本文整理汇总了C++中LinearRegression::setUseValidationSet方法的典型用法代码示例。如果您正苦于以下问题：C++ LinearRegression::setUseValidationSet方法的具体用法？C++ LinearRegression::setUseValidationSet怎么用？C++ LinearRegression::setUseValidationSet使用的例子？那么, 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LinearRegression的用法示例。
在下文中一共展示了LinearRegression::setUseValidationSet方法的1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C++代码示例。
示例1: train
bool train( CommandLineParser &parser ){
infoLog << "Training regression model..." << endl;
string trainDatasetFilename = "";
string modelFilename = "";
float learningRate = 0;
float minChange = 0;
unsigned int maxEpoch = 0;
unsigned int batchSize = 0;
//Get the filename
if( !parser.get("filename",trainDatasetFilename) ){
errorLog << "Failed to parse filename from command line! You can set the filename using the -f." << endl;
printHelp();
return false;
}
//Get the parameters from the parser
parser.get("model-filename",modelFilename);
parser.get( "learning-rate", learningRate );
parser.get( "min-change", minChange );
parser.get( "max-epoch", maxEpoch );
parser.get( "batch-size", batchSize );
infoLog << "settings: learning-rate: " << learningRate << " min-change: " << minChange << " max-epoch: " << maxEpoch << " batch-size: " << batchSize << endl;
//Load the training data to train the model
RegressionData trainingData;
//Try and parse the input and target dimensions
unsigned int numInputDimensions = 0;
unsigned int numTargetDimensions = 0;
if( parser.get("num-inputs",numInputDimensions) && parser.get("num-targets",numTargetDimensions) ){
infoLog << "num input dimensions: " << numInputDimensions << " num target dimensions: " << numTargetDimensions << endl;
trainingData.setInputAndTargetDimensions( numInputDimensions, numTargetDimensions );
}
if( (numInputDimensions == 0 || numTargetDimensions == 0) && Util::stringEndsWith( trainDatasetFilename, ".csv" ) ){
errorLog << "Failed to parse num input dimensions and num target dimensions from input arguments. You must supply the input and target dimensions if the data format is CSV!" << endl;
printHelp();
return false;
}
infoLog << "- Loading Training Data..." << endl;
if( !trainingData.load( trainDatasetFilename ) ){
errorLog << "Failed to load training data!\n";
return false;
}
const unsigned int N = trainingData.getNumInputDimensions();
const unsigned int T = trainingData.getNumTargetDimensions();
infoLog << "- Num training samples: " << trainingData.getNumSamples() << endl;
infoLog << "- Num input dimensions: " << N << endl;
infoLog << "- Num target dimensions: " << T << endl;
//Create a new regression instance
LinearRegression regression;
regression.setMaxNumEpochs( maxEpoch );
regression.setMinChange( minChange );
regression.setUseValidationSet( true );
regression.setValidationSetSize( 20 );
regression.setRandomiseTrainingOrder( true );
regression.enableScaling( true );
//Create a new pipeline that will hold the regression algorithm
GestureRecognitionPipeline pipeline;
//Add a multidimensional regression instance and set the regression algorithm to Linear Regression
pipeline.setRegressifier( MultidimensionalRegression( regression, true ) );
infoLog << "- Training model...\n";
//Train the classifier
if( !pipeline.train( trainingData ) ){
errorLog << "Failed to train model!" << endl;
return false;
}
infoLog << "- Model trained!" << endl;
infoLog << "- Saving model to: " << modelFilename << endl;
//Save the pipeline
if( pipeline.save( modelFilename ) ){
infoLog << "- Model saved." << endl;
}else warningLog << "Failed to save model to file: " << modelFilename << endl;
infoLog << "- TrainingTime: " << pipeline.getTrainingTime() << endl;
return true;
}