bp神经网络算法的java实现
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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(); } } }</span>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 = <pre code_snippet_id="304253" snippet_file_name="blog_20140421_2_1796114" name="code" class="java"> //output.nodes[x].activation * (1 - output.nodes[x].activation ) * (desiredOutputs[x] - output.nodes[x].activation );<pre code_snippet_id="304253" snippet_file_name="blog_20140421_2_1796114" name="code" class="java"> { 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 layer for (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]; } } { 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 layer for (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; } } } 0 0
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