Fixing non CLS-compliant code and malformed XML-comments

src/Numerics/LinearAlgebra/Matrix.Arithmetic.cs

@@ -1720,7 +1720,8 @@ namespace MathNet.Numerics.LinearAlgebra
         /// matrix and a given other matrix being the 'x' of atan2 and the
         /// 'this' matrix being the 'y'
         /// </summary>
-        /// <param name="other"></param>
+        /// <param name="other">The other matrix 'y'</param>
+        /// <param name="result">The matrix with the result and 'x'</param>
         /// <returns></returns>
         public void PointwiseAtan2(Matrix<T> other, Matrix<T> result)
         {

src/Numerics/LinearAlgebra/Vector.Arithmetic.cs

@@ -1028,6 +1028,7 @@ namespace MathNet.Numerics.LinearAlgebra
         /// </summary>
         /// <param name="f">Function which takes a scalar and a vector, modifies the vector in place and returns void</param>
         /// <param name="x">The scalar to be passed to the function</param>
+        /// <param name="result">The vector where the result will be placed</param>
         /// <exception cref="ArgumentException">If this vector and <paramref name="result"/> are not the same size.</exception>
         protected void PointwiseBinary(Action<T, Vector<T>> f, T x, Vector<T> result)
         {

src/Numerics/Optimization/LineSearch/WeakWolfeLineSearch.cs

@@ -1,4 +1,4 @@
-// <copyright file="BfgsTest.cs" company="Math.NET">
+// <copyright file="WeakWolfeLineSearch.cs" company="Math.NET">
 // Math.NET Numerics, part of the Math.NET Project
 // http://numerics.mathdotnet.com
 // http://github.com/mathnet/mathnet-numerics
@@ -36,12 +36,12 @@ namespace MathNet.Numerics.Optimization.LineSearch
     /// <summary>
     /// Search for a step size alpha that satisfies the weak wolfe conditions. The weak Wolfe
     /// Conditions are
-    /// i)  Armijo Rule:         f(x_k + alpha_k p_k) <= f(x_k) + c1 alpha_k p_k^T g(x_k)
-    /// ii) Curvature Condition: p_k^T g(x_k + alpha_k p_k) >= c2 p_k^T g(x_k)
-    /// where g(x) is the gradient of f(x), 0 < c1 < c2 < 1.
-    ///
+    /// i)  Armijo Rule:         f(x_k + alpha_k p_k) &lt;= f(x_k) + c1 alpha_k p_k^T g(x_k)
+    /// ii) Curvature Condition: p_k^T g(x_k + alpha_k p_k) &gt;= c2 p_k^T g(x_k)
+    /// where g(x) is the gradient of f(x), 0 &lt; c1 &lt; c2 &lt; 1.
+    /// 
     /// Implementation is based on http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
-    ///
+    /// 
     /// references:
     /// http://en.wikipedia.org/wiki/Wolfe_conditions
     /// http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
@@ -54,7 +54,10 @@ namespace MathNet.Numerics.Optimization.LineSearch
             // Validation in base class
         }
 
-        protected override MinimizationResult.ExitCondition WolfeExitCondition { get { return MinimizationResult.ExitCondition.WeakWolfeCriteria; } }
+        protected override MinimizationResult.ExitCondition WolfeExitCondition
+        {
+            get { return MinimizationResult.ExitCondition.WeakWolfeCriteria; }
+        }
 
         protected override bool WolfeCondition(double stepDd, double initialDd)
         {

src/Numerics/Optimization/ObjectiveFunctions/ForwardDifferenceGradientObjectiveFunction.cs

@@ -19,66 +19,66 @@ namespace MathNet.Numerics.Optimization.ObjectiveFunctions
     public class ForwardDifferenceGradientObjectiveFunction : IObjectiveFunction
     {
         public IObjectiveFunction InnerObjectiveFunction { get; protected set; }
-        protected Vector<double> _lower_bound;
-        protected Vector<double> _upper_bound;
+        protected Vector<double> LowerBound { get; set; }
+        protected Vector<double> UpperBound { get; set; }
 
-        protected Vector<double> _point;
-        protected bool _value_evaluated = false;
-        protected bool _gradient_evaluated = false;
-        protected Vector<double> _gradient;
+        protected bool ValueEvaluated { get; set; } = false;
+        protected bool GradientEvaluated { get; set; } = false;
+        private Vector<double> _gradient;
+
+        public double MinimumIncrement { get; set; }
+        public double RelativeIncrement { get; set; }
 
-        public double MinimumIncrement;
-        public double RelativeIncrement;
-        
         public ForwardDifferenceGradientObjectiveFunction(IObjectiveFunction valueOnlyObj, Vector<double> lowerBound, Vector<double> upperBound, double relativeIncrement=1e-5, double minimumIncrement=1e-8)
         {
-            this.InnerObjectiveFunction = valueOnlyObj;
-            _lower_bound = lowerBound;
-            _upper_bound = upperBound;
-            _gradient = new LinearAlgebra.Double.DenseVector(_lower_bound.Count);
-            this.RelativeIncrement = relativeIncrement;
-            this.MinimumIncrement = minimumIncrement;
+            InnerObjectiveFunction = valueOnlyObj;
+            LowerBound = lowerBound;
+            UpperBound = upperBound;
+            _gradient = new LinearAlgebra.Double.DenseVector(LowerBound.Count);
+            RelativeIncrement = relativeIncrement;
+            MinimumIncrement = minimumIncrement;
         }
 
         protected void EvaluateValue()
         {
-            _value_evaluated = true;
+            ValueEvaluated = true;
         }
 
         protected void EvaluateGradient()
         {
-            if (!_value_evaluated)
-                this.EvaluateValue();
+            if (!ValueEvaluated)
+                EvaluateValue();
 
-            var tmp_point = _point.Clone();
-            var tmp_obj = this.InnerObjectiveFunction.CreateNew();
+            var tmp_point = Point.Clone();
+            var tmp_obj = InnerObjectiveFunction.CreateNew();
             for (int ii = 0; ii < _gradient.Count; ++ii)
             {
                 var orig_point = tmp_point[ii];
-                var rel_incr = orig_point * this.RelativeIncrement;
-                var h = Math.Max(rel_incr, this.MinimumIncrement);
+                var rel_incr = orig_point * RelativeIncrement;
+                var h = Math.Max(rel_incr, MinimumIncrement);
                 var mult = 1;
-                if (orig_point + h > _upper_bound[ii])
+                if (orig_point + h > UpperBound[ii])
                     mult = -1;
 
                 tmp_point[ii] = orig_point + mult*h;
                 tmp_obj.EvaluateAt(tmp_point);
                 double bumped_value = tmp_obj.Value;
-                _gradient[ii] = (mult * bumped_value - mult * this.InnerObjectiveFunction.Value) / h;
+                _gradient[ii] = (mult * bumped_value - mult * InnerObjectiveFunction.Value) / h;
 
                 tmp_point[ii] = orig_point;
             }
-            _gradient_evaluated = true;
+            GradientEvaluated = true;
         }
 
         public Vector<double> Gradient
         {
             get
             {
-                if (!_gradient_evaluated)
-                    this.EvaluateGradient();
+                if (!GradientEvaluated)
+                    EvaluateGradient();
                 return _gradient;
             }
+            protected set { _gradient = value; }
         }
 
         public Matrix<double> Hessian
@@ -105,47 +105,41 @@ namespace MathNet.Numerics.Optimization.ObjectiveFunctions
             }
         }
 
-        public Vector<double> Point
-        {
-            get
-            {
-                return _point;
-            }
-        }
+        public Vector<double> Point { get; protected set; }
 
         public double Value
         {
             get
             {
-                if (!_value_evaluated)
-                    this.EvaluateValue();
+                if (!ValueEvaluated)
+                    EvaluateValue();
                 return this.InnerObjectiveFunction.Value;
             }
         }
 
         public IObjectiveFunction CreateNew()
         {
-            var tmp = new ForwardDifferenceGradientObjectiveFunction(this.InnerObjectiveFunction.CreateNew(), _lower_bound, _upper_bound, this.RelativeIncrement, this.MinimumIncrement);
+            var tmp = new ForwardDifferenceGradientObjectiveFunction(this.InnerObjectiveFunction.CreateNew(), LowerBound, UpperBound, this.RelativeIncrement, this.MinimumIncrement);
             return tmp;
         }
 
         public void EvaluateAt(Vector<double> point)
         {
-            _point = point;
-            _value_evaluated = false;
-            _gradient_evaluated = false;
-            this.InnerObjectiveFunction.EvaluateAt(point);
+            Point = point;
+            ValueEvaluated = false;
+            GradientEvaluated = false;
+            InnerObjectiveFunction.EvaluateAt(point);
         }
 
         public IObjectiveFunction Fork()
         {
-            var tmp = new ForwardDifferenceGradientObjectiveFunction(this.InnerObjectiveFunction.Fork(), _lower_bound, _upper_bound, this.RelativeIncrement, this.MinimumIncrement);
-            tmp._point = _point?.Clone();
-            tmp._gradient_evaluated = _gradient_evaluated;
-            tmp._value_evaluated = _value_evaluated;
-            tmp._gradient = _gradient?.Clone();
-
-            return tmp;
+            return new ForwardDifferenceGradientObjectiveFunction(this.InnerObjectiveFunction.Fork(), LowerBound, UpperBound, this.RelativeIncrement, this.MinimumIncrement)
+            {
+                Point = Point?.Clone(),
+                GradientEvaluated = GradientEvaluated,
+                ValueEvaluated = ValueEvaluated,
+                _gradient = _gradient?.Clone()
+            };
         }
     }
 }

src/Numerics/Optimization/ObjectiveFunctions/LazyObjectiveFunction.cs

@@ -1,4 +1,34 @@
-using System;
+// <copyright file="LazyObjectiveFunction.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+//
+// Copyright (c) 2009-2016 Math.NET
+//
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+//
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+using System;
 using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization.ObjectiveFunctions

src/Numerics/Optimization/ObjectiveFunctions/LazyObjectiveFunctionBase.cs

@@ -1,4 +1,34 @@
-using MathNet.Numerics.LinearAlgebra;
+// <copyright file="LazyObjectiveFunctionBase.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+//
+// Copyright (c) 2009-2016 Math.NET
+//
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+//
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization.ObjectiveFunctions
 {
@@ -6,14 +36,14 @@ namespace MathNet.Numerics.Optimization.ObjectiveFunctions
     {
         Vector<double> _point;
 
-        protected bool _hasFunctionValue;
-        protected double _functionValue;
+        protected bool HasFunctionValue { get; set; }
+        protected double FunctionValue { get; set; }
 
-        protected bool _hasGradientValue;
-        protected Vector<double> _gradientValue;
+        protected bool HasGradientValue { get; set; }
+        protected Vector<double> GradientValue { get; set; }
 
-        protected bool _hasHessianValue;
-        protected Matrix<double> _hessianValue;
+        protected bool HasHessianValue { get; set; }
+        protected Matrix<double> HessianValue { get; set; }
 
         protected LazyObjectiveFunctionBase(bool gradientSupported, bool hessianSupported)
         {
@@ -27,13 +57,13 @@ namespace MathNet.Numerics.Optimization.ObjectiveFunctions
         {
             // we need to deep-clone values since they may be updated inplace on evaluation
             LazyObjectiveFunctionBase fork = (LazyObjectiveFunctionBase)CreateNew();
-            fork._point = _point == null ? null : _point.Clone();
-            fork._hasFunctionValue = _hasFunctionValue;
-            fork._functionValue = _functionValue;
-            fork._hasGradientValue = _hasGradientValue;
-            fork._gradientValue = _gradientValue == null ? null : _gradientValue.Clone(); ;
-            fork._hasHessianValue = _hasHessianValue;
-            fork._hessianValue = _hessianValue == null ? null : _hessianValue.Clone();
+            fork._point = _point?.Clone();
+            fork.HasFunctionValue = HasFunctionValue;
+            fork.FunctionValue = FunctionValue;
+            fork.HasGradientValue = HasGradientValue;
+            fork.GradientValue = GradientValue?.Clone();
+            fork.HasHessianValue = HasHessianValue;
+            fork.HessianValue = HessianValue?.Clone();
             return fork;
         }
 
@@ -43,9 +73,9 @@ namespace MathNet.Numerics.Optimization.ObjectiveFunctions
         public void EvaluateAt(Vector<double> point)
         {
             _point = point;
-            _hasFunctionValue = false;
-            _hasGradientValue = false;
-            _hasHessianValue = false;
+            HasFunctionValue = false;
+            HasGradientValue = false;
+            HasHessianValue = false;
         }
 
         protected abstract void EvaluateValue();
@@ -69,16 +99,16 @@ namespace MathNet.Numerics.Optimization.ObjectiveFunctions
         {
             get
             {
-                if (!_hasFunctionValue)
+                if (!HasFunctionValue)
                 {
                     EvaluateValue();
                 }
-                return _functionValue;
+                return FunctionValue;
             }
             protected set
             {
-                _functionValue = value;
-                _hasFunctionValue = true;
+                FunctionValue = value;
+                HasFunctionValue = true;
             }
         }
 
@@ -86,16 +116,16 @@ namespace MathNet.Numerics.Optimization.ObjectiveFunctions
         {
             get
             {
-                if (!_hasGradientValue)
+                if (!HasGradientValue)
                 {
                     EvaluateGradient();
                 }
-                return _gradientValue;
+                return GradientValue;
             }
             protected set
             {
-                _gradientValue = value;
-                _hasGradientValue = true;
+                GradientValue = value;
+                HasGradientValue = true;
             }
         }
 
@@ -103,16 +133,16 @@ namespace MathNet.Numerics.Optimization.ObjectiveFunctions
         {
             get
             {
-                if (!_hasHessianValue)
+                if (!HasHessianValue)
                 {
                     EvaluateHessian();
                 }
-                return _hessianValue;
+                return HessianValue;
             }
             protected set
             {
-                _hessianValue = value;
-                _hasHessianValue = true;
+                HessianValue = value;
+                HasHessianValue = true;
             }
         }
     }

src/UnitTests/OptimizationTests/TestFunctionAdapters.cs

@@ -35,9 +35,9 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         {
             if (this.IsGradientSupported)
             {
-                if (this._gradientValue == null)
+                if (this.GradientValue == null)
                     this.Gradient = new DenseVector(this.TestFunction.ParameterDimension);
-                this.TestFunction.SsqGradientByRef(this.Point, _gradientValue);
+                this.TestFunction.SsqGradientByRef(this.Point, GradientValue);
             }
         }
 
@@ -45,9 +45,9 @@ namespace MathNet.Numerics.UnitTests.OptimizationTests
         {
             if (this.IsHessianSupported)
             {
-                if (this._hessianValue == null)
+                if (this.HessianValue == null)
                     this.Hessian = new DenseMatrix(this.TestFunction.ParameterDimension, this.TestFunction.ParameterDimension);
-                this.TestFunction.SsqHessianByRef(this.Point, _hessianValue);
+                this.TestFunction.SsqHessianByRef(this.Point, HessianValue);
             }
         }
     }