GH-398: PrincipalComponentAnalysis serialization error in v3.4.0

Fixing serialization of PCA, KPCA, LDA, KDA and other classes that contain a CancellationToken.

Sources/Accord.Core/ExtensionMethods.cs

@@ -52,6 +52,7 @@ public static class ExtensionMethods
         /// 
         /// <returns>A copy of the collection where each element has also been copied.</returns>
         /// 
+        [Obsolete("Please use Matrix.Copy() instead.")]
         public static T DeepClone<T>(this T list)
             where T : IList<ICloneable>, ICloneable
         {

Sources/Accord.Core/Serializer.cs

@@ -190,6 +190,19 @@ public static T Load<T>(Stream stream, BinaryFormatter formatter)
             }
         }
 
+        /// <summary>
+        ///   Performs a deep copy of an object by serializing and deserializing it.
+        /// </summary>
+        /// 
+        /// <typeparam name="T">The type of the model to be copied.</typeparam>
+        /// <param name="obj">The object.</param>
+        /// 
+        /// <returns>A deep copy of the given object.</returns>
+        /// 
+        public static T DeepClone<T>(this T obj)
+        {
+            return Load<T>(Save<T>(obj));
+        }
 
 
         private static SerializationBinder GetBinder(Type type)

Sources/Accord.MachineLearning/Rules/Apriori.cs

@@ -68,6 +68,9 @@ public class Apriori<T> :
         IUnsupervisedLearning<AssociationRuleMatcher<T>, SortedSet<T>, SortedSet<T>[]>,
         IUnsupervisedLearning<AssociationRuleMatcher<T>, T[], T[][]>
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
+
         private int supportMin;
         private double confidence;
         private Dictionary<SortedSet<T>, int> frequent;
@@ -87,7 +90,11 @@ public Dictionary<SortedSet<T>, int> Frequent
         /// stop the learning algorithm while it is running.
         /// </summary>
         /// <value>The token.</value>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         /// <summary>
         /// Initializes a new instance of the <see cref="Apriori{T}"/> class.

Sources/Accord.MachineLearning/VectorMachines/Learning/Base/BaseSupportVectorCalibration.cs

@@ -44,12 +44,18 @@ public abstract class BaseSupportVectorCalibration<TModel, TKernel, TInput> :
         where TModel : SupportVectorMachine<TKernel, TInput>
         where TInput : ICloneable
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
 
         /// <summary>
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         // Support Vector Machine parameters
         private TModel machine;

Sources/Accord.MachineLearning/VectorMachines/Learning/Base/BaseSupportVectorRegression.cs

@@ -42,12 +42,18 @@ public abstract class BaseSupportVectorRegression<TModel, TKernel, TInput> :
         where TModel : SupportVectorMachine<TKernel, TInput>, ISupportVectorMachine<TInput>
         where TInput : ICloneable
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
 
         /// <summary>
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         // Training data
         private TInput[] inputs;

Sources/Accord.MachineLearning/VectorMachines/Learning/OneclassSupportVectorLearning.cs

@@ -80,6 +80,9 @@ public class OneclassSupportVectorLearning<TModel, TKernel, TInput>
         where TModel : SupportVectorMachine<TKernel, TInput>
         where TInput : ICloneable
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
+
         private double[] alpha;
         private TInput[] inputs;
         private double nu = 0.5;
@@ -122,7 +125,11 @@ public OneclassSupportVectorLearning()
         ///   stop the learning algorithm while it is running.
         /// </summary>
         /// 
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         /// <summary>
         ///   Convergence tolerance. Default value is 1e-2.

Sources/Accord.Math/Accord.Math.csproj

@@ -113,6 +113,7 @@
     <Compile Include="Decompositions\SingularValueDecompositionF.cs">
       <DependentUpon>SingularValueDecomposition.tt</DependentUpon>
     </Compile>
+    <Compile Include="Distances\Angular.cs" />
     <Compile Include="Matrix\Jagged.Product.Generated.cs">
       <AutoGen>True</AutoGen>
       <DesignTime>True</DesignTime>

Sources/Accord.Math/Optimization/Base/BaseGradientOptimizationMethod.cs

@@ -31,12 +31,19 @@ namespace Accord.Math.Optimization
     /// 
     public abstract class BaseGradientOptimizationMethod : BaseOptimizationMethod
     {
+        [NonSerialized]
+        private CancellationToken token = new CancellationToken();
+
         /// <summary>
         ///   Gets or sets a cancellation token that can be used to
         ///   stop the learning algorithm while it is running.
         /// </summary>
         /// 
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         /// <summary>
         ///   Gets or sets a function returning the gradient

Sources/Accord.Math/Optimization/Unconstrained/FanChenLinQuadraticOptimization.cs

@@ -92,6 +92,8 @@ namespace Accord.Math.Optimization
     ///
     public class FanChenLinQuadraticOptimization : IOptimizationMethod
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
 
         const double TAU = 1e-12;
 
@@ -146,7 +148,11 @@ public double[] Solution
         ///   stop the learning algorithm while it is running.
         /// </summary>
         /// 
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         /// <summary>
         ///   Gets the output of the function at the current <see cref="Solution" />.

Sources/Accord.Math/Optimization/Unconstrained/Least Squares/GaussNewton.cs

@@ -39,6 +39,8 @@ namespace Accord.Math.Optimization
     /// 
     public class GaussNewton : ILeastSquaresMethod
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
 
         private int numberOfParameters;
 
@@ -82,7 +84,11 @@ public class GaussNewton : ILeastSquaresMethod
         ///   stop the learning algorithm while it is running.
         /// </summary>
         /// 
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         /// <summary>
         ///   Gets the number of variables (free parameters) in the optimization problem.

Sources/Accord.Statistics/Accord.MachineLearning/MinimumMeanDistanceClassifier.cs

@@ -47,14 +47,21 @@ namespace Accord.MachineLearning
     public class MinimumMeanDistanceClassifier : MulticlassScoreClassifierBase<double[]>,
         ISupervisedLearning<MinimumMeanDistanceClassifier, double[], int>
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
+
         private double[][] means;
         private IDistance<double[]> distance = new SquareEuclidean();
 
         /// <summary>
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         /// <summary>
         ///   Gets or sets the class means to which samples will be compared against.

Sources/Accord.Statistics/Analysis/BaseDiscriminantAnalysis.cs

@@ -40,6 +40,9 @@ namespace Accord.Statistics.Analysis
     public abstract class BaseDiscriminantAnalysis : TransformBase<double[], double[]>
 #pragma warning restore 612, 618
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
+
         private int numSamples;
         private int numClasses;
         private double[] totalMeans;
@@ -141,7 +144,11 @@ protected void Init(double[][] inputs, int[] outputs)
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
 
         /// <summary>

Sources/Accord.Statistics/Analysis/LogisticRegressionAnalysis.cs

@@ -93,11 +93,18 @@ public class LogisticRegressionAnalysis : TransformBase<double[], double>, // TO
         ISupervisedLearning<LogisticRegression, double[], int>,
         ISupervisedLearning<LogisticRegression, double[], double>
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
+
         /// <summary>
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         private LogisticRegression regression;
 

Sources/Accord.Statistics/Analysis/MultinomialLogisticRegressionAnalysis.cs

@@ -68,12 +68,19 @@ public class MultinomialLogisticRegressionAnalysis : TransformBase<double[], int
         ISupervisedLearning<MultinomialLogisticRegression, double[], double[]>,
         ISupervisedLearning<MultinomialLogisticRegression, double[], int>
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
+
         /// <summary>
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
         /// 
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         int inputCount;
         int outputCount;

Sources/Accord.Statistics/Analysis/MultipleLinearRegressionAnalysis.cs

@@ -87,12 +87,18 @@ public class MultipleLinearRegressionAnalysis : TransformBase<double[], double>,
         IRegressionAnalysis, IAnova,
         ISupervisedLearning<MultipleLinearRegression, double[], double>
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
 
         /// <summary>
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
 
         internal MultipleLinearRegression regression;

Sources/Accord.Statistics/Analysis/PartialLeastSquaresAnalysis.cs

@@ -115,11 +115,18 @@ public class PartialLeastSquaresAnalysis : MultipleTransformBase<double[], doubl
         ISupervisedLearning<MultivariateLinearRegression, double[], double[]>
 #pragma warning restore 612, 618
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
+
         /// <summary>
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         [Obsolete]
         internal double[,] sourceX;

Sources/Accord.Statistics/Analysis/ProportionalHazardsAnalysis.cs

@@ -79,11 +79,18 @@ namespace Accord.Statistics.Analysis
     public class ProportionalHazardsAnalysis : IRegressionAnalysis,
         ISupervisedLearning<ProportionalHazards, Tuple<double[], double>, int>
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
+
         /// <summary>
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         private ProportionalHazards regression;
 

Sources/Accord.Statistics/Models/Fields/Learning/Hidden/HiddenConjugateGradientLearning.cs

@@ -47,6 +47,8 @@ public class HiddenConjugateGradientLearning<T> :
         IHiddenConditionalRandomFieldLearning<T>,
         IConvergenceLearning, IDisposable
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
 
         private ForwardBackwardGradient<T> calculator;
         private ConjugateGradient optimizer;
@@ -56,7 +58,11 @@ public class HiddenConjugateGradientLearning<T> :
         /// stop the learning algorithm while it is running.
         /// </summary>
         /// 
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
         /// <summary>
         ///   Gets or sets the model being trained.

Sources/Accord.Statistics/Models/Fields/Learning/Hidden/HiddenGradientDescentLearning.cs

@@ -52,6 +52,9 @@ public class HiddenGradientDescentLearning<T> :
         IHiddenConditionalRandomFieldLearning<T>,
         IConvergenceLearning, IDisposable
     {
+        [NonSerialized]
+        CancellationToken token = new CancellationToken();
+
         private double learningRate = 100;
         private ISingleValueConvergence convergence;
 
@@ -71,7 +74,11 @@ public class HiddenGradientDescentLearning<T> :
         /// Gets or sets a cancellation token that can be used to
         /// stop the learning algorithm while it is running.
         /// </summary>
-        public CancellationToken Token { get; set; }
+        public CancellationToken Token
+        {
+            get { return token; }
+            set { token = value; }
+        }
 
 
         /// <summary>