encog(二)神经网络的训练

Encog中有很多的训练方法。

EncogUtility是一个功能辅助类，提供了很多方便的函数

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.

BasicTraining类是所有训练方法类的父类

构造函数
`BasicTraining()` Used for serialization.
`BasicTraining(TrainingImplementationType implementationType)`

返回值	成员函数
`void`	`addStrategy(Strategy strategy)` Training strategies can be added to improve the training results.
`void`	`finishTraining()` Should be called after training has completed and the iteration method will not be called any further.
`double`	`getError()`
`TrainingImplementationType`	`getImplementationType()`
`int`	`getIteration()`
`List<Strategy>`	`getStrategies()`
`MLDataSet`	`getTraining()`
`boolean`	`isTrainingDone()`
`void`	`iteration(int count)` Perform the specified number of training iterations.
`void`	`postIteration()` Call the strategies after an iteration.
`void`	`preIteration()` Call the strategies before an iteration.
`void`	`setError(double error)`
`void`	`setIteration(int iteration)` Set the current training iteration.
`void`	`setTraining(MLDataSet training)` Set the training object that this strategy is working with.

Backpropagation类是propagation类的子类

构造函数

构造函数
`Backpropagation(ContainsFlat network, MLDataSet training)` Create a class to train using backpropagation.
`Backpropagation(ContainsFlat network, MLDataSet training, double theLearnRate, double theMomentum)` 第一个参数：将被训练的网络第二个参数: 训练集第三个参数：学习率第四个参数: 梯度下降法中一种常用的加速技术。momentum是加速系数，momentum=0表示无加速，值越大表示加速越快。

Backpropagation(ContainsFlat network, MLDataSet training)

Create a class to train using backpropagation.

Backpropagation(ContainsFlat network, MLDataSet training, double theLearnRate, double theMomentum)

第一个参数：将被训练的网络

第二个参数: 训练集

第三个参数：学习率

第四个参数: 梯度下降法中一种常用的加速技术。momentum是加速系数，momentum=0表示无加速，值越大表示加速越快。

返回值	成员函数
`boolean`	`canContinue()`
`double[]`	`getLastDelta()`
`double`	`getLearningRate()`
`double`	`getMomentum()`
`void`	`initOthers()` Perform training method specific init.
`boolean`	`isValidResume(TrainingContinuation state)` Determine if the specified continuation object is valid to resume with.
`TrainingContinuation`	`pause()` Pause the training.
`void`	`resume(TrainingContinuation state)` Resume training.
`void`	`setLearningRate(double rate)` Set the learning rate, this is value is essentially a percent.
`void`	`setMomentum(double m)` Set the momentum for training.
`double`	`updateWeight(double[] gradients, double[] lastGradient, int index)` Update a weight.