Java Code Examples for org.neuroph.core.NeuralNetwork#getOutput()
The following examples show how to use
org.neuroph.core.NeuralNetwork#getOutput() .
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
| Source File: BreastCancerSample.java From NeurophFramework with Apache License 2.0 | 6 votes |
|
public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
System.out.println("********************** TEST RESULT **********************");
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
// get network output
double[] networkOutput = neuralNet.getOutput();
int predicted = interpretOutput(networkOutput);
// get target/desired output
double[] desiredOutput = testSetRow.getDesiredOutput();
int target = (int)desiredOutput[0];
// count predictions
countPredictions(predicted, target);
}
System.out.println("Total cases: " + total + ". ");
System.out.println("Correctly predicted cases: " + correct);
System.out.println("Incorrectly predicted cases: " + incorrect);
double percentTotal = (correct / (double)total) * 100;
System.out.println("Predicted correctly: " + formatDecimalNumber(percentTotal) + "%. ");
}
Example 2
| Source File: BostonHousePrice.java From NeurophFramework with Apache License 2.0 | 6 votes |
|
public void evaluate(NeuralNetwork neuralNet, DataSet dataSet) {
System.out.println("Calculating performance indicators for neural network.");
MeanSquaredError mse = new MeanSquaredError();
MeanAbsoluteError mae = new MeanAbsoluteError();
for (DataSetRow testSetRow : dataSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
double[] desiredOutput = testSetRow.getDesiredOutput();
mse.addPatternError(networkOutput, desiredOutput);
mae.addPatternError(networkOutput, desiredOutput);
}
System.out.println("Mean squared error is: " + mse.getTotalError());
System.out.println("Mean absolute error is: " + mae.getTotalError());
}
Example 3
| Source File: SwedishAutoInsurance.java From NeurophFramework with Apache License 2.0 | 6 votes |
|
public void evaluate(NeuralNetwork neuralNet, DataSet dataSet) {
System.out.println("Calculating performance indicators for neural network.");
MeanSquaredError mse = new MeanSquaredError();
MeanAbsoluteError mae = new MeanAbsoluteError();
for (DataSetRow testSetRow : dataSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
double[] desiredOutput = testSetRow.getDesiredOutput();
mse.addPatternError(networkOutput, desiredOutput);
mae.addPatternError(networkOutput, desiredOutput);
}
System.out.println("Mean squared error is: " + mse.getTotalError());
System.out.println("Mean absolute error is: " + mae.getTotalError());
}
Example 4
| Source File: DiabetesSample.java From NeurophFramework with Apache License 2.0 | 6 votes |
|
public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
System.out.println("**********************RESULT**********************");
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
// get network output
double[] networkOutput = neuralNet.getOutput();
int predicted = interpretOutput(networkOutput);
// get target/desired output
double[] desiredOutput = testSetRow.getDesiredOutput();
int target = (int)desiredOutput[0];
// count predictions
countPredictions(predicted, target);
}
System.out.println("Total cases: " + total + ". ");
System.out.println("Correctly predicted cases: " + correct);
System.out.println("Incorrectly predicted cases: " + incorrect);
double percentTotal = (correct / (double)total) * 100;
System.out.println("Predicted correctly: " + formatDecimalNumber(percentTotal) + "%. ");
}
Example 5
| Source File: BalanceScaleSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public 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));
}
}
Example 6
| Source File: ConcreteStrenghtTestSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public 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));
}
}
Example 7
| Source File: ConceptLearningAndClassificationSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public 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));
}
}
Example 8
| Source File: PredictingPerformanceOfCPUSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public 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));
}
}
Example 9
| Source File: SwedishAutoInsurance.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
System.out.println("Showing inputs, desired output and neural network output for every row in test set.");
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.println("Input: " + Arrays.toString(testSetRow.getInput()));
System.out.println("Output: " + networkOutput[0]);
System.out.println("Desired output" + Arrays.toString(networkOutput));
}
}
Example 10
| Source File: ForestFiresSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public 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));
}
}
Example 11
| Source File: ShuttleLandingControlSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public 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));
}
}
Example 12
| Source File: Banknote.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
System.out.println("Showing inputs, desired output and neural network output for every row in test set.");
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.println("Input: " + Arrays.toString(testSetRow.getInput()));
System.out.println("Output: " + Arrays.toString(networkOutput));
System.out.println("Desired output" + Arrays.toString(testSetRow.getDesiredOutput()));
}
}
Example 13
| Source File: XorMultiLayerPerceptronSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
/**
* Prints network output for the each element from the specified training set.
* @param neuralNet neural network
* @param testSet test 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) );
}
}
Example 14
| Source File: WheatSeeds.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
System.out.println("Showing inputs, desired output and neural network output for every row in test set.");
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.println("Input: " + Arrays.toString(testSetRow.getInput()));
System.out.println("Output: " + Arrays.toString(networkOutput));
System.out.println("Desired output" + Arrays.toString(testSetRow.getDesiredOutput()));
}
}
Example 15
| Source File: BrestCancerSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
System.out.println("**************************************************");
System.out.println("**********************RESULT**********************");
System.out.println("**************************************************");
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
//Finding network output
double[] networkOutput = neuralNet.getOutput();
int predicted = maxOutput(networkOutput);
//Finding actual output
double[] networkDesiredOutput = testSetRow.getDesiredOutput();
int ideal = maxOutput(networkDesiredOutput);
//Colecting data for network evaluation
keepScore(predicted, ideal);
}
System.out.println("Total cases: " + this.count[2] + ". ");
System.out.println("Correctly predicted cases: " + this.correct[2] + ". ");
System.out.println("Incorrectly predicted cases: " + (this.count[2] - this.correct[2] - unpredicted) + ". ");
System.out.println("Unrecognized cases: " + unpredicted + ". ");
double percentTotal = (double) this.correct[2] * 100 / (double) this.count[2];
System.out.println("Predicted correctly: " + formatDecimalNumber(percentTotal) + "%. ");
double percentM = (double) this.correct[0] * 100.0 / (double) this.count[0];
System.out.println("Prediction for 'M (malignant)' => (Correct/total): "
+ this.correct[0] + "/" + count[0] + "(" + formatDecimalNumber(percentM) + "%). ");
double percentB = (double) this.correct[1] * 100.0 / (double) this.count[1];
System.out.println("Prediction for 'B (benign)' => (Correct/total): "
+ this.correct[1] + "/" + count[1] + "(" + formatDecimalNumber(percentB) + "%). ");
}
Example 16
| Source File: Evaluate.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
//Finding network output
double[] networkOutput = neuralNet.getOutput();
int predicted = maxOutput(networkOutput);
//Finding actual output
double[] networkDesiredOutput = testSetRow.getDesiredOutput();
int ideal = maxOutput(networkDesiredOutput);
//Colecting data for network evaluation
keepScore(predicted, ideal);
}
System.out.println("Total cases: " + this.count[7] + ". ");
System.out.println("Correct cases: " + this.correct[7] + ". ");
System.out.println("Incorrectly predicted cases: " + (this.count[7] - this.correct[7] - unpredicted) + ". ");
System.out.println("Unrecognized cases: " + unpredicted + ". ");
double percentTotal = (double) this.correct[7] * 100 / (double) this.count[7];
System.out.println("Predicted correctly: " + formatDecimalNumber(percentTotal) + "%. ");
for (int i = 0; i < correct.length - 1; i++) {
double p = (double) this.correct[i] * 100.0 / (double) this.count[i];
System.out.println("Tree type: " + (i + 1) + " - Correct/total: "
+ this.correct[i] + "/" + count[i] + "(" + formatDecimalNumber(p) + "%). ");
}
}
Example 17
| Source File: GlassIdentificationSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public 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));
}
}
Example 18
| Source File: PredictingPokerHandsSample.java From NeurophFramework with Apache License 2.0 | 5 votes |
|
public 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));
}
}
Example 19
| Source File: SunSpots.java From NeurophFramework with Apache License 2.0 | 4 votes |
|
/**
* Predict sunspots.
* @param network Neural network to use.
*/
public void predict(NeuralNetwork network) {
NumberFormat f = NumberFormat.getNumberInstance();
f.setMaximumFractionDigits(4);
f.setMinimumFractionDigits(4);
System.out.println("Year\tActual\tPredict\tClosed Loop Predict");
for (int year = EVALUATE_START; year < EVALUATE_END; year++) {
// calculate based on actual data
double[] input = new double[WINDOW_SIZE];
for (int i = 0; i < input.length; i++) {
input[i] = this.normalizedSunspots[(year - WINDOW_SIZE) + i];
}
network.setInput(input);
network.calculate();
double[] output = network.getOutput();
double prediction = output[0];
this.closedLoopSunspots[year] = prediction;
// calculate "closed loop", based on predicted data
for (int i = 0; i < input.length; i++) {
input[i] = this.closedLoopSunspots[(year - WINDOW_SIZE) + i];
}
network.setInput(input);
network.calculate();
output = network.getOutput();
double closedLoopPrediction = output[0];
// display
System.out.println((STARTING_YEAR + year) + "\t"
+ f.format(this.normalizedSunspots[year]) + "\t"
+ f.format(prediction) + "\t"
+ f.format(closedLoopPrediction));
}
}
Example 20
| Source File: McNemarTest.java From NeurophFramework with Apache License 2.0 | 4 votes |
|
/**
* @param network1 first trained neurl netowrk
* @param network2 second trained neural network
* @param dataSet data set used for performance evaluation
* @return if there exists significant difference between two classification models
*/
public boolean evaluateNetworks(NeuralNetwork network1, NeuralNetwork network2, DataSet dataSet) {
for (DataSetRow dataRow : dataSet.getRows()) {
forwardPass(network1, dataRow);
forwardPass(network2, dataRow);
double[] networkOutput1 = network1.getOutput();
double[] networkOutput2 = network2.getOutput();
int maxNeuronIdx1 = Utils.maxIdx(networkOutput1);
int maxNeuronIdx2 = Utils.maxIdx(networkOutput2);
ClassificationResult output1 = new ClassificationResult(maxNeuronIdx1, networkOutput1[maxNeuronIdx1]);
ClassificationResult output2 = new ClassificationResult(maxNeuronIdx2, networkOutput2[maxNeuronIdx2]);
// ClassificationResult output1 = ClassificationResult.fromMaxOutput(network1.getOutput());
// ClassificationResult output2 = ClassificationResult.fromMaxOutput(network2.getOutput());
//are their results different
if (output1.getClassIdx() != output2.getClassIdx()) {
//if first one is correct and second incorrect
if (output1.getClassIdx() == getDesiredClass(dataRow.getDesiredOutput())) {
contigencyMatrix[1][0]++;
//if first is incorrect and second is correct
} else {
contigencyMatrix[0][1]++;
}
} else {
//if both are correct
if (output1.getClassIdx() == getDesiredClass(dataRow.getDesiredOutput())) {
contigencyMatrix[1][1]++;
//if both are incorrect
} else {
contigencyMatrix[0][0]++;
}
}
}
printContingencyMatrix();
double a = Math.abs(contigencyMatrix[0][1] - contigencyMatrix[1][0]) - 1;
double hiSquare = (a * a) / (contigencyMatrix[0][1] + contigencyMatrix[1][0]);
System.out.println(hiSquare);
return hiSquare > 3.841;
}
