autocorrelation function based on FFT for lags k.
        /// The first element is hidden since ACF(k = 0) = 1.
        /// </summary>
        /// <param name="x">The data array to calculate auto correlation for.</param>
        /// <param name="k">Array with lags to calculate ACF for.</param>
@ -89,14 +86,16 @@ namespace MathNet.Numerics.Statistics
                throw new ArgumentException("k");
            }
            var k_min = k.Min();
            var k_max = k.Max();
            // get acf between full range
-            var acf = AutoCorrelationFft(x, k.Min(), k.Max());
+            var acf = AutoCorrelationFft(x, k_min, k_max);
            // map output by indexing
            var result = new double[k.Length];
            for (int i = 0; i < result.Length; i++)
            {
-                result[i] = acf[k[i]];
+                result[i] = acf[k[i] - k_min];
            }
            return result;
@ -106,8 +105,8 @@ namespace MathNet.Numerics.Statistics
        /// The internal core method for calculating the autocorrelation.
        /// </summary>
        /// <param name="x">The data array to calculate auto correlation for</param>
-        /// <param name="k_low">Min lag to calculate ACF for (0 = no shift with acf=1) must be zero or positive and smaller than x.Length-1</param>
+        /// <param name="k_low">Min lag to calculate ACF for (0 = no shift with acf=1) must be zero or positive and smaller than x.Length</param>
-        /// <param name="k_high">Max lag to calculate ACF for must be positive and smaller than x.Length-1</param>
+        /// <param name="k_high">Max lag (EXCLUSIVE) to calculate ACF for must be positive and smaller than x.Length</param>
        /// <returns>An array with the ACF as a function of the lags k.</returns>
        private static double[] AutoCorrelationFft(IEnumerable<double> x, int k_low, int k_high)
        {
@ -162,13 +161,12 @@ namespace MathNet.Numerics.Statistics
            double acf_Val1 = x_fft2[0].Real;
-            double[] acf_Vec = new double[k_high - k_low];
+            double[] acf_Vec = new double[k_high - k_low + 1];
            double[] acf_Val = new double[k_high + 1];
-            // normalize such that acf[0] would be 1.0 and drop the first element
+            // normalize such that acf[0] would be 1.0
-            for (int ii = 0; ii < (k_high - k_low); ii++)
+            for (int ii = 0; ii < (k_high - k_low + 1); ii++)
            {
-                acf_Vec[ii] = x_fft2[k_low + ii + 1].Real / acf_Val1;
+                acf_Vec[ii] = x_fft2[k_low + ii].Real / acf_Val1;
            }
            return acf_Vec;