simplified some Count() calls

Src/numl.Tests/CollectionTests/CollectionsTests.cs

@@ -137,7 +137,7 @@ public void NSortedList_Size_Test()
 
             var contains = (dict.Where(w => w.Value == 0 && sorted.Contains(w.Key)));
 
-            bool result = (contains.Count() == 0);
+            bool result = (!contains.Any());
 
             Assert.True(result, "Length was not equal to the elements in the collection");
 

Src/numl.Tests/DataTests/GraphTests.cs

@@ -167,8 +167,8 @@ public void RemoveVertexTest()
 
             g.RemoveVertex(vertex1);
 
-            Assert.Equal(0, g.GetVertices().Where(v => v.Id == 1).Count());
-            Assert.Equal(0, g.GetEdges().Where(e => e.ParentId == 1 || e.ChildId == 1).Count());
+            Assert.Equal(0, g.GetVertices().Count(v => v.Id == 1));
+            Assert.Equal(0, g.GetEdges().Count(e => e.ParentId == 1 || e.ChildId == 1));
         }
 
         [Fact]
@@ -190,7 +190,7 @@ public void RemoveEdgeTest()
 
             g.RemoveEdge(edge);
 
-            Assert.Equal(0, g.GetEdges().Where(e => e.ParentId == 1 && e.ChildId == 5).Count());
+            Assert.Equal(0, g.GetEdges().Count(e => e.ParentId == 1 && e.ChildId == 5));
 
         }
 

Src/numl/AI/Search/AdversarialSearch.cs

@@ -28,7 +28,7 @@ internal Node GetBestChildNode(Node node, double v)
                         .Where(n => n.Cost == v && n.State.IsTerminal);
 
             Node r;
-            if (q.Count() > 0) // favor terminal nodes first
+            if (q.Any()) // favor terminal nodes first
                 r = q.Random();
             else
                 r = node.Children

Src/numl/Learner.cs

@@ -141,7 +141,7 @@ private static LearningModel GenerateModel(IGenerator generator, Matrix x, Vecto
 
             Score score = new Score();
 
-            if (testingSlice.Count() > 0)
+            if (testingSlice.Any())
             {
                 // testing            
                 object[] test = GetTestExamples(testingSlice, examples);

Src/numl/Math/LinearAlgebra/MatrixStatics.cs

@@ -700,8 +700,8 @@ public static Matrix Slice(Matrix m, IEnumerable<int> indices, VectorType t)
 		{
 			var q = indices.Distinct();
 
-			int rows = t == VectorType.Row ? q.Where(j => j < m.Rows).Count() : m.Rows;
-			int cols = t == VectorType.Col ? q.Where(j => j < m.Cols).Count() : m.Cols;
+			int rows = t == VectorType.Row ? q.Count(j => j < m.Rows) : m.Rows;
+			int cols = t == VectorType.Col ? q.Count(j => j < m.Cols) : m.Cols;
 
 			Matrix n = new Matrix(rows, cols);
 

Src/numl/Math/LinearAlgebra/VectorExtensions.cs

@@ -160,8 +160,7 @@ public static Vector ToVector(this double[] array)
         /// <returns>e as a Matrix.</returns>
         public static Matrix ToMatrix(this IEnumerable<Vector> source, VectorType vectorType = VectorType.Row)
         {
-            var c = source.Count();
-            if (c == 0)
+            if (!source.Any())
                 throw new InvalidOperationException("Cannot create matrix from an empty set.");
 
             return Matrix.Stack(vectorType, source.ToArray());
@@ -333,7 +332,7 @@ group i by i into g
                     select new
                     {
                         key = g.Key,
-                        count = source.Where(d => d == g.Key).Count()
+                        count = source.Count(d => d == g.Key)
                     };
 
             double mode = -1;
@@ -801,7 +800,9 @@ public static bool IsNaN(this Vector vector)
         public static bool IsBinary(this Vector vector)
         {
             var v = vector.Distinct();
-            return ((v.Count() == 2 && (v.Contains(1d) && (v.Contains(0d) || v.Contains(-1d)))) || (v.Count() == 1 && (v.Contains(1d) || v.Contains(0d) || v.Contains(-1d))));
+            var count = v.Count();
+
+            return ((count == 2 && (v.Contains(1d) && (v.Contains(0d) || v.Contains(-1d)))) || (count == 1 && (v.Contains(1d) || v.Contains(0d) || v.Contains(-1d))));
         }
 
         /// <summary>

Src/numl/Model/Descriptor.cs

@@ -59,10 +59,7 @@ public Property this[string name]
 		{
 			get
 			{
-				if (Features.Where(p => p.Name == name).Count() == 1)
-					return Features.Where(p => p.Name == name).First();
-				else
-					return null;
+			    return Features.Count(p => p.Name == name) == 1 ? Features.First(p => p.Name == name) : null;
 			}
 		}
 		/// <summary>

Src/numl/Recommendation/CofiRecommenderGenerator.cs

@@ -82,7 +82,7 @@ public override IModel Generate(Matrix X, Vector y)
             // The mean needs to be values within rating range only.
             Vector mean = X.GetRows().Select(s => 
                                         s.Where(w => this.Ratings.Test(w)).Sum() / 
-                                        s.Where(w => this.Ratings.Test(w)).Count()
+                                        s.Count(w => this.Ratings.Test(w))
                                     ).ToVector();
 
             // update feature averages before preprocessing features.

Src/numl/Reinforcement/ReinforcementGenerator.cs

@@ -123,7 +123,7 @@ public virtual void Preprocess(Matrix X1, Vector y, Matrix X2, Vector r)
         /// <returns>IReinforcementModel.</returns>
         public IReinforcementModel Generate(Descriptor description, IEnumerable<object> examples)
         {
-            if (examples.Count() == 0) throw new InvalidOperationException("Empty example set.");
+            if (!examples.Any()) throw new InvalidOperationException("Empty example set.");
 
             Descriptor = description;
             if (Descriptor.Features == null || Descriptor.Features.Length == 0)
@@ -169,7 +169,7 @@ public IReinforcementModel Generate<T>(Descriptor descriptor, IEnumerable<T> exa
         /// <returns>IReinforcementModel.</returns>
         public IReinforcementModel Generate(IEnumerable<object> examples1, IEnumerable<object> examples2)
         {
-            if (examples1.Count() == 0) throw new InvalidOperationException("Empty example set.");
+            if (!examples1.Any()) throw new InvalidOperationException("Empty example set.");
 
             if (this.Descriptor == null)
                 throw new InvalidOperationException("Descriptor is null");
@@ -188,7 +188,7 @@ public IReinforcementModel Generate(IEnumerable<object> examples1, IEnumerable<o
         /// <returns>IReinforcementModel.</returns>
         public IReinforcementModel Generate(Descriptor descriptor, IEnumerable<object> examples1, Descriptor transitionDescriptor, IEnumerable<object> examples2)
         {
-            if (examples1.Count() == 0) throw new InvalidOperationException("Empty example set.");
+            if (!examples1.Any()) throw new InvalidOperationException("Empty example set.");
 
             bool hasTransitionStates = (examples2 != null && examples2.Any());
 

Src/numl/Supervised/Classification/MultiClassLearner.cs

@@ -79,7 +79,7 @@ private static Tuple<IClassifier, Score, object> GenerateModel(IGenerator genera
 
             Score score = new Score();
 
-            if (testingSlice.Count() > 0)
+            if (testingSlice.Any())
             {
                 object[] testExamples = examples.Slice(testingSlice).ToArray();
                 var testing = generator.Descriptor.Convert(testExamples, true).ToExamples();

Src/numl/Supervised/DecisionTree/DecisionTreeGenerator.cs

@@ -184,7 +184,7 @@ private Node BuildTree(Matrix x, Vector y, int depth, List<int> used, Tree tree)
                 // if this number is 0 then this edge 
                 // leads to a dead end - the edge will 
                 // not be built
-                if (slice.Count() > 0)
+                if (slice.Any())
                 {
                     Vector ySlice = y.Slice(slice);
                     // only one answer, set leaf

Src/numl/Supervised/Generator.cs

@@ -131,7 +131,7 @@ public virtual Matrix ToEncoded(Vector y)
         /// <returns>Model.</returns>
         public IModel Generate(IEnumerable<object> examples)
         {
-            if (examples.Count() == 0) throw new InvalidOperationException("Empty example set.");
+            if (!examples.Any()) throw new InvalidOperationException("Empty example set.");
 
             if (Descriptor == null)
                 throw new InvalidOperationException("Descriptor is null");
@@ -146,7 +146,7 @@ public IModel Generate(IEnumerable<object> examples)
         /// <returns>Model.</returns>
         public IModel Generate(Descriptor description, IEnumerable<object> examples)
         {
-            if (examples.Count() == 0) throw new InvalidOperationException("Empty example set.");
+            if (!examples.Any()) throw new InvalidOperationException("Empty example set.");
 
             Descriptor = description;
             if (Descriptor.Features == null || Descriptor.Features.Length == 0)

Src/numl/Supervised/NeuralNetwork/NetworkOps.cs

@@ -194,7 +194,8 @@ public static Network Create(this Network network, int inputLayer, int outputLay
         /// <returns></returns>
         public static Network AddConnections(this Network network, Neuron node, IEnumerable<Neuron> parentNodes, IEnumerable<Neuron> childNodes, double epsilon = double.NaN)
         {
-            if (epsilon == double.NaN)
+            //episilon == double.Nan will be always false, even if epsilon is Nan
+            if (double.IsNaN(epsilon))
                 epsilon = Edge.GetEpsilon(node.ActivationFunction.Minimum, node.ActivationFunction.Maximum, parentNodes.Count(), childNodes.Count());
 
             if (parentNodes != null)
@@ -257,7 +258,7 @@ public static Vector GetBiases(this Network network, int layer = 0)
 
             var nodes = network.GetNodes(layer + 1).ToArray();
 
-            Vector biases = Vector.Zeros(nodes.Where(w => !w.IsBias).Count());
+            Vector biases = Vector.Zeros(nodes.Count(w => !w.IsBias));
 
             for (int i = 0; i < nodes.Length; i++)
             {

Src/numl/Supervised/Score.cs

@@ -318,8 +318,8 @@ public static Score ScorePredictions(Vector predictions, Vector actual,
         {
             var score = new numl.Supervised.Score()
             {
-                TotalPositives = actual.Where(w => w == truthLabel).Count(),
-                TotalNegatives = actual.Where(w => (w == falseLabel || w != truthLabel)).Count(),
+                TotalPositives = actual.Count(w => w == truthLabel),
+                TotalNegatives = actual.Count(w => (w == falseLabel || w != truthLabel)),
 
                 TruePositives = actual.Where((i, idx) => i == truthLabel && i == predictions[idx]).Count(),
                 FalsePositives = actual.Where((i, idx) => (i == falseLabel || i != truthLabel) && predictions[idx] == truthLabel).Count(),