a few style reversions

Author: davecom

CCSPiJ/src/chapter7/IrisTest.java

@@ -45,12 +45,15 @@ public IrisTest() {
 			// last item is species
 			String species = iris[4];
 			switch (species) {
-				case IRIS_SETOSA :
-					irisClassifications.add(new double[] { 1.0, 0.0, 0.0 }); break;
-				case IRIS_VERSICOLOR :
-					irisClassifications.add(new double[] { 0.0, 1.0, 0.0 }); break;
-				default :
-					irisClassifications.add(new double[] { 0.0, 0.0, 1.0 }); break;
+			case IRIS_SETOSA:
+				irisClassifications.add(new double[] { 1.0, 0.0, 0.0 });
+				break;
+			case IRIS_VERSICOLOR:
+				irisClassifications.add(new double[] { 0.0, 1.0, 0.0 });
+				break;
+			default:
+				irisClassifications.add(new double[] { 0.0, 0.0, 1.0 });
+				break;
 			}
 			irisSpecies.add(species);
 		}
@@ -61,11 +64,11 @@ public String irisInterpretOutput(double[] output) {
 		double max = Util.max(output);
 		if (max == output[0]) {
 			return IRIS_SETOSA;
-		}
-		if (max == output[1]) {
+		} else if (max == output[1]) {
 			return IRIS_VERSICOLOR;
+		} else {
+			return IRIS_VIRGINICA;
 		}
-		return IRIS_VIRGINICA;
 	}
 
 	public Network<String>.Results classify() {

CCSPiJ/src/chapter7/Network.java

@@ -46,7 +46,12 @@ public Network(int[] layerStructure, double learningRate,
 	// Pushes input data to the first layer, then output from the first
 	// as input to the second, second to the third, etc.
 	private double[] outputs(double[] input) {
-		return layers.stream().reduce(input, (r, l) -> l.outputs(r), (r1, r2) -> r1);
+		double[] result = input;
+		for (Layer layer : layers) {
+			result = layer.outputs(result);
+		}
+		return result;
+
 	}
 
 	// Figure out each neuron's changes based on the errors of the output

CCSPiJ/src/chapter7/WineTest.java

@@ -41,12 +41,16 @@ public WineTest() {
 			// first item is species
 			int species = Integer.parseInt(wine[0]);
 			switch (species) {
-				case 1 :
-					wineClassifications.add(new double[] { 1.0, 0.0, 0.0 }); break;
-				case 2 :
-					wineClassifications.add(new double[] { 0.0, 1.0, 0.0 }); break;
-				default :
-					wineClassifications.add(new double[] { 0.0, 0.0, 1.0 });; break;
+			case 1:
+				wineClassifications.add(new double[] { 1.0, 0.0, 0.0 });
+				break;
+			case 2:
+				wineClassifications.add(new double[] { 0.0, 1.0, 0.0 });
+				break;
+			default:
+				wineClassifications.add(new double[] { 0.0, 0.0, 1.0 });
+				;
+				break;
 			}
 			wineSpecies.add(species);
 		}
@@ -57,11 +61,11 @@ public Integer wineInterpretOutput(double[] output) {
 		double max = Util.max(output);
 		if (max == output[0]) {
 			return 1;
-		}
-		if (max == output[1]) {
+		} else if (max == output[1]) {
 			return 2;
+		} else {
+			return 3;
 		}
-		return 3;
 	}
 
 	public Network<Integer>.Results classify() {