org.encog.util.simple
public final class EncogUtility extends Object
Modifier and Type | Method and Description |
---|---|
static double |
calculateClassificationError(MLClassification method,
MLDataSet data)
Calculate the classification error.
|
static double |
calculateRegressionError(MLRegression method,
MLDataSet data) |
static void |
convertCSV2Binary(File csvFile,
CSVFormat format,
File binFile,
int[] input,
int[] ideal,
boolean headers) |
static void |
convertCSV2Binary(File csvFile,
File binFile,
int inputCount,
int outputCount,
boolean headers)
Convert a CSV file to a binary training file.
|
static void |
convertCSV2Binary(String csvFile,
String binFile,
int inputCount,
int outputCount,
boolean headers)
Convert a CSV file to a binary training file.
|
static void |
evaluate(MLRegression network,
MLDataSet training)
Evaluate the network and display (to the console) the output for every
value in the training set.
|
static void |
explainErrorMSE(MLRegression method,
MatrixMLDataSet training) |
static void |
explainErrorRMS(MLRegression method,
MatrixMLDataSet training) |
static String |
formatNeuralData(MLData data)
Format neural data as a list of numbers.
|
static MLDataSet |
loadCSV2Memory(String filename,
int input,
int ideal,
boolean headers,
CSVFormat format,
boolean significance)
Load CSV to memory.
|
static MLDataSet |
loadEGB2Memory(File filename) |
static void |
saveCSV(File targetFile,
CSVFormat format,
MLDataSet set) |
static void |
saveEGB(File f,
MLDataSet data)
Save a training set to an EGB file.
|
static BasicNetwork |
simpleFeedForward(int input,
int hidden1,
int hidden2,
int output,
boolean tanh)
Create a simple feedforward neural network.
|
static void |
trainConsole(BasicNetwork network,
MLDataSet trainingSet,
int minutes)
Train the neural network, using SCG training, and output status to the
console.
|
static void |
trainConsole(MLTrain train,
BasicNetwork network,
MLDataSet trainingSet,
int minutes)
Train the network, using the specified training algorithm, and send the
output to the console.
|
static void |
trainToError(MLMethod method,
MLDataSet dataSet,
double error)
Train the method, to a specific error, send the output to the console.
|
static void |
trainToError(MLTrain train,
double error)
Train to a specific error, using the specified training method, send the
output to the console.
|
public static void convertCSV2Binary(File csvFile, File binFile, int inputCount, int outputCount, boolean headers)
csvFile
- The CSV file.binFile
- The binary file.inputCount
- The number of input values.outputCount
- The number of output values.headers
- True, if there are headers on the3 CSV.public static MLDataSet loadCSV2Memory(String filename, int input, int ideal, boolean headers, CSVFormat format, boolean significance)
filename
- The CSV file to load.input
- The input count.ideal
- The ideal count.headers
- True, if headers are present.format
- The loaded dataset.significance
- True, if there is a significance column.public static void evaluate(MLRegression network, MLDataSet training)
network
- The network to evaluate.training
- The training set to evaluate.public static String formatNeuralData(MLData data)
data
- The neural data to format.public static BasicNetwork simpleFeedForward(int input, int hidden1, int hidden2, int output, boolean tanh)
input
- The number of input neurons.hidden1
- The number of hidden layer 1 neurons.hidden2
- The number of hidden layer 2 neurons.output
- The number of output neurons.tanh
- True to use hyperbolic tangent activation function, false to
use the sigmoid activation function.public static void trainConsole(BasicNetwork network, MLDataSet trainingSet, int minutes)
network
- The network to train.trainingSet
- The training set.minutes
- The number of minutes to train for.public static void trainConsole(MLTrain train, BasicNetwork network, MLDataSet trainingSet, int minutes)
train
- The training method to use.network
- The network to train.trainingSet
- The training set.minutes
- The number of minutes to train for.public static void trainToError(MLMethod method, MLDataSet dataSet, double error)
method
- The method to train.dataSet
- The training set to use.error
- The error level to train to.public static void trainToError(MLTrain train, double error)
train
- The training method.error
- The desired error level.public static void convertCSV2Binary(String csvFile, String binFile, int inputCount, int outputCount, boolean headers)
csvFile
- The binary file.binFile
- The binary file.inputCount
- The number of input values.outputCount
- The number of output values.headers
- True, if there are headers on the CSV.public static void convertCSV2Binary(File csvFile, CSVFormat format, File binFile, int[] input, int[] ideal, boolean headers)
public static double calculateRegressionError(MLRegression method, MLDataSet data)
public static double calculateClassificationError(MLClassification method, MLDataSet data)
method
- The method to check.data
- The data to check.public static void saveEGB(File f, MLDataSet data)
f
- data
- public static void explainErrorMSE(MLRegression method, MatrixMLDataSet training)
public static void explainErrorRMS(MLRegression method, MatrixMLDataSet training)
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