package ann;public class Node implements java.io.Serializable,Cloneable {public double activation;public double threshold;public double weights[];public double detweightslast[];public double detthresholdlast;public double error;public int numOfweights;public double amultinum;public ArrayList arWeight=new ArrayList();public Node() {activation = 0;error = 0;threshold = 0;amultinum = 1;}public Node(int numOfweights0) {amultinum = 1;numOfweights = numOfweights0;weights = new double[numOfweights];detweightslast = new double[numOfweights];detthresholdlast = 0;error = 0;int i;for (i = 0; i < numOfweights; i++) {weights[i] = (2 * Math.random() – 1) * amultinum;detweightslast[i] = 0;}threshold = (2 * Math.random() – 1) * amultinum;}public Node(double act, double thr, int numOfweights0) {amultinum = 1;numOfweights = numOfweights0;activation = act;threshold = thr;weights = new double[numOfweights];detweightslast = new double[numOfweights];}public void setWeight(ArrayList weight){weights = new double[weight.size()];for(int i=0;i<weight.size();i++){weights[i] = ((Double)weight.get(i)).doubleValue();}}}package ann;import ann.*;public class Nnetwork implements java.io.Serializable,Cloneable{public int n_input;public int n_output;public int n_layer;public Layer hidelayer[];public Layer input, output;boolean iftrain[];public double LH=-10;public double LO=-10;public double desiredOutputs[];public double a=0.2;public int connecttype;public double total_error, total_error_one_circle_all;public double error_compared_to_tolerance;double total_error_one_circle[];public int trainnum;public Nnetwork() {}public Nnetwork(int inp, int hide[], int outp, int hidenum, int connecttype0) {connecttype = connecttype0;int i, j;n_input = inp;n_output = outp;total_error_one_circle = new double[outp];desiredOutputs = new double[outp];output = new Layer(n_output);for (i = 0; i < n_output; i++) {output.nodes[i] = new Node(0);}n_layer = hidenum;hidelayer = new Layer[n_layer];for (i = n_layer – 1; i >= 0; i–) {hidelayer[i] = new Layer(hide[i]);for (j = 0; j < hidelayer[i].N_NODES; j++) {if (i == n_layer – 1) {hidelayer[i].nodes[j] = new Node(outp);}else {hidelayer[i].nodes[j] = new Node(hidelayer[i + 1].N_NODES);}}}input = new Layer(n_input);for (i = 0; i < n_input; i++) {input.nodes[i] = new Node(hidelayer[0].N_NODES);}}void FirstTimeSettings() {for (int i = 0; i < n_layer; i++) {int j;for (j = 0; j < hidelayer[i].N_NODES; j++) {hidelayer[i].nodes[j].threshold = (2 * Math.random() – 1) *hidelayer[i].nodes[j].amultinum;}}for (int i = 0; i < n_output; i++) {output.nodes[i].threshold = (2 * Math.random() – 1) *output.nodes[i].amultinum;}}void BeforeTraining(double inp[], double outp[]) {int i;for (i = 0; i < n_input; i++) {input.nodes[i].activation = inp[i];}for (i = 0; i < n_output; i++) {desiredOutputs[i] = outp[i];}}public void Calc_Activation(double result[]) {int i, j, ci;for (i = 0; i < n_layer; i++) {if (i == 0) {for (j = 0; j < hidelayer[i].N_NODES; j++) {hidelayer[i].nodes[j].activation = 0;for (ci = 0; ci < n_input; ci++) {hidelayer[i].nodes[j].activation += input.nodes[ci].activation *input.nodes[ci].weights[j];}hidelayer[i].nodes[j].activation += hidelayer[i].nodes[j].threshold;hidelayer[i].nodes[j].activation = activefun(hidelayer[i].nodes[j].activation);}}else {for (j = 0; j < hidelayer[i].N_NODES; j++) {hidelayer[i].nodes[j].activation = 0;for (ci = 0; ci < hidelayer[i – 1].N_NODES; ci++) {hidelayer[i].nodes[j].activation += hidelayer[i -1].nodes[ci].activation * hidelayer[i – 1].nodes[ci].weights[j];}hidelayer[i].nodes[j].activation += hidelayer[i].nodes[j].threshold;hidelayer[i].nodes[j].activation = activefun(hidelayer[i].nodes[j].activation);}}}for (j = 0; j < output.N_NODES; j++) {output.nodes[j].activation = 0;for (ci = 0; ci < hidelayer[n_layer – 1].N_NODES; ci++) {output.nodes[j].activation += hidelayer[n_layer -1].nodes[ci].activation * hidelayer[n_layer -1].nodes[ci].weights[j];}output.nodes[j].activation += output.nodes[j].threshold;output.nodes[j].activation = activefun(output.nodes[j].activation);}for (i = 0; i < n_output; i++) {result[i] = output.nodes[i].activation;}}void Calc_error_output() {for (int x = 0; x < n_output; x++)//output.nodes[x].error = output.nodes[x].activation * (1 – output.nodes[x].activation ) * (desiredOutputs[x] – output.nodes[x].activation );{output.nodes[x].error += (output.nodes[x].activation – desiredOutputs[x]);output.nodes[x].error *= difactivefun(output.nodes[x].activation);}}void Calc_error_hidden() {int j, i;for (j = 0; j < hidelayer[n_layer – 1].N_NODES; j++) {for (int x = 0; x < n_output; x++) {hidelayer[n_layer – 1].nodes[j].error += hidelayer[n_layer -1].nodes[j].weights[x] * output.nodes[x].error;}hidelayer[n_layer -1].nodes[j].error *=difactivefun(hidelayer[n_layer – 1].nodes[j].activation);}for (i = n_layer – 2; i >= 0; i–) {for (j = 0; j < hidelayer[i].N_NODES; j++) {for (int x = 0; x < hidelayer[i + 1].N_NODES; x++) {hidelayer[i].nodes[j].error += hidelayer[i].nodes[j].weights[x] *hidelayer[i + 1].nodes[x].error;}hidelayer[i].nodes[j].error *=difactivefun(hidelayer[i].nodes[j].activation);}}}void Calc_new_Thresholds() {int i;// computing the thresholds for next itration for hidden layerfor (i = 0; i < n_layer; i++) {for (int x = 0; x < hidelayer[i].N_NODES; x++) {double det = a * hidelayer[i].nodes[x].detthresholdlast +hidelayer[i].nodes[x].error * LH;hidelayer[i].nodes[x].detthresholdlast = det;hidelayer[i].nodes[x].threshold += det;}}for (int y = 0; y < output.N_NODES; y++) {double det = a * output.nodes[y].detthresholdlast +output.nodes[y].error * LO;output.nodes[y].detthresholdlast = det;output.nodes[y].threshold += det;}}void Calc_new_weights_in_hidden() {int i, j;double temp = 0.0f;for (j = 0; j < hidelayer[n_layer – 1].N_NODES; j++) {temp = hidelayer[n_layer – 1].nodes[j].activation * LO;for (int y = 0; y < n_output; y++) {double det = a * hidelayer[n_layer – 1].nodes[j].detweightslast[y] +temp * output.nodes[y].error;hidelayer[n_layer – 1].nodes[j].detweightslast[y] = det;hidelayer[n_layer – 1].nodes[j].weights[y] += det;}}for (i = 0; i < n_layer – 1; i++) {for (j = 0; j < hidelayer[i].N_NODES; j++) {temp = hidelayer[i].nodes[j].activation * LH;for (int y = 0; y < hidelayer[i + 1].N_NODES; y++) {double det = a * hidelayer[i].nodes[j].detweightslast[y] +temp * hidelayer[i + 1].nodes[y].error;hidelayer[i].nodes[j].detweightslast[y] = det;hidelayer[i].nodes[j].weights[y] += det;}}}}void Calc_new_weights_in_input() {int j;double temp = 0.0f;for (j = 0; j < input.N_NODES; j++) {temp = input.nodes[j].activation * LH;for (int y = 0; y < hidelayer[0].N_NODES; y++) {double det = a * input.nodes[j].detweightslast[y] +temp * hidelayer[0].nodes[y].error;input.nodes[j].detweightslast[y] = det;input.nodes[j].weights[y] += det;}}}double Calc_total_error_in_pattern() {double temp = 0.0;for (int x = 0; x < n_output; x++) {[x].activation – desiredOutputs[x]);continue;temp += Math.pow((output.nodes[x].activation – desiredOutputs[x]), 2);total_error_one_circle[x] += Math.pow((output.nodes[x].activation – desiredOutputs[x]), 2);}total_error = temp;total_error_one_circle_all += total_error;return temp;}void reset_error() {for (int i = 0; i < n_input; i++) {input.nodes[i].error = 0;}for (int i = 0; i < n_output; i++) {output.nodes[i].error = 0;}for (int i = 0; i < n_layer; i++) {for (int j = 0; j < hidelayer[i].N_NODES; j++) {hidelayer[i].nodes[j].error = 0;}}}void reset_total_error() {total_error_one_circle_all = 0;for (int x = 0; x < n_output; x++) {total_error_one_circle[x] = 0;}}void Training_for_one_pattern(double result[]) {Calc_Activation(result);Calc_error_output();Calc_error_hidden();Calc_new_Thresholds();Calc_new_weights_in_hidden();Calc_new_weights_in_input();}public void Training(double inputs[][], double outputs[][], int num, boolean ifresort) {clearlastdetweight();iftrain = new boolean[num];setiftrain(inputs, outputs, num, iftrain);int neworder[] = new int[num];sortrandom(neworder, num, ifresort);reset_total_error();for (int k = 0; k < num; k++) {int i = neworder[k];if (iftrain[i]) {//System.out.println("k="+k+",i="+i+",iftrain[i]="+iftrain[i]);reset_error();BeforeTraining(inputs[i], outputs[i]);double tmp[] = new double[output.N_NODES];Calc_Activation(tmp);Calc_error_output();Calc_total_error_in_pattern();//System.out.println(k+" "+tmp[0]+" "+outputs[i][0]+" "+output.nodes[0].activation +" "+ desiredOutputs[0]+" "+total_error_one_circle_all);Calc_error_hidden();Calc_new_Thresholds();Calc_new_weights_in_hidden();Calc_new_weights_in_input();}}}void TrainingAll(double inputs[][], double outputs[][], int num,boolean ifresort) {clearlastdetweight();iftrain = new boolean[num];setiftrain(inputs, outputs, num, iftrain);int neworder[] = new int[num];sortrandom(neworder, num, ifresort);reset_total_error();reset_error();for (int k = 0; k < num; k++) {int i = neworder[k];if (iftrain[i]) {BeforeTraining(inputs[i], outputs[i]);double tmp[] = new double[output.N_NODES];Calc_Activation(tmp);Calc_error_output();Calc_total_error_in_pattern();}}Calc_error_hidden();Calc_new_Thresholds();Calc_new_weights_in_hidden();Calc_new_weights_in_input();}void clearlastdetweight() {for (int i = 0; i < n_input; i++) {input.nodes[i].detthresholdlast = 0;for (int j = 0; j < input.nodes[i].numOfweights; j++) {input.nodes[i].detweightslast[j] = 0;}}for (int i = 0; i < n_output; i++) {output.nodes[i].detthresholdlast = 0;for (int j = 0; j < output.nodes[i].numOfweights; j++) {output.nodes[i].detweightslast[j] = 0;}}for (int k = 0; k < n_layer; k++) {for (int i = 0; i < hidelayer[k].N_NODES; i++) {hidelayer[k].nodes[i].detthresholdlast = 0;for (int j = 0; j < hidelayer[k].nodes[i].numOfweights; j++) {hidelayer[k].nodes[i].detweightslast[j] = 0;}}}}void sortrandom(int neworder[], int num, boolean ifresort) {for (int i = 0; i < num; i++) {neworder[i] = i;}if (ifresort) {for (int i = 0; i < num; i++) {int pos = (int) (Math.random() * (num – i)) + i;int tmp = neworder[pos];neworder[pos] = neworder[i];neworder[i] = tmp;}}}int setiftrain(double inputs[][], double outputs[][], int num,boolean iftrain[]) {for (int i = 0; i < num; i++) {iftrain[i] = true;if (outputs[i][0] <= 0) {iftrain[i] = false;}}for (int i = 0; i < num; i++) {for (int j = 0; j < num; j++) {if (i != j) {boolean ifsame = true;for (int k = 0; k < n_input; k++) {if (inputs[i][k] != inputs[j][k]) {ifsame = false;break;}}if (ifsame) {iftrain[i] = false;}}if (iftrain[i] == false) {break;}}}trainnum = 0;for (int i = 0; i < num; i++) {if (iftrain[i]) {trainnum++;}}return trainnum;}double sigmoid(double x) {return 1 / (1 + Math.exp( -x));}double difsigmoid(double x) {return x – x * x;}double tanh(double x) {return (1 – Math.exp( -x)) / (1 + Math.exp( -x));}double diftanh(double x) {return (1 – x * x) / 2;}double activefun(double x) {if (connecttype == 0) {return sigmoid(x);}else if (connecttype == 1) {return tanh(x);}else {return 0;}}double difactivefun(double x) {if (connecttype == 0) {return difsigmoid(x);}else if (connecttype == 1) {return diftanh(x);}else {return 0;}}}package ann;public class Layer implements java.io.Serializable,Cloneable{public int N_NODES;public Node nodes[];public Layer() {}public Layer(int n){N_NODES=n;nodes=new Node[N_NODES];}}
，对的，坚持;错的，放弃!
