Fix issue 105 (partial)

src/main/java/org/encog/neural/flat/FlatNetwork.java

@@ -25,6 +25,7 @@
 
 import java.io.Serializable;
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
 import org.encog.Encog;
@@ -289,19 +290,17 @@ public final void clearContext() {
 			final boolean hasBias = (this.layerContextCount[i] + this.layerFeedCounts[i]) != this.layerCounts[i];
 
 			// fill in regular neurons
-			for (int j = 0; j < this.layerFeedCounts[i]; j++) {
-				this.layerOutput[index++] = 0;
-			}
+			Arrays.fill(this.layerOutput, index, this.layerFeedCounts[i], 0);
+			index += this.layerFeedCounts[i];
 
 			// fill in the bias
 			if (hasBias) {
 				this.layerOutput[index++] = this.biasActivation[i];
 			}
 
 			// fill in context
-			for (int j = 0; j < this.layerContextCount[i]; j++) {
-				this.layerOutput[index++] = 0;
-			}
+			Arrays.fill(this.layerOutput, index, this.layerContextCount[i], 0);
+			index += this.layerContextCount[i];
 		}
 	}
 
@@ -372,9 +371,8 @@ public void compute(final double[] input, final double[] output) {
 		// update context values
 		final int offset = this.contextTargetOffset[0];
 
-		for (int x = 0; x < this.contextTargetSize[0]; x++) {
-			this.layerOutput[offset + x] = this.layerOutput[x];
-		}
+		EngineArray.arrayCopy(this.layerOutput, 0, layerOutput,
+				offset, this.contextTargetSize[0]);
 
 		EngineArray.arrayCopy(this.layerOutput, 0, output, 0, this.outputCount);
 	}
@@ -413,9 +411,8 @@ protected void computeLayer(final int currentLayer) {
 		// update context values
 		final int offset = this.contextTargetOffset[currentLayer];
 
-		for (int x = 0; x < this.contextTargetSize[currentLayer]; x++) {
-			this.layerOutput[offset + x] = this.layerOutput[outputIndex + x];
-		}
+		EngineArray.arrayCopy(this.layerOutput, outputIndex,
+				this.layerOutput, offset, this.contextTargetSize[currentLayer]);
 	}
 
 	/**
@@ -727,11 +724,7 @@ public final void randomize(final double hi, final double lo) {
 	 * @param af The activation functions.
 	 */
 	public final void setActivationFunctions(final ActivationFunction[] af) {
-
-		this.activationFunctions = new ActivationFunction[af.length];
-		for (int i = 0; i < af.length; i++) {
-			this.activationFunctions[i] = af[i];
-		}
+		this.activationFunctions = Arrays.copyOf(af, af.length);
 	}
 
 	/**

src/main/java/org/encog/neural/flat/FlatNetworkRBF.java

@@ -24,6 +24,7 @@
 package org.encog.neural.flat;
 
 import java.io.Serializable;
+import java.util.Arrays;
 
 import org.encog.engine.network.activation.ActivationLinear;
 import org.encog.mathutil.rbf.RadialBasisFunction;
@@ -117,10 +118,7 @@ public final void compute(final double[] x, final double[] output) {
 	 * @param rbf The RBF's used.
 	 */
 	public final void setRBF(final RadialBasisFunction[] rbf) {
-		this.rbf = new RadialBasisFunction[rbf.length];
-		for(int i=0;i<rbf.length;i++) {
-			this.rbf[i] = rbf[i];
-		}
+		this.rbf = Arrays.copyOf(rbf, rbf.length);
 	}
 
 	/**

src/main/java/org/encog/neural/networks/training/pnn/DeriveMinimum.java

@@ -170,9 +170,7 @@ public double calculate(final int maxIterations, final double maxError,
 					}
 					fbest = globalMinimum.getY2();
 				} else {
-					for (int i = 0; i < n; i++) {
-						x[i] = base[i];
-					}
+					System.arraycopy(base, 0, x, 0, n);
 				}
 				break;
 			}
@@ -254,8 +252,8 @@ private void findNewDir(final int n, final double gam, final double[] g,
 			final double[] h, final double[] grad) {
 		int i;
 
+		System.arraycopy(grad, 0, g, 0, n);
 		for (i = 0; i < n; i++) {
-			g[i] = grad[i];
 			grad[i] = h[i] = g[i] + gam * h[i];
 		}
 	}

src/main/java/org/encog/util/EngineArray.java

@@ -99,9 +99,7 @@ public static double[][] arrayCopy(final double[][] source) {
 		final double[][] result = new double[source.length][source[0].length];
 
 		for (int row = 0; row < source.length; row++) {
-			for (int col = 0; col < source[0].length; col++) {
-				result[row][col] = source[row][col];
-			}
+			System.arraycopy(source[row], 0, result[row], 0, source[0].length);
 		}
 
 		return result;