Adding extension methods to simplify how distributions can be estimat…

…ed from the data (without requiring the distribution to be created first).

Sources/Accord.Statistics/Measures/Tools.cs

@@ -28,6 +28,8 @@ namespace Accord.Statistics
     using Accord.Math.Decompositions;
     using Accord.Statistics.Kernels;
     using AForge;
+    using Accord.Statistics.Distributions;
+    using Accord.Statistics.Distributions.Fitting;
 
     /// <summary>
     ///   Set of statistics functions.
@@ -441,6 +443,121 @@ public static double[][] Whitening(double[][] value, out double[][] transformMat
             return Matrix.Dot(value, transformMatrix);
         }
 
+        /// <summary>
+        ///   Creates a new distribution that has been fit to a given set of observations.
+        /// </summary>
+        /// 
+        /// <param name="observations">The array of observations to fit the model against. The array
+        ///   elements can be either of type double (for univariate data) or
+        ///   type double[] (for multivariate data).</param>
+        /// <param name="weights">The weight vector containing the weight for each of the samples.</param>
+        ///   
+        public static TDistribution Fit<TDistribution>(this double[] observations, double[] weights = null)
+            where TDistribution : IFittable<double>, new()
+        {
+            var dist = new TDistribution();
+            dist.Fit(observations, weights);
+            return dist;
+        }
+
+        /// <summary>
+        ///   Creates a new distribution that has been fit to a given set of observations.
+        /// </summary>
+        /// 
+        /// <param name="observations">The array of observations to fit the model against. The array
+        ///   elements can be either of type double (for univariate data) or
+        ///   type double[] (for multivariate data).</param>
+        /// <param name="weights">The weight vector containing the weight for each of the samples.</param>
+        ///   
+        public static TDistribution Fit<TDistribution>(this double[][] observations, double[] weights = null)
+            where TDistribution : IFittable<double[]>, new()
+        {
+            var dist = new TDistribution();
+            dist.Fit(observations, weights);
+            return dist;
+        }
+
+        /// <summary>
+        ///   Creates a new distribution that has been fit to a given set of observations.
+        /// </summary>
+        /// 
+        /// <param name="observations">The array of observations to fit the model against. The array
+        ///   elements can be either of type double (for univariate data) or
+        ///   type double[] (for multivariate data).</param>
+        /// <param name="weights">The weight vector containing the weight for each of the samples.</param>
+        /// <param name="options">Optional arguments which may be used during fitting, such
+        ///   as regularization constants and additional parameters.</param>
+        ///   
+        public static TDistribution Fit<TDistribution, TOptions>(this double[] observations, TOptions options, double[] weights = null)
+            where TDistribution : IFittable<double, TOptions>, new()
+            where TOptions : class, IFittingOptions
+        {
+            var dist = new TDistribution();
+            dist.Fit(observations, weights, options);
+            return dist;
+        }
+
+        /// <summary>
+        ///   Creates a new distribution that has been fit to a given set of observations.
+        /// </summary>
+        /// 
+        /// <param name="observations">The array of observations to fit the model against. The array
+        ///   elements can be either of type double (for univariate data) or
+        ///   type double[] (for multivariate data).</param>
+        /// <param name="weights">The weight vector containing the weight for each of the samples.</param>
+        /// <param name="options">Optional arguments which may be used during fitting, such
+        ///   as regularization constants and additional parameters.</param>
+        ///   
+        public static TDistribution Fit<TDistribution, TOptions>(this double[][] observations, TOptions options, double[] weights = null)
+            where TDistribution : IFittable<double[], TOptions>, new()
+            where TOptions : class, IFittingOptions
+        {
+            var dist = new TDistribution();
+            dist.Fit(observations, weights, options);
+            return dist;
+        }
+
+        /// <summary>
+        ///   Creates a new distribution that has been fit to a given set of observations.
+        /// </summary>
+        /// 
+        /// <param name="distribution">The distribution whose parameters should be fitted to the samples.</param>
+        /// <param name="observations">The array of observations to fit the model against. The array
+        ///   elements can be either of type double (for univariate data) or
+        ///   type double[] (for multivariate data).</param>
+        /// <param name="weights">The weight vector containing the weight for each of the samples.</param>
+        ///   
+        public static TDistribution FitNew<TDistribution, TObservations>(
+            this TDistribution distribution, TObservations[] observations, double[] weights = null)
+            where TDistribution : IFittable<TObservations>, ICloneable
+        {
+            var clone = (TDistribution)distribution.Clone();
+            clone.Fit(observations, weights);
+            return clone;
+        }
+
+        /// <summary>
+        ///   Creates a new distribution that has been fit to a given set of observations.
+        /// </summary>
+        /// 
+        /// <param name="distribution">The distribution whose parameters should be fitted to the samples.</param>
+        /// <param name="observations">The array of observations to fit the model against. The array
+        ///   elements can be either of type double (for univariate data) or
+        ///   type double[] (for multivariate data).</param>
+        /// <param name="weights">The weight vector containing the weight for each of the samples.</param>
+        /// <param name="options">Optional arguments which may be used during fitting, such
+        ///   as regularization constants and additional parameters.</param>
+        ///   
+        public static TDistribution FitNew<TDistribution, TObservations, TOptions>(
+            this TDistribution distribution, TObservations[] observations, TOptions options, double[] weights = null)
+            where TDistribution : IFittable<TObservations, TOptions>, ICloneable
+            where TOptions : class, IFittingOptions
+        {
+            var clone = (TDistribution)distribution.Clone();
+            clone.Fit(observations, weights, options);
+            return clone;
+        }
+
     }
 }
 

Unit Tests/Accord.Tests.Statistics/Distributions/Univariate/Continuous/NormalDistributionTest.cs

@@ -27,8 +27,9 @@ namespace Accord.Tests.Statistics
     using Accord.Statistics;
     using Accord.Statistics.Distributions.Multivariate;
     using Accord.Statistics.Distributions.Univariate;
-    using NUnit.Framework;
-
+    using NUnit.Framework;
+    using Accord.Statistics.Distributions.Fitting;
+
     [TestFixture]
     public class NormalDistributionTest
     {
@@ -107,8 +108,65 @@ public void FitTest()
             target.Fit(observations2, weights2);
 
             Assert.AreEqual(expectedMean, target.Mean);
-        }
-
+        }
+
+
+        [Test]
+        public void FitExtensionTest_options()
+        {
+            NormalDistribution target = new NormalDistribution();
+            double[] observations = { 0.10, 0.40, 2.00, 2.00 };
+            double[] weights = { 0.25, 0.25, 0.25, 0.25 };
+            target.Fit(observations, weights);
+            NormalDistribution same = observations.Fit<NormalDistribution, NormalOptions>(new NormalOptions()
+            {
+                Regularization = 10
+            }, weights);
+            Assert.AreNotSame(same, target);
+            Assert.AreEqual(same.ToString(), target.ToString());
+
+            NormalDistribution copy = target.FitNew(observations, new NormalOptions()
+            {
+                Regularization = 10
+            }, weights);
+            Assert.AreNotSame(copy, target);
+            Assert.AreEqual(copy.ToString(), target.ToString());
+        }
+
+
+        [Test]
+        public void FitExtensionTest_weights()
+        {
+            NormalDistribution target = new NormalDistribution();
+            double[] observations = { 0.10, 0.40, 2.00, 2.00 };
+            double[] weights = { 0.25, 0.25, 0.25, 0.25 };
+            target.Fit(observations, weights);
+            NormalDistribution same = observations.Fit<NormalDistribution>(weights);
+            Assert.AreNotSame(same, target);
+            Assert.AreEqual(same.ToString(), target.ToString());
+
+            NormalDistribution copy = target.FitNew(observations, weights);
+            Assert.AreNotSame(copy, target);
+            Assert.AreEqual(copy.ToString(), target.ToString());
+        }
+
+        [Test]
+        public void FitExtensionTest()
+        {
+            NormalDistribution target = new NormalDistribution();
+            double[] observations = { 0.10, 0.40, 2.00, 2.00 };
+            target.Fit(observations);
+            NormalDistribution same = observations.Fit<NormalDistribution>();
+            Assert.AreNotSame(same, target);
+            Assert.AreEqual(same.ToString(), target.ToString());
+
+            NormalDistribution copy = target.FitNew(observations);
+            Assert.AreNotSame(copy, target);
+            Assert.AreEqual(copy.ToString(), target.ToString());
+        }
+
+
+
         [Test]
         public void FitTest2()
         {