Fri Mar 31 16:00:00 GMT 2000

Test_2H

/** * Title: <p> * Description: <p> * Copyright: Copyright (c) <p> * Company: <p> * @author * @version 1.0 */ public class Test_2H { static float [] in1 = { -0.4f, -0.4f, +0.4f }; static float [] in2 = { -0.4f, +0.4f, -0.4f }; static float [] in3 = { +0.4f, -0.4f, -0.4f }; static float [] out1= { +0.4f, -0.4f, -0.4f}; static float [] out2= { -0.4f, -0.4f, +0.4f}; static float [] out3= { -0.4f, +0.4f, -0.4f}; static float [] test1 = { -0.2f, -0.45f, +0.35f }; static float [] test2 = { -0.33f, +0.41f, -0.38f }; static float [] test3 = { +0.33f, -0.41f, -0.23f }; public static void main(String[] args) { Neural_2H nn = new Neural_2H(3, 3, 3, 3); nn.addTrainingExample(in1, out1); nn.addTrainingExample(in2, out2); nn.addTrainingExample(in3, out3); for (int i=0; i<2002; i++) { float error = nn.train(); if ((i + 99) % 100 == 0) System.out.println("cycle " + i + " error is " + error); } test_recall(nn, test1); test_recall(nn, test2); test_recall(nn, test3); nn.save("test.neural"); Neural_2H nn2 = Neural_2H.Factory("test.neural"); nn2.addTrainingExample(in1, out1); nn2.addTrainingExample(in2, out2); nn2.addTrainingExample(in3, out3); for (int i=0; i<102; i++) { float error = nn2.train(); if ((i + 19) % 20 == 0) System.out.println("cycle " + i + " error is " + error); } test_recall(nn2, test1); test_recall(nn2, test2); test_recall(nn2, test3); } public static void test_recall(Neural_2H nn, float [] inputs) { float [] results = nn.recall(inputs); System.out.print("Test case: "); for (int i=0; i<inputs.length; i++) System.out.print(pp(inputs[i]) + " "); System.out.print(" results: "); for (int i=0; i<results.length; i++) System.out.print(pp(results[i]) + " "); System.out.println(); } public static String pp(float x) { String s = new String("" + x + "00"); int index = s.indexOf("."); if (index > -1) s = s.substring(0, index + 3); if (s.startsWith("-") == false) s = " " + s; return s; } }


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