C++ GestureRecognitionPipeline::getTrainingTime方法代码示例

bool train( CommandLineParser &parser ){

    infoLog << "Training regression model..." << endl;

    string trainDatasetFilename = "";
    string modelFilename = "";
    string defaultFilename = "linear-regression-model.grt";
    bool removeFeatures = false;
    bool defaultRemoveFeatures = false;

    //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;
        printUsage();
        return false;
    }

    //Get the model filename
    parser.get("model-filename",modelFilename,defaultFilename);

    //Load the training data to train the model
    RegressionData trainingData;

    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
    LogisticRegression regression;

    regression.setMaxNumEpochs( 500 );
    regression.setMinChange( 1.0e-5 );
    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;
}

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
    LogisticRegression 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;
}

//.........这里部分代码省略.........
    if( !parser.get("filename",trainDatasetFilename) ){
        errorLog << "Failed to parse filename from command line! You can set the filename using the -f." << endl;
        printUsage();
        return false;
    }

    //Get the model filename
    parser.get("model-filename",modelFilename);

    //Get the forest size
    parser.get("forest-size",forestSize);

    //Get the max depth
    parser.get("max-depth",maxDepth);

    //Get the min node size
    parser.get("min-node-size",minNodeSize);

    //Get the number of random splits
    parser.get("num-splits",numSplits);
    
    //Get the remove features
    parser.get("remove-features",removeFeatures);
   
    //Get the bootstrap weight 
    parser.get("bootstrap-weight",bootstrapWeight);

    //Load some training data to train the classifier
    ClassificationData trainingData;

    infoLog << "- Loading Training Data..." << endl;
    if( !trainingData.load( trainDatasetFilename ) ){
        errorLog << "Failed to load training data!\n";
        return false;
    }

    const unsigned int N = trainingData.getNumDimensions();
    Vector< ClassTracker > tracker = trainingData.getClassTracker();
    infoLog << "- Num training samples: " << trainingData.getNumSamples() << endl;
    infoLog << "- Num dimensions: " << N << endl;
    infoLog << "- Num classes: " << trainingData.getNumClasses() << endl;
    infoLog << "- Class stats: " << endl;
    for(unsigned int i=0; i<tracker.getSize(); i++){
        infoLog << "- class " << tracker[i].classLabel << " number of samples: " << tracker[i].counter << endl;
    }
    
    //Create a new RandomForests instance
    RandomForests forest;

    //Set the decision tree node that will be used for each tree in the forest
    string nodeType = "cluster-node"; //TODO: make this a command line option in the future
    if( nodeType == "cluster-node" ){
        forest.setDecisionTreeNode( DecisionTreeClusterNode() );
    }
    if( nodeType == "threshold-node" ){
        forest.setTrainingMode( Tree::BEST_RANDOM_SPLIT );
        forest.setDecisionTreeNode( DecisionTreeThresholdNode() );
    }

    //Set the number of trees in the forest
    forest.setForestSize( forestSize );

    //Set the maximum depth of the tree
    forest.setMaxDepth( maxDepth );

    //Set the minimum number of samples allowed per node
    forest.setMinNumSamplesPerNode( minNodeSize );

    //Set the number of random splits used per node
    forest.setNumRandomSplits( numSplits );

    //Set if selected features should be removed at each node
    forest.setRemoveFeaturesAtEachSplit( removeFeatures );

    //Set the bootstrap weight
    forest.setBootstrappedDatasetWeight( bootstrapWeight );

    //Add the classifier to a pipeline
    GestureRecognitionPipeline pipeline;
    pipeline.setClassifier( forest );

    infoLog << "- Training model..." << endl;

    //Train the classifier
    if( !pipeline.train( trainingData ) ){
        errorLog << "Failed to train classifier!" << endl;
        return false;
    }

    infoLog << "- Model trained!" << endl;
    infoLog << "- Training time: " << (pipeline.getTrainingTime() * 0.001) / 60.0 << " (minutes)" << endl;
    infoLog << "- Saving model to: " << modelFilename << endl;

    //Save the pipeline
    if( !pipeline.save( modelFilename ) ){
        warningLog << "Failed to save model to file: " << modelFilename << endl;
    } 

    return true;
}

bool train( CommandLineParser &parser ){

    infoLog << "Training regression model..." << endl;

    string trainDatasetFilename = "";
    string modelFilename = "";

    //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 model filename
    parser.get("model-filename",modelFilename);

    //Load the training data to train the model
    ClassificationData trainingData;

    infoLog << "- Loading Training Data..." << endl;
    if( !trainingData.load( trainDatasetFilename ) ){
        errorLog << "Failed to load training data!\n";
        return false;
    }

    const unsigned int N = trainingData.getNumDimensions();
    const unsigned int K = trainingData.getNumClasses();
    infoLog << "- Num training samples: " << trainingData.getNumSamples() << endl;
    infoLog << "- Num input dimensions: " << N << endl;
    infoLog << "- Num classes: " << K << endl;
    
    float learningRate = 0;
    float minChange = 0;
    unsigned int maxEpoch = 0;
    unsigned int batchSize = 0;

    parser.get( "learning-rate", learningRate );
    parser.get( "min-change", minChange );
    parser.get( "max-epoch", maxEpoch );
    parser.get( "batch-size", batchSize );

    infoLog << "Softmax settings: learning-rate: " << learningRate << " min-change: " << minChange << " max-epoch: " << maxEpoch << " batch-size: " << batchSize << endl;

    //Create a new softmax instance
    bool enableScaling = true;
    Softmax classifier(enableScaling,learningRate,minChange,maxEpoch,batchSize);

    //Create a new pipeline that will hold the classifier
    GestureRecognitionPipeline pipeline;

    //Add the classifier to the pipeline
    pipeline << classifier;

    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;
    
    string logFilename = "";
    if( parser.get( "log-filename", logFilename ) && logFilename.length() > 0 ){
      infoLog << "Writing training log to: " << logFilename << endl;

      fstream logFile( logFilename.c_str(), fstream::out );

      if( !logFile.is_open() ){
        errorLog << "Failed to open training log file: " << logFilename << endl;
        return false;
      }

      Vector< TrainingResult > trainingResults = pipeline.getTrainingResults();

      for(UINT i=0; i<trainingResults.getSize(); i++){
        logFile << trainingResults[i].getTrainingIteration() << "\t" << trainingResults[i].getAccuracy() << endl;
      }

      logFile.close();
    }

    return true;
}