Cosmetics

LICENSE.md

@@ -1,7 +1,7 @@
 Math.NET Numerics License (MIT/X11)
 ===================================
 
-Copyright (c) 2002-2013 Math.NET
+Copyright (c) 2002-2014 Math.NET
 
 Permission is hereby granted, free of charge, to any person
 obtaining a copy of this software and associated documentation

MathNet.Numerics.sln.DotSettings

@@ -1,4 +1,5 @@
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+	<s:Boolean x:Key="/Default/CodeInspection/ExcludedFiles/FileMasksToSkip/=_002A_002Ecshtml/@EntryIndexedValue">True</s:Boolean>
 	<s:String x:Key="/Default/CodeStyle/CodeCleanup/Profiles/=Full_0020Cleanup_0020_0028Math_002ENET_0029/@EntryIndexedValue">&lt;?xml version="1.0" encoding="utf-16"?&gt;&lt;Profile name="Full Cleanup (Math.NET)"&gt;&lt;CSArrangeThisQualifier&gt;True&lt;/CSArrangeThisQualifier&gt;&lt;CSRemoveCodeRedundancies&gt;True&lt;/CSRemoveCodeRedundancies&gt;&lt;CSUseAutoProperty&gt;True&lt;/CSUseAutoProperty&gt;&lt;CSMakeFieldReadonly&gt;True&lt;/CSMakeFieldReadonly&gt;&lt;CSUpdateFileHeader&gt;True&lt;/CSUpdateFileHeader&gt;&lt;CSOptimizeUsings&gt;&lt;OptimizeUsings&gt;True&lt;/OptimizeUsings&gt;&lt;EmbraceInRegion&gt;False&lt;/EmbraceInRegion&gt;&lt;RegionName&gt;&lt;/RegionName&gt;&lt;/CSOptimizeUsings&gt;&lt;CSShortenReferences&gt;True&lt;/CSShortenReferences&gt;&lt;CSReformatCode&gt;True&lt;/CSReformatCode&gt;&lt;XMLReformatCode&gt;True&lt;/XMLReformatCode&gt;&lt;CssAlphabetizeProperties&gt;True&lt;/CssAlphabetizeProperties&gt;&lt;CssReformatCode&gt;True&lt;/CssReformatCode&gt;&lt;JsReformatCode&gt;True&lt;/JsReformatCode&gt;&lt;JsInsertSemicolon&gt;True&lt;/JsInsertSemicolon&gt;&lt;VBFormatDocComments&gt;True&lt;/VBFormatDocComments&gt;&lt;VBReformatCode&gt;True&lt;/VBReformatCode&gt;&lt;VBShortenReferences&gt;True&lt;/VBShortenReferences&gt;&lt;VBOptimizeImports&gt;True&lt;/VBOptimizeImports&gt;&lt;HtmlReformatCode&gt;True&lt;/HtmlReformatCode&gt;&lt;AspOptimizeRegisterDirectives&gt;True&lt;/AspOptimizeRegisterDirectives&gt;&lt;CSReorderTypeMembers&gt;True&lt;/CSReorderTypeMembers&gt;&lt;CSUseVar&gt;&lt;BehavourStyle&gt;CAN_CHANGE_BOTH&lt;/BehavourStyle&gt;&lt;LocalVariableStyle&gt;IMPLICIT_WHEN_INITIALIZER_HAS_TYPE&lt;/LocalVariableStyle&gt;&lt;ForeachVariableStyle&gt;ALWAYS_EXPLICIT&lt;/ForeachVariableStyle&gt;&lt;/CSUseVar&gt;&lt;/Profile&gt;</s:String>
 	<s:String x:Key="/Default/CodeStyle/CodeCleanup/RecentlyUsedProfile/@EntryValue">Full Cleanup (Math.NET)</s:String>
 	<s:Boolean x:Key="/Default/CodeStyle/CodeFormatting/CSharpFormat/ALIGN_LINQ_QUERY/@EntryValue">False</s:Boolean>
@@ -48,11 +49,14 @@ WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING&#xD;
 FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR&#xD;
 OTHER DEALINGS IN THE SOFTWARE.&#xD;
 &lt;/copyright&gt;</s:String>
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=AVX/@EntryIndexedValue">AVX</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=CDF/@EntryIndexedValue">CDF</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=DFT/@EntryIndexedValue">DFT</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=FFT/@EntryIndexedValue">FFT</s:String>
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=IA/@EntryIndexedValue">IA</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=ILU/@EntryIndexedValue">ILU</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=ILUTP/@EntryIndexedValue">ILUTP</s:String>
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=IX/@EntryIndexedValue">IX</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=LU/@EntryIndexedValue">LU</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=MAE/@EntryIndexedValue">MAE</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=MC/@EntryIndexedValue">MC</s:String>
@@ -66,6 +70,7 @@ OTHER DEALINGS IN THE SOFTWARE.&#xD;
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=SAS/@EntryIndexedValue">SAS</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=SPSS/@EntryIndexedValue">SPSS</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=SSD/@EntryIndexedValue">SSD</s:String>
+	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=SSE/@EntryIndexedValue">SSE</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=SVD/@EntryIndexedValue">SVD</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=TFQMR/@EntryIndexedValue">TFQMR</s:String>
 	<s:String x:Key="/Default/CodeStyle/Naming/CSharpNaming/Abbreviations/=WH/@EntryIndexedValue">WH</s:String>

src/Numerics/Distributions/Triangular.cs

@@ -138,7 +138,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         public double Mean
         {
-            get { return (_lower + _upper + _mode) / 3.0; }
+            get { return (_lower + _upper + _mode)/3.0; }
         }
 
         /// <summary>
@@ -151,7 +151,7 @@ namespace MathNet.Numerics.Distributions
                 var a = _lower;
                 var b = _upper;
                 var c = _mode;
-                return (a * a + b * b + c * c - a * b - a * c - b * c) / 18.0;
+                return (a*a + b*b + c*c - a*b - a*c - b*c)/18.0;
             }
         }
 
@@ -169,7 +169,7 @@ namespace MathNet.Numerics.Distributions
         /// <value></value>
         public double Entropy
         {
-            get { return 0.5 + Math.Log((_upper - _lower) / 2); }
+            get { return 0.5 + Math.Log((_upper - _lower)/2); }
         }
 
         /// <summary>
@@ -182,9 +182,9 @@ namespace MathNet.Numerics.Distributions
                 var a = _lower;
                 var b = _upper;
                 var c = _mode;
-                var q = Math.Sqrt(2) * (a + b - 2 * c) * (2 * a - b - c) * (a - 2 * b + c);
+                var q = Math.Sqrt(2)*(a + b - 2*c)*(2*a - b - c)*(a - 2*b + c);
-                var d = 5 * Math.Pow(a * a + b * b + c * c - a * b - a * c - b * c, 3.0 / 2);
+                var d = 5*Math.Pow(a*a + b*b + c*c - a*b - a*c - b*c, 3.0/2);
-                return q / d;
+                return q/d;
             }
         }
 
@@ -208,9 +208,9 @@ namespace MathNet.Numerics.Distributions
                 var a = _lower;
                 var b = _upper;
                 var c = _mode;
-                return c >= (a + b) / 2
+                return c >= (a + b)/2
-                    ? a + Math.Sqrt((b - a) * (c - a) / 2)
+                    ? a + Math.Sqrt((b - a)*(c - a)/2)
-                    : b - Math.Sqrt((b - a) * (b - c) / 2);
+                    : b - Math.Sqrt((b - a)*(b - c)/2);
             }
         }
 
@@ -311,8 +311,8 @@ namespace MathNet.Numerics.Distributions
             var b = upper;
             var c = mode;
 
-            if (a <= x && x <= c) return 2 * (x - a) / ((b - a) * (c - a));
+            if (a <= x && x <= c) return 2*(x - a)/((b - a)*(c - a));
-            if (c < x & x <= b) return 2 * (b - x) / ((b - a) * (b - c));
+            if (c < x & x <= b) return 2*(b - x)/((b - a)*(b - c));
             return 0;
         }
 
@@ -342,15 +342,15 @@ namespace MathNet.Numerics.Distributions
         public static double CDF(double lower, double upper, double mode, double x)
         {
             if (upper < mode) throw new ArgumentOutOfRangeException("upper", Resources.InvalidDistributionParameters);
-            if (mode < lower) throw new ArgumentOutOfRangeException("lower", Resources.InvalidDistributionParameters); // TODO: Is "lower" the appropriate argument here?
+            if (lower > mode) throw new ArgumentOutOfRangeException("lower", Resources.InvalidDistributionParameters);
 
             var a = lower;
             var b = upper;
             var c = mode;
 
             if (x < a) return 0;
-            if (a <= x && x <= c) return (x - a) * (x - a) / ((b - a) * (c - a));
+            if (a <= x && x <= c) return (x - a)*(x - a)/((b - a)*(c - a));
-            if (c < x & x <= b) return 1 - (b - x) * (b - x) / ((b - a) * (b - c));
+            if (c < x & x <= b) return 1 - (b - x)*(b - x)/((b - a)*(b - c));
             return 1;
         }
 
@@ -367,7 +367,7 @@ namespace MathNet.Numerics.Distributions
         public static double InvCDF(double lower, double upper, double mode, double p)
         {
             if (upper < mode) throw new ArgumentOutOfRangeException("upper", Resources.InvalidDistributionParameters);
-            if (mode < lower) throw new ArgumentOutOfRangeException("lower", Resources.InvalidDistributionParameters); // TODO: Is "lower" the appropriate argument here?
+            if (lower > mode) throw new ArgumentOutOfRangeException("lower", Resources.InvalidDistributionParameters);
 
             var a = lower;
             var b = upper;
@@ -375,8 +375,8 @@ namespace MathNet.Numerics.Distributions
 
             if (p <= 0) return lower;
             // Taken from http://www.ntrand.com/triangular-distribution/
-            if (p < (c - a) / (b - a)) return a + Math.Sqrt(p * (c - a) * (b - a));
+            if (p < (c - a)/(b - a)) return a + Math.Sqrt(p*(c - a)*(b - a));
-            if (p < 1) return b - Math.Sqrt((1 - p) * (b - c) * (b - a));
+            if (p < 1) return b - Math.Sqrt((1 - p)*(b - c)*(b - a));
             return upper;
         }
 
@@ -395,9 +395,9 @@ namespace MathNet.Numerics.Distributions
             var c = mode;
             var u = rnd.NextDouble();
 
-            return u < (c - a) / (b - a)
+            return u < (c - a)/(b - a)
-                       ? a + Math.Sqrt(u * (b - a) * (c - a))
+                ? a + Math.Sqrt(u*(b - a)*(c - a))
-                       : b - Math.Sqrt((1 - u) * (b - a) * (b - c)); ;
+                : b - Math.Sqrt((1 - u)*(b - a)*(b - c));
         }
 
         /// <summary>
@@ -417,4 +417,4 @@ namespace MathNet.Numerics.Distributions
         }
 
     }
-}
+}

src/Numerics/Financial/AbsoluteRiskMeasures.cs

@@ -37,7 +37,7 @@ namespace MathNet.Numerics.Financial
 {
     public static class AbsoluteRiskMeasures
     {
-        //Note: The following statistics would be condidered an absolute risk statistic in the finance realm as well.
+        //Note: The following statistics would be considered an absolute risk statistic in the finance realm as well.
         // Standard Deviation 
         // Annualized Standard Deviation = Math.Sqrt(Monthly Standard Deviation x ( 12 ))
         // Skewness

src/Numerics/LinearRegression/WeightedRegression.cs

@@ -64,7 +64,7 @@ namespace MathNet.Numerics.LinearRegression
         /// <param name="x">Predictor matrix X</param>
         /// <param name="y">Response vector Y</param>
         /// <param name="w">Weight matrix W, usually diagonal with an entry for each predictor (row).</param>
-        /// <param name="intercept">True if an intercept should be added as first artificial perdictor value. Default = false.</param>
+        /// <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
         public static T[] Weighted<T>(T[][] x, T[] y, T[] w, bool intercept = false) where T : struct, IEquatable<T>, IFormattable
         {
             var predictor = Matrix<T>.Build.DenseOfRowArrays(x);
@@ -82,7 +82,7 @@ namespace MathNet.Numerics.LinearRegression
         /// </summary>
         /// <param name="samples">List of sample vectors (predictor) together with their response.</param>
         /// <param name="weights">List of weights, one for each sample.</param>
-        /// <param name="intercept">True if an intercept should be added as first artificial perdictor value. Default = false.</param>
+        /// <param name="intercept">True if an intercept should be added as first artificial predictor value. Default = false.</param>
         public static T[] Weighted<T>(IEnumerable<Tuple<T[], T>> samples, T[] weights, bool intercept = false) where T : struct, IEquatable<T>, IFormattable
         {
             var xy = samples.UnpackSinglePass();

src/Numerics/Providers/LinearAlgebra/Mkl/MklLinearAlgebraProvider.cs

@@ -72,10 +72,10 @@ namespace MathNet.Numerics.Providers.LinearAlgebra.Mkl
     /// </summary>
     public partial class MklLinearAlgebraProvider : ManagedLinearAlgebraProvider
     {
-        int _nativeRevision = 0;
+        int _nativeRevision;
-        bool _nativeIX86 = false;
+        bool _nativeIX86;
-        bool _nativeX64 = false;
+        bool _nativeX64;
-        bool _nativeIA64 = false;
+        bool _nativeIA64;
 
         readonly MklConsistency _consistency;
         readonly MklPrecision _precision;
@@ -113,7 +113,7 @@ namespace MathNet.Numerics.Providers.LinearAlgebra.Mkl
         {
             // TODO: Choose x86 or x64 based on Environment.Is64BitProcess
 
-            int a = 0, b = 0, linearAlgebra = 0;
+            int a, b, linearAlgebra;
             try
             {
                 a = SafeNativeMethods.query_capability(0);

src/Numerics/SpecialFunctions/Gamma.cs

@@ -265,9 +265,13 @@ namespace MathNet.Numerics
             const double big = 4503599627370496.0;
             const double bigInv = 2.22044604925031308085e-16;
 
-            if (a < 0d || x < 0d)
+            if (a < 0d)
             {
-                throw new ArgumentOutOfRangeException("a,x", Properties.Resources.ArgumentNotNegative);
+                throw new ArgumentOutOfRangeException("a", Properties.Resources.ArgumentNotNegative);
+            }
+            if (x < 0d)
+            {
+                throw new ArgumentOutOfRangeException("x", Properties.Resources.ArgumentNotNegative);
             }
 
             if (a.AlmostEqual(0.0))
@@ -379,9 +383,13 @@ namespace MathNet.Numerics
                 return double.NaN;
             }
 
-            if (a < 0 || a.AlmostEqual(0.0) || y0 < 0 || y0 > 1)
+            if (a < 0 || a.AlmostEqual(0.0))
+            {
+                throw new ArgumentOutOfRangeException("a");
+            }
+            if (y0 < 0 || y0 > 1)
             {
-                throw new ArgumentOutOfRangeException("a,y0", Properties.Resources.ArgumentNotNegative);
+                throw new ArgumentOutOfRangeException("y0");
             }
 
             if (y0.AlmostEqual(0.0))

src/Numerics/Statistics/DescriptiveStatistics.cs

@@ -28,11 +28,11 @@
 // OTHER DEALINGS IN THE SOFTWARE.
 // </copyright>
 
+using System;
+using System.Collections.Generic;
+
 namespace MathNet.Numerics.Statistics
 {
-    using System;
-    using System.Collections.Generic;
-
     /// <summary>
     /// Computes the basic statistics of data set. The class meets the
     /// NIST standard of accuracy for mean, variance, and standard deviation
@@ -153,7 +153,7 @@ namespace MathNet.Numerics.Statistics
         /// Computes descriptive statistics from a stream of data values.
         /// </summary>
         /// <param name="data">A sequence of datapoints.</param>
-        private void Compute(IEnumerable<double> data)
+        void Compute(IEnumerable<double> data)
         {
             double mean = 0;
             double variance = 0;
@@ -165,20 +165,26 @@ namespace MathNet.Numerics.Statistics
             foreach (var xi in data)
             {
                 double delta = xi - mean;
-                double scaleDelta = delta / ++n;
+                double scaleDelta = delta/++n;
-                double scaleDeltaSqr = scaleDelta * scaleDelta;
+                double scaleDeltaSqr = scaleDelta*scaleDelta;
-                double tmpDelta = delta * (n - 1);
+                double tmpDelta = delta*(n - 1);
 
                 mean += scaleDelta;
 
-                kurtosis += tmpDelta * scaleDelta * scaleDeltaSqr * (n * n - 3 * n + 3)
+                kurtosis += tmpDelta*scaleDelta*scaleDeltaSqr*(n*n - 3*n + 3)
-                    + 6 * scaleDeltaSqr * variance - 4 * scaleDelta * skewness;
+                            + 6*scaleDeltaSqr*variance - 4*scaleDelta*skewness;
 
-                skewness += tmpDelta * scaleDeltaSqr * (n - 2) - 3 * scaleDelta * variance;
+                skewness += tmpDelta*scaleDeltaSqr*(n - 2) - 3*scaleDelta*variance;
-                variance += tmpDelta * scaleDelta;
+                variance += tmpDelta*scaleDelta;
 
-                if (minimum > xi) { minimum = xi; }
+                if (minimum > xi)
-                if (maximum < xi) { maximum = xi; }
+                {
+                    minimum = xi;
+                }
+                if (maximum < xi)
+                {
+                    maximum = xi;
+                }
             }
             SetStatistics(mean, variance, skewness, kurtosis, minimum, maximum, n);
         }
@@ -189,7 +195,7 @@ namespace MathNet.Numerics.Statistics
         /// Computes descriptive statistics from a stream of nullable data values.
         /// </summary>
         /// <param name="data">A sequence of datapoints.</param>
-        private void Compute(IEnumerable<double?> data)
+        void Compute(IEnumerable<double?> data)
         {
             double mean = 0;
             double variance = 0;
@@ -203,19 +209,25 @@ namespace MathNet.Numerics.Statistics
                 if (xi.HasValue)
                 {
                     double delta = xi.Value - mean;
-                    double scaleDelta = delta / ++n;
+                    double scaleDelta = delta/++n;
-                    double scaleDeltaSqr = scaleDelta * scaleDelta;
+                    double scaleDeltaSqr = scaleDelta*scaleDelta;
-                    double tmpDelta = delta * (n - 1);
+                    double tmpDelta = delta*(n - 1);
 
                     mean += scaleDelta;
 
-                    kurtosis += tmpDelta * scaleDelta * scaleDeltaSqr * (n * n - 3 * n + 3)
+                    kurtosis += tmpDelta*scaleDelta*scaleDeltaSqr*(n*n - 3*n + 3)
-                        + 6 * scaleDeltaSqr * variance - 4 * scaleDelta * skewness;
+                                + 6*scaleDeltaSqr*variance - 4*scaleDelta*skewness;
 
-                    skewness += tmpDelta * scaleDeltaSqr * (n - 2) - 3 * scaleDelta * variance;
+                    skewness += tmpDelta*scaleDeltaSqr*(n - 2) - 3*scaleDelta*variance;
-                    variance += tmpDelta * scaleDelta;
+                    variance += tmpDelta*scaleDelta;
-                    if (minimum > xi) { minimum = xi.Value; }
+                    if (minimum > xi)
-                    if (maximum < xi) { maximum = xi.Value; }
+                    {
+                        minimum = xi.Value;
+                    }
+                    if (maximum < xi)
+                    {
+                        maximum = xi.Value;
+                    }
                 }
             }
 
@@ -224,10 +236,10 @@ namespace MathNet.Numerics.Statistics
         }
 
         /// <summary>
-        /// Computes descriptive statistics from a stream of data values using high accuracy.
+        /// Computes descriptive statistics from a stream of data values.
         /// </summary>
         /// <param name="data">A sequence of datapoints.</param>
-        private void ComputeDecimal(IEnumerable<double> data)
+        void ComputeDecimal(IEnumerable<double> data)
         {
             decimal mean = 0;
             decimal variance = 0;
@@ -236,22 +248,29 @@ namespace MathNet.Numerics.Statistics
             decimal minimum = Decimal.MaxValue;
             decimal maximum = Decimal.MinValue;
             int n = 0;
-            foreach (decimal xi in data)
+            foreach (double x in data)
             {
+                decimal xi = (decimal)x;
                 decimal delta = xi - mean;
-                decimal scaleDelta = delta / ++n;
+                decimal scaleDelta = delta/++n;
-                decimal scaleDeltaSQR = scaleDelta * scaleDelta;
+                decimal scaleDeltaSQR = scaleDelta*scaleDelta;
-                decimal tmpDelta = delta * (n - 1);
+                decimal tmpDelta = delta*(n - 1);
 
                 mean += scaleDelta;
 
-                kurtosis += tmpDelta * scaleDelta * scaleDeltaSQR * (n * n - 3 * n + 3)
+                kurtosis += tmpDelta*scaleDelta*scaleDeltaSQR*(n*n - 3*n + 3)
-                    + 6 * scaleDeltaSQR * variance - 4 * scaleDelta * skewness;
+                            + 6*scaleDeltaSQR*variance - 4*scaleDelta*skewness;
 
-                skewness += tmpDelta * scaleDeltaSQR * (n - 2) - 3 * scaleDelta * variance;
+                skewness += tmpDelta*scaleDeltaSQR*(n - 2) - 3*scaleDelta*variance;
-                variance += tmpDelta * scaleDelta;
+                variance += tmpDelta*scaleDelta;
-                if (minimum > xi) { minimum = xi; }
+                if (minimum > xi)
-                if (maximum < xi) { maximum = xi; }
+                {
+                    minimum = xi;
+                }
+                if (maximum < xi)
+                {
+                    maximum = xi;
+                }
             }
 
             SetStatistics((double)mean, (double)variance, (double)skewness, (double)kurtosis, (double)minimum, (double)maximum, n);
@@ -259,10 +278,10 @@ namespace MathNet.Numerics.Statistics
         }
 
         /// <summary>
-        /// Computes descriptive statistics from a stream of nullable data values using high accuracy.
+        /// Computes descriptive statistics from a stream of nullable data values.
         /// </summary>
         /// <param name="data">A sequence of datapoints.</param>
-        private void ComputeDecimal(IEnumerable<double?> data)
+        void ComputeDecimal(IEnumerable<double?> data)
         {
             decimal mean = 0;
             decimal variance = 0;
@@ -271,24 +290,31 @@ namespace MathNet.Numerics.Statistics
             decimal minimum = Decimal.MaxValue;
             decimal maximum = Decimal.MinValue;
             int n = 0;
-            foreach (decimal? xi in data)
+            foreach (double? x in data)
             {
-                if (xi.HasValue)
+                if (x.HasValue)
                 {
-                    decimal delta = xi.Value - mean;
+                    decimal xi = (decimal)x.Value;
-                    decimal scaleDelta = delta / ++n;
+                    decimal delta = xi - mean;
-                    decimal scaleDeltaSQR = scaleDelta * scaleDelta;
+                    decimal scaleDelta = delta/++n;
-                    decimal tmpDelta = delta * (n - 1);
+                    decimal scaleDeltaSQR = scaleDelta*scaleDelta;
+                    decimal tmpDelta = delta*(n - 1);
 
                     mean += scaleDelta;
 
-                    kurtosis += tmpDelta * scaleDelta * scaleDeltaSQR * (n * n - 3 * n + 3)
+                    kurtosis += tmpDelta*scaleDelta*scaleDeltaSQR*(n*n - 3*n + 3)
-                        + 6 * scaleDeltaSQR * variance - 4 * scaleDelta * skewness;
+                                + 6*scaleDeltaSQR*variance - 4*scaleDelta*skewness;
 
-                    skewness += tmpDelta * scaleDeltaSQR * (n - 2) - 3 * scaleDelta * variance;
+                    skewness += tmpDelta*scaleDeltaSQR*(n - 2) - 3*scaleDelta*variance;
-                    variance += tmpDelta * scaleDelta;
+                    variance += tmpDelta*scaleDelta;
-                    if (minimum > xi) { minimum = xi.Value; }
+                    if (minimum > xi)
-                    if (maximum < xi) { maximum = xi.Value; }
+                    {
+                        minimum = xi;
+                    }
+                    if (maximum < xi)
+                    {
+                        maximum = xi;
+                    }
                 }
             }
             SetStatistics((double)mean, (double)variance, (double)skewness, (double)kurtosis, (double)minimum, (double)maximum, n);
@@ -305,7 +331,7 @@ namespace MathNet.Numerics.Statistics
         /// <param name="minimum">For setting Minimum.</param>
         /// <param name="maximum">For setting Maximum.</param>
         /// <param name="n">For setting Count.</param>
-        private void SetStatistics(double mean, double variance, double skewness, double kurtosis, double minimum, double maximum, int n)
+        void SetStatistics(double mean, double variance, double skewness, double kurtosis, double minimum, double maximum, int n)
         {
             Mean = mean;
             Count = n;
@@ -315,24 +341,23 @@ namespace MathNet.Numerics.Statistics
                 Maximum = maximum;
                 if (n > 1)
                 {
-                    Variance = variance / (n - 1);
+                    Variance = variance/(n - 1);
                     StandardDeviation = Math.Sqrt(Variance);
                 }
                 if (Variance != 0)
                 {
                     if (n > 2)
                     {
-                        Skewness = (double)n / ((n - 1) * (n - 2)) * (skewness / (Variance * StandardDeviation));
+                        Skewness = (double)n/((n - 1)*(n - 2))*(skewness/(Variance*StandardDeviation));
                     }
 
                     if (n > 3)
                     {
-                        Kurtosis = ((double)n * n - 1) / ((n - 2) * (n - 3))
+                        Kurtosis = ((double)n*n - 1)/((n - 2)*(n - 3))
-                            * (n * kurtosis / (variance * variance) - 3 + 6.0 / (n + 1));
+                                   *(n*kurtosis/(variance*variance) - 3 + 6.0/(n + 1));
                     }
                 }
             }
         }
     }
-
 }