Red neuronal para LeJOS MindStorm NXT

tag:blogger.com,1999:blog-7942320359574647252.post-59373701741103761032007-11-02T09:38:00.000-05:002007-11-02T09:49:00.213-05:00Red neuronal para LeJOS MindStorm NXTEste código es traducido de su original para el MindStorm RXC. Es un algoritmo de propagación hacia atras "Backpropagation Network". Nos tomamos el trabajo de traducirlo para el nuevo modelo. Disfrutenlo. El código y su explicación se encuentran en la siguiente dirección: http://www.javaworld.com/javaworld/jw-05-2005/jw-0516-lego.html import lejos.nxt.Button; import lejos.nxt.LCD; import lejos.nxt.Motor; import lejos.nxt.SensorConstants; import lejos.nxt.SensorPort; import lejos.nxt.Sound; public class AlgoritmoAprendizaje { public static LegoMindstormBP bpn = new LegoMindstormBP(); public static void main(String args[]) throws InterruptedException { int i, white; int inp[] = {0,0,0}; int out[] = {0,0}; Sound.beep(); LCD.drawString( "Train",10,10 ); // Train bpn 500 epochs, sit down and wait about 5 minutes! for(i=0;i<500;i++) { bpn.train(1); LCD.drawInt( bpn.trainedEpochs,0,10); } SensorPort.S1.setTypeAndMode(SensorConstants.TYPE_SWITCH, SensorConstants.MODE_BOOLEAN ); SensorPort.S2.setTypeAndMode(SensorConstants.TYPE_SWITCH, SensorConstants.MODE_RAW ); SensorPort.S3.setTypeAndMode(SensorConstants.TYPE_LIGHT_ACTIVE, SensorConstants.MODE_BOOLEAN ); Sound.twoBeeps(); SensorPort.S2.activate(); white = SensorPort.S2.readRawValue(); Motor.A.setPower(1); Motor.C.setPower(1); Sound.twoBeeps(); while( !Button.ENTER.isPressed() ) { LCD.drawInt(SensorPort.S2.readRawValue(),0,20 ); if( SensorPort.S1.readBooleanValue() ) inp[0] = 1; // Sensor 1 on else inp[0] = 0; // Sensor 1 off if( SensorPort.S2.readRawValue() > white + 50 ) inp[1] = 1; // Sensor 2 over black floor else inp[1] = 0; // Sensor 2 over white floor if( SensorPort.S3.readBooleanValue() ) inp[2] = 1; // Sensor 3 on else inp[2] = 0; // Sensor 3 off bpn.test( inp, out ); if( out[0] == 1 ) Motor.A.forward(); else Motor.A.backward(); if( out[1] == 1 ) Motor.C.forward(); else Motor.C.backward(); Thread.sleep( 500 ); } // while() SensorPort.S2.passivate(); Motor.A.stop(); Motor.C.stop(); Sound.beep(); } // main() } // class LMbpn class LegoMindstormBP { public static int data1[][] = {{0,0,0}, {1,1}}; public static int data2[][] = {{1,0,0}, {1,0}}; public static int data3[][] = {{0,0,1}, {0,1}}; public static int data4[][] = {{0,1,0}, {0,0}}; public static double input[] = {0,0,0,1}; public static double w1[][] = {{0,0,0}, {0,0,0}, {0,0,0}, {0,0,0}}; public static double hidden[] = {0,0,1}; public static double w2[][] = {{0,0}, {0,0}, {0,0}}; public static double output[] = {0,0}; public static double delta2[] = {0,0}; public static double delta1[] = {0,0,0}; public static int trainedEpochs = 0; LegoMindstormBP() { byte i, j; // Initialize weights randomly between 0.1 and 0.9 for(i=0; i< w1.length; i++) for(j=0; j< w1[i].length; j++) w1[i][j] = Math.random()*0.8+0.1; for(i=0; i< w2.length; i++) for(j=0; j< w2[i].length; j++) w2[i][j] = Math.random()*0.8+0.1; } public static void train(int e) { for(int i=0; i< e; i++) { // Call method learn with training data learn( data1[0], data1[1] ); learn( data2[0], data2[1] ); learn( data3[0], data3[1] ); learn( data4[0], data4[1] ); trainedEpochs++; } } public static void learn( int inp[], int out[] ) { int i, j; double sum, out_j; // Initialize input units for(i=0; i< inp.length; i++) input[i] = inp[i]; // Calculate hidden units for(j=0; j< hidden.length-1; j++) { sum = 0; for(i=0; i< input.length; i++) sum = sum + w1[i][j]*input[i]; hidden[j] = 1 / ( 1 + Math.exp(-sum)); } // Calculate output units for(j=0; j< output.length; j++) { sum = 0; for(i=0; i< hidden.length; i++) sum = sum + w2[i][j]*hidden[i]; output[j] = 1 / (1 + Math.exp(-sum)); } // Calculate delta2 errors for(j=0; j< output.length; j++) { if( out[j] == 0 ) out_j = 0.1; else if( out[j] == 1 ) out_j = 0.9; else out_j = out[j]; delta2[j] = output[j]*(1-output[j])*(out_j-output[j]); } // Calculate delta1 errors for(j=0; j< hidden.length; j++) { sum = 0; for(i=0; i< output.length; i++) sum = sum + delta2[i]*w2[j][i]; delta1[j] = hidden[j]*(1-hidden[j])*sum; } // Adjust weights w2 for(i=0; i< hidden.length; i++) for(j=0; j< output.length; j++) w2[i][j] = w2[i][j] + 0.35*delta2[j]*hidden[i]; // Adjust weights w1 for(i=0; i< input.length; i++) for(j=0; j< hidden.length; j++) w1[i][j] = w1[i][j] + 0.35*delta1[j]*input[i]; } public static void test(int inp[], int out[]) { int i, j; double sum; // Initialize input units for(i=0; i< inp.length; i++) input[i] = inp[i]; // Calculate hidden units for(j=0; j< hidden.length-1; j++) { sum = 0; for(i=0; i< input.length; i++) sum = sum + w1[i][j]*input[i]; hidden[j] = 1 / ( 1 + Math.exp(-sum)); } // Calculate output units for(j=0; j< output.length; j++) { sum = 0; for(i=0; i< hidden.length; i++) sum = sum + w2[i][j]*hidden[i]; output[j] = 1 / (1 + Math.exp(-sum)); } // Assign output to param out[] for(i=0; i< output.length; i++) if( output[i] >= 0.5 ) out[i] = 1; else out[i] = 0; } }Daniel Gómezhttp://www.blogger.com/profile/16651665560267217312noreply@blogger.com