本文整理汇总了Java中org.neuroph.core.NeuralNetwork.getOutput方法的典型用法代码示例。如果您正苦于以下问题:Java NeuralNetwork.getOutput方法的具体用法?Java NeuralNetwork.getOutput怎么用?Java NeuralNetwork.getOutput使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.neuroph.core.NeuralNetwork
的用法示例。
在下文中一共展示了NeuralNetwork.getOutput方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: testNeuralNet
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
private static void testNeuralNet(NeuralNetwork nnet, DataSet dataSet, String setName) {
int counter = 0;
for (DataSetRow row : dataSet.getRows()) {
nnet.setInput(row.getInput());
nnet.calculate();
double[] networkOutput = nnet.getOutput();
if (isOutputSame(networkOutput, row.getDesiredOutput())) {
counter++;
} else {
for (int i = 0; i < row.getDesiredOutput().length; i++) {
if (row.getDesiredOutput()[i] == 1) {
Integer d = errorMap.get(i);
if (d == null) {
errorMap.put(i, 1);
} else {
errorMap.put(i, ++d);
}
break;
}
}
}
}
System.out.println(setName + " success rate: " + (counter / (double) dataSet.size()));
}
示例2: testNeuralNet
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
private static void testNeuralNet(NeuralNetwork nnet, DataSet dataSet, String setName) {
int counter = 0;
for (DataSetRow row : dataSet.getRows()) {
nnet.setInput(row.getInput());
nnet.calculate();
double[] networkOutput = nnet.getOutput();
if (isOutputSame(networkOutput, row.getDesiredOutput())) {
counter++;
} else {
for (int i = 0; i < row.getDesiredOutput().length; i++) {
if (row.getDesiredOutput()[i] == 1) {
Integer d = errorMap.get(i);
if (d == null) {
errorMap.put(i, 1);
} else {
errorMap.put(i, ++d);
}
break;
}
}
}
}
System.out.println(setName + " success rate: " + (counter / (double) dataSet.size()));
}
示例3: testNeuralNetwork
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
public static void testNeuralNetwork(NeuralNetwork nnet, DataSet testSet) {
for(DataSetRow dataRow : testSet.getRows()) {
nnet.setInput(dataRow.getInput());
nnet.calculate();
double[ ] networkOutput = nnet.getOutput();
System.out.print("Input: " + Arrays.toString(dataRow.getInput()) );
System.out.println(" Output: " + Arrays.toString(networkOutput) );
}
}
示例4: testNeuralNet
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
private static void testNeuralNet(NeuralNetwork nnet, DataSet dataSet, String setName) {
int counter = 0;
for (DataSetRow row : dataSet.getRows()) {
nnet.setInput(row.getInput());
nnet.calculate();
double[] networkOutput = nnet.getOutput();
// System.out.println(Arrays.toString(networkOutput));
if (isOutputSame(networkOutput, row.getDesiredOutput())) {
counter++;
} else {
for (int i = 0; i < row.getDesiredOutput().length; i++) {
if (row.getDesiredOutput()[i] == 1) {
Integer d = errorMap.get(i);
if (d == null) {
errorMap.put(i, 1);
} else {
errorMap.put(i, ++d);
}
break;
}
}
}
}
System.out.println(setName + " success rate: " + (counter / (double) dataSet.size()));
}
示例5: testNeuralNet
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
private static void testNeuralNet(NeuralNetwork nnet, DataSet dataSet, String setName) {
int counter = 0;
for (DataSetRow row : dataSet.getRows()) {
nnet.setInput(row.getInput());
nnet.calculate();
double[] networkOutput = nnet.getOutput();
//System.out.println(Arrays.toString(networkOutput));
if (isOutputSame(networkOutput, row.getDesiredOutput())) {
counter++;
} else {
for (int i = 0; i < row.getDesiredOutput().length; i++) {
if (row.getDesiredOutput()[i] == 1) {
Integer d = errorMap.get(i);
if (d == null) {
errorMap.put(i, 1);
} else {
errorMap.put(i, ++d);
}
break;
}
}
}
}
System.out.println(setName + " success rate: " + (counter / (double) dataSet.size()));
}
示例6: testNeuralNetwork
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
/**
* Prints network output for the each element from the specified training set.
* @param neuralNet neural network
* @param testSet test data set
*/
public static void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
for(DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.print("Input: " + Arrays.toString( testSetRow.getInput() ) );
System.out.println(" Output: " + Arrays.toString( networkOutput) );
}
}
示例7: testNeuralNetwork
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
/**
* Prints network output for the each element from the specified training set.
* @param neuralNet neural network
* @param trainingSet training set
*/
public static void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
for(DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.print("Input: " + Arrays.toString( testSetRow.getInput() ) );
System.out.println(" Output: " + Arrays.toString( networkOutput) );
}
}
示例8: testNeuralNetwork
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
/**
* Prints network output for each element from the specified training set.
* @param neuralNet neural network
* @param trainingSet training set
*/
public static void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
for(DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.print("Input: " + Arrays.toString( testSetRow.getInput() ) );
System.out.println(" Output: " + Arrays.toString( networkOutput) );
}
}
示例9: main
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
/**
* Runs this sample
*/
public static void main(String[] args) throws FileNotFoundException, IOException {
// create neural network
//MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(2, 3, 1);
// use file provided in org.neuroph.sample.data package
String inputFileName = FileIOSample.class.getResource("data/xor_data.txt").getFile();
// create file input adapter using specifed file
FileInputAdapter fileIn = new FileInputAdapter(inputFileName);
// create file output adapter using specified file name
FileOutputAdapter fileOut = new FileOutputAdapter("some_output_file.txt");
NeuralNetwork neuralNet = NeuralNetwork.load("myMlPerceptron.nnet");
double[] input; // input buffer used for reading network input from file
// read network input using input adapter
while( (input = fileIn.readInput()) != null) {
// feed neywork with input
neuralNet.setInput(input);
// calculate network ...
neuralNet.calculate();
// .. and get network output
double[] output = neuralNet.getOutput();
// write network output using output adapter
fileOut.writeOutput(output);
}
// close input and output files
fileIn.close();
fileOut.close();
// Also note that shorter way for this is using org.neuroph.util.io.IOHelper class
}
示例10: testNeuralNetwork
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
/**
* Prints network output for the each element from the specified training set.
* @param neuralNet neural network
* @param testSet data set used for testing
*/
public static void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
for(DataSetRow trainingElement : testSet.getRows()) {
neuralNet.setInput(trainingElement.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.print("Input: " + Arrays.toString(trainingElement.getInput()) );
System.out.println(" Output: " + Arrays.toString(networkOutput) );
}
}
示例11: main
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
public static void main(String[] args) {
// create new perceptron network
NeuralNetwork neuralNetwork = new Perceptron(3, 1);
// create training set
DataSet trainingSet = new DataSet(3, 1);
// add training data to training set (logical OR function)
trainingSet.addRow(new DataSetRow(new double[] { 0, 0 ,0}, new double[] { 0 }));
trainingSet.addRow(new DataSetRow(new double[] { 0, 1 ,0}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 0 ,0}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 1 ,0}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 0, 0 ,1}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 0, 1 ,1}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 0 ,1}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 1 ,1}, new double[] { 1 }));
// learn the training set
neuralNetwork.learn(trainingSet);
// save the trained network into file
neuralNetwork.save("or_perceptron.nnet");
// load the saved network
NeuralNetwork neuralNetwork1 = NeuralNetwork.createFromFile("or_perceptron.nnet");
// set network input
neuralNetwork1.setInput(1, 1,1);
// calculate network
neuralNetwork1.calculate();
// get network output
double[] networkOutput = neuralNetwork1.getOutput();
System.out.println(Arrays.toString(networkOutput));
}
示例12: testNeuralNetwork
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
public static void testNeuralNetwork(NeuralNetwork neuralNet,
DataSet testSet) {
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out
.print("Input: " + Arrays.toString(testSetRow.getInput()));
System.out.println(" Output: " + Arrays.toString(networkOutput));
}
}
示例13: testNeuralNetwork
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
public static void testNeuralNetwork(NeuralNetwork nnet, DataSetRow dataRow) {
nnet.setInput(dataRow.getInput());
nnet.calculate();
double[ ] networkOutput = nnet.getOutput();
System.out.print("Input: " + Arrays.toString(dataRow.getInput()) );
System.out.println(" Output: " + Arrays.toString(networkOutput) );
}
示例14: process
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
/**
* Feeds specified neural network with data from InputAdapter and writes
* output using OutputAdapter
* @param neuralNet neural network
* @param in input data source
* @param out output data target
*/
public static void process(NeuralNetwork neuralNet, InputAdapter in, OutputAdapter out) {
double[] input;
while( (input = in.readInput()) != null) {
neuralNet.setInput(input);
neuralNet.calculate();
double[] output = neuralNet.getOutput();
out.writeOutput(output);
}
in.close();
out.close();
}
示例15: predict
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
public static String predict(int[] dys,String HG) {
int[] TUNING=YSubCladeUtil.getTuning(HG);
int[] BITS=YSubCladeUtil.getBits(HG);
String[] OUTPUT=YSubCladeUtil.getOutput(HG);
int total_bits=0;
for(int i=0;i<BITS.length;i++)
total_bits+=BITS[i];
String[] binary_str=new String[12];
StringBuffer sb=new StringBuffer();
for(int i=0;i<12;i++)
{
binary_str[i]="00000"+Integer.toBinaryString(dys[i]-TUNING[i]);
binary_str[i]=binary_str[i].substring(binary_str[i].length()-BITS[i]);
sb.append(binary_str[i]);
}
String input=sb.toString();
double[] input_normalized=new double[total_bits];
for(int i=0;i<total_bits;i++)
{
if(input.charAt(i)=='0')
input_normalized[i]=0;
else if(input.charAt(i)=='1')
input_normalized[i]=1;
else
throw new RuntimeException("Error:Input data not normalized.");
}
NeuralNetwork neuralNetwork = NeuralNetwork.load(YHaploEntryDlg.class.getResourceAsStream("/fc/id/au/haplogroups/"+HG+".nnet"));
neuralNetwork.setInput(input_normalized);
neuralNetwork.calculate();
double[] networkOutput = neuralNetwork.getOutput();
sb.setLength(0);
for(int i=0;i<networkOutput.length;i++)
{
sb.append((Math.round(networkOutput[i])));
}
String output=sb.toString();
int value=Integer.parseInt(output, 2);
String haplogroup=OUTPUT[value];
return haplogroup;
}