ClassificationModulesExamples/SVMExample/SVMExample.cpp

This class acts as a front end for the LIBSVM library (http://www.csie.ntu.edu.tw/~cjlin/libsvm/). It implements a Support Vector Machine (SVM) classifier, a powerful classifier that works well on a wide range of classification problems, particularly on more complex problems that other classifiers (such as the KNN, GMM or ANBC algorithms) might not be able to solve.

Remarks

This implementation is a wrapper for LIBSVM: Chang, Chih-Chung, and Chih-Jen Lin. "LIBSVM: a library for support vector machines." ACM Transactions on Intelligent Systems and Technology (TIST) 2, no. 3 (2011): 27.

/*
 GRT MIT License
 Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>
 
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/*
 GRT SVM Example
 his examples demonstrates how to initialize, train, and use the SVM algorithm for classification. 
 
 The Support Vector Machine (SVM) classifier is a powerful classifier that works well on a wide range of classification problems, even problems in high dimensions and that are not linearly separable. 
 
 In this example we create an instance of a SVM algorithm and then train the algorithm using some pre-recorded training data.
 The trained SVM algorithm is then used to predict the class label of some test data.
 
 This example shows you how to:
 - Create an initialize the SVM algorithm using a LINEAR kernel
 - Load some ClassificationData from a file and partition the training data into a training dataset and a test dataset
 - Train the SVM algorithm using the training dataset
 - Test the SVM algorithm using the test dataset
 - Manually compute the accuracy of the classifier

You should run this example with one argument pointing to the data you want to load. A good dataset to run this example is acc-orientation.grt, which can be found in the GRT data folder.
*/

//You might need to set the specific path of the GRT header relative to your project
#include <GRT/GRT.h>
using namespace GRT;
using namespace std;

int main (int argc, const char * argv[])
{
 //Parse the data filename from the argument list
 if( argc != 2 ){
 cout << "Error: failed to parse data filename from command line. You should run this example with one argument pointing to the data filename!\n";
 return EXIT_FAILURE;
 }
 const string filename = argv[1];

 //Create a new SVM classifier with a linear kernel
 //Other kernel options you could choose are: POLY_KERNEL, RBF_KERNEL, SIGMOID_KERNEL, PRECOMPUTED_KERNEL
 SVM svm(SVM::LINEAR_KERNEL);
 
 //The SVM will typically work much better if we scale the training and prediction data, so turn scaling on
 svm.enableScaling( true );
 
 //Train the classifier with some training data
 ClassificationData trainingData;
 
 if( !trainingData.load( filename ) ){
 cout << "Failed to load training data: " << filename << endl;
 return EXIT_FAILURE;
 }
 
 //Use 20% of the training dataset to create a test dataset
 ClassificationData testData = trainingData.split( 80 );
 
 //Train the classifier
 if( !svm.train( trainingData ) ){
 cout << "Failed to train classifier!\n";
 return EXIT_FAILURE;
 }
 
 //Save the svm model to a file
 if( !svm.save("SVMModel.grt") ){
 cout << "Failed to save the classifier model!\n";
 return EXIT_FAILURE;
 }
 
 //Load the knn model from a file
 if( !svm.load("SVMModel.grt") ){
 cout << "Failed to load the classifier model!\n";
 return EXIT_FAILURE;
 }
 
 //Use the test dataset to test the SVM model
 double accuracy = 0;
 for(UINT i=0; i<testData.getNumSamples(); i++){
 //Get the i'th test sample
 UINT classLabel = testData[i].getClassLabel();
 VectorFloat inputVector = testData[i].getSample();
 
 //Perform a prediction using the classifier
 bool predictSuccess = svm.predict( inputVector );
 
 if( !predictSuccess ){
 cout << "Failed to perform prediction for test sampel: " << i <<"\n";
 return EXIT_FAILURE;
 }
 
 //Get the predicted class label
 UINT predictedClassLabel = svm.getPredictedClassLabel();
 VectorFloat classLikelihoods = svm.getClassLikelihoods();
 VectorFloat classDistances = svm.getClassDistances();
 
 //Update the accuracy
 if( classLabel == predictedClassLabel ) accuracy++;
 
 cout << "TestSample: " << i << " ClassLabel: " << classLabel << " PredictedClassLabel: " << predictedClassLabel << endl;
 }
 
 cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
 
 return EXIT_SUCCESS;
}