GC-82: Serializable annotation is needed on class Accord.Statistics.A…

…nalysis.ContrastFunctions.Logcosh

Sources/Accord.Statistics/Accord.Statistics.csproj

@@ -110,6 +110,10 @@
     <Compile Include="Analysis\Base\IMultivariateRegressionAnalysis.cs" />
     <Compile Include="Analysis\Base\IRegressionAnalysis.cs" />
     <Compile Include="Analysis\Base\IDiscriminantAnalysis.cs" />
+    <Compile Include="Analysis\Contrast Functions\Exponential.cs" />
+    <Compile Include="Analysis\Contrast Functions\IContrastFunction.cs" />
+    <Compile Include="Analysis\Contrast Functions\Kurtosis.cs" />
+    <Compile Include="Analysis\Contrast Functions\Logcosh.cs" />
     <Compile Include="Analysis\Performance\WeightedConfusionMatrix.cs" />
     <Compile Include="Analysis\ProportionalHazardsAnalysis.cs" />
     <Compile Include="Analysis\MultipleLinearRegressionAnalysis.cs" />

Sources/Accord.Statistics/Analysis/Contrast Functions/Exponential.cs

@@ -0,0 +1,99 @@
+// Accord Statistics Library
+// The Accord.NET Framework
+// http://accord-framework.net
+//
+// Copyright © César Souza, 2009-2013
+// cesarsouza at gmail.com
+//
+//    This library is free software; you can redistribute it and/or
+//    modify it under the terms of the GNU Lesser General Public
+//    License as published by the Free Software Foundation; either
+//    version 2.1 of the License, or (at your option) any later version.
+//
+//    This library 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 GNU
+//    Lesser General Public License for more details.
+//
+//    You should have received a copy of the GNU Lesser General Public
+//    License along with this library; if not, write to the Free Software
+//    Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
+//
+
+namespace Accord.Statistics.Analysis.ContrastFunctions
+{
+    using System;
+
+    /// <summary>
+    ///   Exponential contrast function.
+    /// </summary>
+    /// 
+    /// <remarks>
+    ///   According to Hyvärinen, the Exponential contrast function may be
+    ///   used when the independent components are highly super-Gaussian or
+    ///   when robustness is very important.
+    /// </remarks>
+    ///
+    /// <seealso cref="IndependentComponentAnalysis"/>
+    ///
+    [Serializable]
+    public class Exponential : IContrastFunction
+    {
+        double alpha = 1;
+
+        /// <summary>
+        ///   Initializes a new instance of the <see cref="Exponential"/> class.
+        /// </summary>
+        /// <param name="alpha">The exponential alpha constant. Default is 1.</param>
+        /// 
+        public Exponential(double alpha)
+        {
+            this.alpha = alpha;
+        }
+
+        /// <summary>
+        ///   Gets the exponential alpha constant.
+        /// </summary>
+        /// 
+        public double Alpha { get { return alpha; } }
+
+        /// <summary>
+        ///   Initializes a new instance of the <see cref="Exponential"/> class.
+        /// </summary>
+        /// 
+        public Exponential() { }
+
+        /// <summary>
+        ///   Contrast function.
+        /// </summary>
+        /// 
+        /// <param name="x">The vector of observations.</param>
+        /// <param name="output">At method's return, this parameter
+        /// should contain the evaluation of function over the vector
+        /// of observations <paramref name="x"/>.</param>
+        /// <param name="derivative">At method's return, this parameter
+        /// should contain the evaluation of function derivative over
+        /// the vector of observations <paramref name="x"/>.</param>
+        /// 
+        public void Evaluate(double[] x, double[] output, double[] derivative)
+        {
+            // Exponential contrast function and its derivative, as given
+            //  in original Hyvärinen's paper. See main references for the
+            //  Independent Component Analysis class for details.
+
+            for (int j = 0; j < x.Length; j++)
+            {
+                double w = x[j];
+                double e = System.Math.Exp(-alpha * (w * w) / 2.0);
+
+                // g(w*x) = wx * exp(-(wx^2)/2)
+                output[j] = w * e;
+
+                // g'(w*x) = (1 - wx^2) * exp(-(wx^2)/2)
+                derivative[j] = (1.0 - alpha * w * w) * e;
+            }
+        }
+    }
+
+
+}

Sources/Accord.Statistics/Analysis/Contrast Functions/IContrastFunction.cs

@@ -0,0 +1,54 @@
+// Accord Statistics Library
+// The Accord.NET Framework
+// http://accord-framework.net
+//
+// Copyright © César Souza, 2009-2013
+// cesarsouza at gmail.com
+//
+//    This library is free software; you can redistribute it and/or
+//    modify it under the terms of the GNU Lesser General Public
+//    License as published by the Free Software Foundation; either
+//    version 2.1 of the License, or (at your option) any later version.
+//
+//    This library 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 GNU
+//    Lesser General Public License for more details.
+//
+//    You should have received a copy of the GNU Lesser General Public
+//    License along with this library; if not, write to the Free Software
+//    Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
+//
+
+namespace Accord.Statistics.Analysis.ContrastFunctions
+{
+    using System;
+
+    /// <summary>
+    ///   Common interface for contrast functions.
+    /// </summary>
+    /// 
+    /// <remarks>
+    ///   Contrast functions are used as objective functions in
+    ///   neg-entropy calculations.
+    /// </remarks>
+    /// 
+    /// <seealso cref="IndependentComponentAnalysis"/>
+    ///
+    public interface IContrastFunction
+    {
+        /// <summary>
+        ///   Contrast function.
+        /// </summary>
+        /// 
+        /// <param name="x">The vector of observations.</param>
+        /// <param name="output">At method's return, this parameter
+        ///   should contain the evaluation of function over the vector
+        ///   of observations <paramref name="x"/>.</param>
+        /// <param name="derivative">At method's return, this parameter
+        ///   should contain the evaluation of function derivative over 
+        ///   the vector of observations <paramref name="x"/>.</param>
+        ///   
+        void Evaluate(double[] x, double[] output, double[] derivative);
+    }
+}

Sources/Accord.Statistics/Analysis/Contrast Functions/Kurtosis.cs

@@ -0,0 +1,78 @@
+// Accord Statistics Library
+// The Accord.NET Framework
+// http://accord-framework.net
+//
+// Copyright © César Souza, 2009-2013
+// cesarsouza at gmail.com
+//
+//    This library is free software; you can redistribute it and/or
+//    modify it under the terms of the GNU Lesser General Public
+//    License as published by the Free Software Foundation; either
+//    version 2.1 of the License, or (at your option) any later version.
+//
+//    This library 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 GNU
+//    Lesser General Public License for more details.
+//
+//    You should have received a copy of the GNU Lesser General Public
+//    License along with this library; if not, write to the Free Software
+//    Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
+//
+
+namespace Accord.Statistics.Analysis.ContrastFunctions
+{
+    using System;
+
+    /// <summary>
+    ///   Kurtosis contrast function.
+    /// </summary>
+    /// <remarks>
+    ///   According to using to Hyvärinen, the kurtosis contrast function is
+    ///   justified on statistical grounds only for estimating sub-Gaussian
+    ///   independent components when there are no outliers.
+    /// </remarks>
+    ///
+    /// <seealso cref="IndependentComponentAnalysis"/>
+    ///
+    [Serializable]
+    public class Kurtosis : IContrastFunction
+    {
+
+        /// <summary>
+        ///   Initializes a new instance of the <see cref="Kurtosis"/> class.
+        /// </summary>
+        /// 
+        public Kurtosis() { }
+
+        /// <summary>
+        ///   Contrast function.
+        /// </summary>
+        /// 
+        /// <param name="x">The vector of observations.</param>
+        /// <param name="output">At method's return, this parameter
+        /// should contain the evaluation of function over the vector
+        /// of observations <paramref name="x"/>.</param>
+        /// <param name="derivative">At method's return, this parameter
+        /// should contain the evaluation of function derivative over
+        /// the vector of observations <paramref name="x"/>.</param>
+        /// 
+        public void Evaluate(double[] x, double[] output, double[] derivative)
+        {
+            for (int j = 0; j < x.Length; j++)
+            {
+                // Kurtosis contrast function and its derivative, as given
+                //  in original Hyvärinen's paper. See main references for the
+                //  Independent Component Analysis class for details.
+
+                double v = x[j];
+
+                // g(w*x)
+                output[j] = v * v * v;
+
+                // g'(w*x)
+                derivative[j] = (1.0 / 3.0) * v * v;
+            }
+        }
+    }
+}

Sources/Accord.Statistics/Analysis/Contrast Functions/Logcosh.cs

@@ -0,0 +1,97 @@
+// Accord Statistics Library
+// The Accord.NET Framework
+// http://accord-framework.net
+//
+// Copyright © César Souza, 2009-2013
+// cesarsouza at gmail.com
+//
+//    This library is free software; you can redistribute it and/or
+//    modify it under the terms of the GNU Lesser General Public
+//    License as published by the Free Software Foundation; either
+//    version 2.1 of the License, or (at your option) any later version.
+//
+//    This library 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 GNU
+//    Lesser General Public License for more details.
+//
+//    You should have received a copy of the GNU Lesser General Public
+//    License along with this library; if not, write to the Free Software
+//    Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
+//
+
+namespace Accord.Statistics.Analysis.ContrastFunctions
+{
+    using System;
+
+    /// <summary>
+    ///   Log-cosh (Hyperbolic Tangent) contrast function.
+    /// </summary>
+    /// 
+    /// <remarks>
+    ///   According to Hyvärinen, the Logcosh contrast function
+    ///   is a good general-purpose contrast function.
+    /// </remarks>
+    /// 
+    /// <seealso cref="IndependentComponentAnalysis"/>
+    /// 
+    [Serializable]
+    public class Logcosh : IContrastFunction
+    {
+        double alpha = 1;
+
+        /// <summary>
+        ///   Initializes a new instance of the <see cref="Logcosh"/> class.
+        /// </summary>
+        /// 
+        public Logcosh() { }
+
+        /// <summary>
+        ///   Initializes a new instance of the <see cref="Logcosh"/> class.
+        /// </summary>
+        /// 
+        /// <param name="alpha">The log-cosh alpha constant. Default is 1.</param>
+        /// 
+        public Logcosh(double alpha)
+        {
+            this.alpha = alpha;
+        }
+
+        /// <summary>
+        ///   Gets the exponential log-cosh constant.
+        /// </summary>
+        /// 
+        public double Alpha { get { return alpha; } }
+
+        /// <summary>
+        ///   Contrast function.
+        /// </summary>
+        /// 
+        /// <param name="x">The vector of observations.</param>
+        /// <param name="output">At method's return, this parameter
+        /// should contain the evaluation of function over the vector
+        /// of observations <paramref name="x"/>.</param>
+        /// <param name="derivative">At method's return, this parameter
+        /// should contain the evaluation of function derivative over
+        /// the vector of observations <paramref name="x"/>.</param>
+        /// 
+        public void Evaluate(double[] x, double[] output, double[] derivative)
+        {
+            // Log-cosh contrast function and its derivative, as given
+            //  in original Hyvärinen's paper. See main references for the
+            //  Independent Component Analysis class for details.
+
+            for (int j = 0; j < x.Length; j++)
+            {
+                double f;
+
+                // g(w*x)
+                f = output[j] = System.Math.Tanh(alpha * x[j]);
+
+                // g'(w*x)
+                derivative[j] = alpha * (1.0 - f * f);
+            }
+        }
+    }
+
+}