Remove the need for RandomHelper.rnd

PatrickShaw · PatrickShaw · commit f866e5cc4394 · 2017-06-08T17:12:49.000+10:00
Was not utelising following dependency injection. Instead, classes were depending on a static Random variable. Removed the need for this dependency.
diff --git a/GeNeural/Activations/LogisticActivator.cs b/GeNeural/Activations/LogisticActivator.cs
@@ -11,7 +11,8 @@ public double ActivationFunction(double x) {
         }
 
         public double GetThresholdThatResultsInZeroOutput() {
-            return 6; // Not really but close enough to zero
+            // Not really but close enough to zero
+            return 6;
         }
 
         public double[] GetInactiveNeuronWeights(int weightCount) {
diff --git a/GeNeural/GeNeural.csproj b/GeNeural/GeNeural.csproj
@@ -37,19 +37,19 @@
     <Compile Include="Activations\IActivator.cs" />
     <Compile Include="Activations\LogisticActivator.cs" />
     <Compile Include="Training\Backpropagation\StandardBackpropagationTrainer.cs" />
-    <Compile Include="GeneticNeuralNetworkFacilitator.cs" />
-    <Compile Include="Genetic\DisimilarityFunctions.cs" />
-    <Compile Include="Genetic\EfficiencyErrorFunction.cs" />
-    <Compile Include="Genetic\GeneTrainer.cs" />
-    <Compile Include="Genetic\NeuralNetworkGeneTrainer.cs" />
-    <Compile Include="Genetic\NewGenerationFunctions.cs" />
-    <Compile Include="Genetic\OutputAccuracyErrorFunctions.cs" />
-    <Compile Include="Genetic\PartnerSelectionFunction.cs" />
-    <Compile Include="Genetic\ReproductionFunctions.cs" />
-    <Compile Include="Genetic\Trainers.cs" />
+    <Compile Include="Genetics\GeneticNeuralNetworkFacilitator.cs" />
+    <Compile Include="Genetics\DisimilarityFunctions.cs" />
+    <Compile Include="Genetics\EfficiencyErrorFunction.cs" />
+    <Compile Include="Genetics\GeneTrainer.cs" />
+    <Compile Include="Genetics\NeuralNetworkGeneTrainer.cs" />
+    <Compile Include="Genetics\NewGenerationFunctions.cs" />
+    <Compile Include="Genetics\OutputAccuracyErrorFunctions.cs" />
+    <Compile Include="Genetics\PartnerSelectionFunction.cs" />
+    <Compile Include="Genetics\ReproductionFunctions.cs" />
+    <Compile Include="Genetics\Trainers.cs" />
     <Compile Include="IDeepCloneable.cs" />
-    <Compile Include="IGenetic.cs" />
-    <Compile Include="IMutatable.cs" />
+    <Compile Include="Genetics\IGenetic.cs" />
+    <Compile Include="Genetics\IMutatable.cs" />
     <Compile Include="INeuralNetwork.cs" />
     <Compile Include="Training\ISupervisedTrainer.cs" />
     <Compile Include="NeuralNetwork.cs" />
diff --git a/GeNeural/Genetics/EfficiencyErrorFunction.cs b/GeNeural/Genetics/EfficiencyErrorFunction.cs
@@ -4,7 +4,7 @@
 using System.Text;
 using System.Threading.Tasks;
 
-namespace GeNeural.Genetic {
+namespace GeNeural.Genetics {
     public static partial class Preset {
         public static class EfficiencyError {
             public static double Ignore(long ticks) {
diff --git a/GeNeural/Genetics/GeneTrainer.cs b/GeNeural/Genetics/GeneTrainer.cs
@@ -5,7 +5,7 @@
 using System.Text;
 using System.Threading.Tasks;
 
-namespace GeNeural.Genetic {
+namespace GeNeural.Genetics {
     public delegate T ReproductionFunction<T>(T parent1, T parent2);
     public delegate double AttributeDisimilarityFunction(double value1, double value2);
     public delegate double GeneticDisimilarityFunction<T>(
diff --git a/GeNeural/Genetics/GeneticNeuralNetworkFacilitator.cs b/GeNeural/Genetics/GeneticNeuralNetworkFacilitator.cs
@@ -4,7 +4,7 @@
 using System.Linq;
 using System.Text;
 using System.Threading.Tasks;
-namespace GeNeural {
+namespace GeNeural.Genetics {
     public class GeneticNeuralNetworkFacilitator : IMutatable, IDeepCloneable<GeneticNeuralNetworkFacilitator> {
         private const double VARIANCE_FACTOR = 0.01;
 
@@ -17,8 +17,10 @@ public class GeneticNeuralNetworkFacilitator : IMutatable, IDeepCloneable<Geneti
         private double neuronMutationFactorVarianceFactor = 0.01;
         private double neuronMutationFactor = 0.50; // Adds round(-x to x) neurons per layer
         private NeuralNetwork network;
-        public GeneticNeuralNetworkFacilitator(NeuralNetwork network) {
+        private Random rnd;
+        public GeneticNeuralNetworkFacilitator(NeuralNetwork network, Random random) {
             this.network = network;
+            this.rnd = random;
         }
         protected GeneticNeuralNetworkFacilitator(GeneticNeuralNetworkFacilitator parent) {
             network = parent.network.DeepClone();
@@ -76,38 +78,38 @@ public void Mutate(double mutationFactor = 1) {
         public void MutateHiddenLayerCount() {
             int numberOfLayersToClone = GetRandomCount(layerMutationFactor);
             //Debug.WriteLine("Creating {0} more layers.", numberOfLayersToClone);
-            if (RandomHelper.rnd.Next(0, 2) == 1) {
+            if (this.rnd.Next(0, 2) == 1) {
                 for (int _ = 0; _ < numberOfLayersToClone; _++) {
-                    int layerIndex = RandomHelper.rnd.Next(0, network.LayerCount - 1);
+                    int layerIndex = this.rnd.Next(0, network.LayerCount - 1);
                     network.InsertAfterLayer(layerIndex);
                 }
             } else {
                 for (int _ = 0; _ < numberOfLayersToClone; _++) {
                     if (network.LayerCount <= 1) { break; }
-                    int layerIndex = RandomHelper.rnd.Next(0, network.LayerCount - 1);
+                    int layerIndex = this.rnd.Next(0, network.LayerCount - 1);
                     network.RemoveLayer(layerIndex);
                 }
             }
         }
         public void MutateHiddenNeuronCount() {
             int numberOfNeuronsToClone = GetRandomCount(neuronMutationFactor);
             //Debug.WriteLine("Creating {0} more neurons", numberOfNeuronsToClone);
-            if (RandomHelper.rnd.Next(0, 2) == 1) {
+            if (this.rnd.Next(0, 2) == 1) {
                 for (int _ = 0; _ < numberOfNeuronsToClone; _++) {
                     if (network.LayerCount <= 1) {
                         break;
                     }
-                    int layerIndex = RandomHelper.rnd.Next(0, network.LayerCount - 1);
+                    int layerIndex = this.rnd.Next(0, network.LayerCount - 1);
                     //Debug.WriteLine("New neuron at layer: {0}", layerIndex);
-                    int neuronIndex = RandomHelper.rnd.Next(0, network.GetLayer(layerIndex).Length);
+                    int neuronIndex = this.rnd.Next(0, network.GetLayer(layerIndex).Length);
                     network.SplitNeuronNonDestructive(layerIndex, neuronIndex);
                 }
             } else {
                 for (int _ = 0; _ < numberOfNeuronsToClone; _++) {
                     if (network.LayerCount <= 1) { break; }
-                    int layerIndex = RandomHelper.rnd.Next(0, network.LayerCount - 1);
+                    int layerIndex = this.rnd.Next(0, network.LayerCount - 1);
                     //Debug.WriteLine("New neuron at layer: {0}", layerIndex);
-                    int neuronIndex = RandomHelper.rnd.Next(0, network.GetLayer(layerIndex).Length);
+                    int neuronIndex = this.rnd.Next(0, network.GetLayer(layerIndex).Length);
                     network.RemoveNeuron(layerIndex, neuronIndex);
                 }
             }
@@ -122,22 +124,20 @@ public void MutateWeights() {
                         double delta = GetDeltaMutatableValue(weightMutationFactor);
                         weight += delta;
                         //Debug.WriteLine("Changing weight by: {0}", delta);
-                        //weight += 0.05 + 0.1 * RandomHelper.rnd.NextDouble();//delta;
-                        //weight += RandomHelper.rnd.NextDouble();
                         neuron.SetWeight(w, weight);
                     }
                 }
             }
         }
         public double GetMultiplicativeMutableFactor(double mutableFactor) {
-            return 1 + (mutableFactor - RandomHelper.rnd.NextDouble() * mutableFactor * 2.0);
+            return 1 + (mutableFactor - this.rnd.NextDouble() * mutableFactor * 2.0);
         }
         public int GetRandomCount(double mutationFactor) {
-            return (int)Math.Round(RandomHelper.rnd.NextDouble() * mutationFactor);
+            return (int)Math.Round(this.rnd.NextDouble() * mutationFactor);
         }
         public double GetDeltaMutatableValue(double mutationFactor) {
-            double delta = RandomHelper.rnd.NextDouble() * mutationFactor;
-            if (RandomHelper.rnd.Next(0, 2) == 1)
+            double delta = this.rnd.NextDouble() * mutationFactor;
+            if (this.rnd.Next(0, 2) == 1)
                 return delta;
             else
                 return -delta;
diff --git a/GeNeural/Genetics/IGenetic.cs b/GeNeural/Genetics/IGenetic.cs
@@ -6,7 +6,7 @@
 using System.Text;
 using System.Threading.Tasks;
 
-namespace GeNeural {
+namespace GeNeural.Genetics {
     public interface IGeneticVarianceFunction {
         double GetGeneticVariance(double value1, double value2);
     }
diff --git a/GeNeural/Genetics/IMutatable.cs b/GeNeural/Genetics/IMutatable.cs
@@ -4,7 +4,7 @@
 using System.Text;
 using System.Threading.Tasks;
 
-namespace GeNeural {
+namespace GeNeural.Genetics {
     public interface IMutatable {
         void Mutate(double mutationFactor = 1);
     }
diff --git a/GeNeural/Genetics/NeuralNetworkGeneTrainer.cs b/GeNeural/Genetics/NeuralNetworkGeneTrainer.cs
@@ -5,7 +5,7 @@
 using System.Text;
 using System.Threading.Tasks;
 
-namespace GeNeural.Genetic {
+namespace GeNeural.Genetics {
     public class NeuralNetworkGeneTrainer : GeneTrainer<GeneticNeuralNetworkFacilitator, NeuralNetwork> {
         public NeuralNetworkGeneTrainer(GeneticNeuralNetworkFacilitator[] initialPopulation, ReproductionFunction<GeneticNeuralNetworkFacilitator> reproductionFunction, GeneticDisimilarityFunction<GeneticNeuralNetworkFacilitator> geneticDisimilarityFunction, AttributeDisimilarityFunction attributeDisimilarityFunction, ReproduceNewGeneration<GeneticNeuralNetworkFacilitator> newGenerationFunction, OutputAccuracyErrorFunction getOutputAccuracyError, SelectPartnerFunction<GeneticNeuralNetworkFacilitator> selectPartnerFunction, EfficiencyErrorFunction efficiencyErrorFunction)
             : base(initialPopulation, reproductionFunction, geneticDisimilarityFunction, attributeDisimilarityFunction, newGenerationFunction, getOutputAccuracyError, selectPartnerFunction, efficiencyErrorFunction) { }
@@ -30,7 +30,7 @@ public override double[] UnfitnessOfPopulation(double[][] inputs, double[][] des
                 unfitnessOfPopulation[p] = unfitness;
                 averageUnfitness += unfitness;
             }
-            averageUnfitness /= (double)population.Length;
+            averageUnfitness /= population.Length;
             Debug.WriteLine(averageUnfitness);
             return unfitnessOfPopulation;
         }
diff --git a/GeNeural/Genetics/NewGenerationFunctions.cs b/GeNeural/Genetics/NewGenerationFunctions.cs
@@ -5,11 +5,12 @@
 using System.Text;
 using System.Threading.Tasks;
 
-namespace GeNeural.Genetic {
+namespace GeNeural.Genetics {
     public static partial class Preset {
         public static class Generation {
             public static T[] SimpleProbabalisticNewGeneration<T>
                 (
+                    Random rnd,
                     T[] oldGeneration,
                     double[] unfitnessOfPopulation,
                     int newPopulationCount,
@@ -26,7 +27,7 @@ AttributeDisimilarityFunction attributeDisimilarityFunction
                 int i = 0;
                 // NOTE: When can definately optimize this algoirthm as it is effectively a recurrence equation
                 while (count < newPopulationCount) {
-                    if (RandomHelper.rnd.NextDouble() < 1.0 / (double)newPopulationCount) {
+                    if (rnd.NextDouble() < 1.0 / newPopulationCount) {
                         T chosenOne = oldGeneration[i];
                         double[] geneticDifference = new double[oldGeneration.Length];
                         for (int p = 0; p < oldGeneration.Length; p++) {
@@ -69,7 +70,7 @@ AttributeDisimilarityFunction attributeDisimilarityFunction
                 }
                 T chosenOne = oldGeneration[fittestIndex];
                 Debug.WriteLine("Fittest index: {0} | {1}", fittestIndex, unfitnessOfPopulation[fittestIndex]);
-                Debug.WriteLine("Unfitness index: {0} | {1}", unfittestIndex, unfitnessOfPopulation[unfittestIndex]);
+                Debug.WriteLine("Unfittest index: {0} | {1}", unfittestIndex, unfitnessOfPopulation[unfittestIndex]);
                 double[] geneticDifference = new double[oldGeneration.Length];
                 for (int p = 0; p < oldGeneration.Length; p++) {
                     geneticDifference[p] = geneticDisimilarityFunction(oldGeneration[p], chosenOne, attributeDisimilarityFunction);
diff --git a/GeNeural/Genetics/OutputAccuracyErrorFunctions.cs b/GeNeural/Genetics/OutputAccuracyErrorFunctions.cs
@@ -4,7 +4,7 @@
 using System.Text;
 using System.Threading.Tasks;
 
-namespace GeNeural.Genetic {
+namespace GeNeural.Genetics {
     public static partial class Preset {
         public static class AccuracyError {
             /// <summary>
diff --git a/GeNeural/Genetics/PartnerSelectionFunction.cs b/GeNeural/Genetics/PartnerSelectionFunction.cs
@@ -4,25 +4,25 @@
 using System.Text;
 using System.Threading.Tasks;
 
-namespace GeNeural.Genetic {
+namespace GeNeural.Genetics {
     public static partial class Preset {
         public static class PartnerSelection {
             /// <summary>
             /// Mainly used for quick test purposes.
             /// Literally going to select a random entity from the population to reproduce with. Yeah...
             /// </summary>
-            public static T RandomPartnerSelection<T>(T[] population, double[] fitness, double[] geneticDifference) {
-                int mateIndex = RandomHelper.rnd.Next(0, population.Length);
+            public static T RandomPartnerSelection<T>(Random rnd, T[] population, double[] fitness, double[] geneticDifference) {
+                int mateIndex = rnd.Next(0, population.Length);
                 return population[mateIndex];
             }
-            public static T Probabalistic<T>(T[] population, double[] fitness, double[] geneticDifference) {
+            public static T Probabalistic<T>(Random rnd, T[] population, double[] fitness, double[] geneticDifference) {
                 double[] attaction = new double[population.Length];
                 for (int i = 0; i < geneticDifference.Length; i++) {
                     attaction[i] = fitness[i] * (1.0 / (geneticDifference[i] + 1));
                 }
                 Sorter.QuickSort(geneticDifference, attaction);
                 for (int p = 0; true; p = (1 + p) % population.Length) {
-                    if (RandomHelper.rnd.NextDouble() < 1 / (double)population.Length) {
+                    if (rnd.NextDouble() < 1 / (double)population.Length) {
                         return population[p];
                     }
                 }
diff --git a/GeNeural/Genetics/ReproductionFunctions.cs b/GeNeural/Genetics/ReproductionFunctions.cs
@@ -3,7 +3,7 @@
 using System.Linq;
 using System.Text;
 using System.Threading.Tasks;
-namespace GeNeural.Genetic {
+namespace GeNeural.Genetics {
     public static partial class Preset {
         public static class Reproduction {
             public static T AlwaysCloneFirstEntity<T>(T network1, T network2) where T : IDeepCloneable<T> {
@@ -12,14 +12,14 @@ public static T AlwaysCloneFirstEntity<T>(T network1, T network2) where T : IDee
             /// <summary>
             /// Really just a testing function. 50% chance to clone network1, 50% chance to clone network2.
             /// </summary>
-            public static T CloningCoinToss<T>(T network1, T network2) where T : IDeepCloneable<T> {
-                return PickAttributeCoinToss(network1.DeepClone(), network2.DeepClone());
+            public static T CloningCoinToss<T>(Random random, T network1, T network2) where T : IDeepCloneable<T> {
+                return PickAttributeCoinToss(random, network1.DeepClone(), network2.DeepClone());
             }
-            public static GeneticNeuralNetworkFacilitator DefaultReproduction(GeneticNeuralNetworkFacilitator network1, GeneticNeuralNetworkFacilitator network2) {
+            public static GeneticNeuralNetworkFacilitator DefaultReproduction(Random random, GeneticNeuralNetworkFacilitator network1, GeneticNeuralNetworkFacilitator network2) {
                 // Pick a parent to clone
                 GeneticNeuralNetworkFacilitator chosenClonerParent;
                 GeneticNeuralNetworkFacilitator mergerParent;
-                if (RandomHelper.rnd.Next(0, 2) == 1) {
+                if (random.Next(0, 2) == 1) {
                     chosenClonerParent = network1;
                     mergerParent = network2;
                 } else {
@@ -28,14 +28,14 @@ public static GeneticNeuralNetworkFacilitator DefaultReproduction(GeneticNeuralN
                 }
                 GeneticNeuralNetworkFacilitator baby = chosenClonerParent.DeepClone();
 
-                baby.LayerMutationFactor = PickAttributeCoinToss(network1.LayerMutationFactor, network2.LayerMutationFactor);
-                baby.LayerMutationFactorVarianceFactor = PickAttributeCoinToss(network1.LayerMutationFactorVarianceFactor, network2.LayerMutationFactorVarianceFactor);
+                baby.LayerMutationFactor = PickAttributeCoinToss(random, network1.LayerMutationFactor, network2.LayerMutationFactor);
+                baby.LayerMutationFactorVarianceFactor = PickAttributeCoinToss(random, network1.LayerMutationFactorVarianceFactor, network2.LayerMutationFactorVarianceFactor);
 
-                baby.WeightMutationFactor = PickAttributeCoinToss(network1.WeightMutationFactor, network2.WeightMutationFactor);
-                baby.WeightMutationFactorVarianceFactor = PickAttributeCoinToss(network1.WeightMutationFactorVarianceFactor, network2.WeightMutationFactorVarianceFactor);
+                baby.WeightMutationFactor = PickAttributeCoinToss(random, network1.WeightMutationFactor, network2.WeightMutationFactor);
+                baby.WeightMutationFactorVarianceFactor = PickAttributeCoinToss(random, network1.WeightMutationFactorVarianceFactor, network2.WeightMutationFactorVarianceFactor);
 
-                baby.NeuronMutationFactor = PickAttributeCoinToss(network1.NeuronMutationFactor, network2.NeuronMutationFactor);
-                baby.NeuronMutationFactorVarianceFactor = PickAttributeCoinToss(network1.NeuronMutationFactorVarianceFactor, network2.NeuronMutationFactorVarianceFactor);
+                baby.NeuronMutationFactor = PickAttributeCoinToss(random, network1.NeuronMutationFactor, network2.NeuronMutationFactor);
+                baby.NeuronMutationFactorVarianceFactor = PickAttributeCoinToss(random, network1.NeuronMutationFactorVarianceFactor, network2.NeuronMutationFactorVarianceFactor);
 
                 NeuralNetwork babyNetwork = baby.Network;
                 NeuralNetwork mergingNetwork = mergerParent.Network;
@@ -60,8 +60,8 @@ public static GeneticNeuralNetworkFacilitator DefaultReproduction(GeneticNeuralN
                 }
                 return baby;
             }
-            private static T PickAttributeCoinToss<T>(T heads, T tails) {
-                return RandomHelper.rnd.Next(0, 2) == 1 ? heads : tails;
+            private static T PickAttributeCoinToss<T>(Random random, T heads, T tails) {
+                return random.Next(0, 2) == 1 ? heads : tails;
             }
         }
     }
diff --git a/GeNeural/Genetics/Trainers.cs b/GeNeural/Genetics/Trainers.cs
diff --git a/GeNeural/Helpers.cs b/GeNeural/Helpers.cs
diff --git a/GeNeural/NeuralNetwork.cs b/GeNeural/NeuralNetwork.cs

Original file line number	Diff line number	Diff line change
`@@ -11,7 +11,8 @@ public double ActivationFunction(double x) {`
`11`	`11`	`}`
`12`	`12`
`13`	`13`	`public double GetThresholdThatResultsInZeroOutput() {`
`14`		`- return 6; // Not really but close enough to zero`
	`14`	`+ // Not really but close enough to zero`
	`15`	`+ return 6;`
`15`	`16`	`}`
`16`	`17`
`17`	`18`	`public double[] GetInactiveNeuronWeights(int weightCount) {`
Original file line number	Diff line number	Diff line change
`@@ -6,7 +6,7 @@`
`6`	`6`	`using System.Text;`
`7`	`7`	`using System.Threading.Tasks;`
`8`	`8`
`9`		`-namespace GeNeural {`
	`9`	`+namespace GeNeural.Genetics {`
`10`	`10`	`public interface IGeneticVarianceFunction {`
`11`	`11`	`double GetGeneticVariance(double value1, double value2);`
`12`	`12`	`}`
Original file line number	Diff line number	Diff line change
`@@ -4,7 +4,7 @@`
`4`	`4`	`using System.Text;`
`5`	`5`	`using System.Threading.Tasks;`
`6`	`6`
`7`		`-namespace GeNeural {`
	`7`	`+namespace GeNeural.Genetics {`
`8`	`8`	`public interface IMutatable {`
`9`	`9`	`void Mutate(double mutationFactor = 1);`
`10`	`10`	`}`