ClassificationModulesExamples/SoftmaxExample/SoftmaxExample.cpp

The Softmax Classifier is a simple but effective classifier (based on logisitc regression) that works well on problems that are linearly separable.GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Remarks

This implementation is based on Bishop, Christopher M. Pattern recognition and machine learning. Vol. 1. New York: springer, 2006.

/*
 GRT MIT License
 Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software 
 and associated documentation files (the "Software"), to deal in the Software without restriction, 
 including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, 
 and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, 
 subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all copies or substantial 
 portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT 
 LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, 
 WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE 
 SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 */

/*
 GRT Softmax Example
 This examples demonstrates how to initialize, train, and use the Softmax algorithm for classification.
 
 The Softmax classifier is a simple but effective classifier based on logisitic regression that works well on problems that are linearly separable.
 
 In this example we create an instance of an Softmax algorithm and then train the algorithm using some pre-recorded training data.
 The trained Softmax classification model is then used to predict the class label of some test data.
 
 This example shows you how to:
 - Create an initialize the Softmax algorithm
 - Load some ClassificationData from a file and partition the training data into a training dataset and a test dataset
 - Train the Softmax algorithm using the training dataset
 - Test the Softmax 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 Softmax instance
    Softmax softmax;
    
    //Load some training data to train the classifier
    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( !softmax.train( trainingData ) ){
        cout << "Failed to train classifier!\n";
        return EXIT_FAILURE;
    }
    
    //Save the Softmax model to a file
    if( !softmax.save("SoftmaxModel.grt") ){
        cout << "Failed to save the classifier model!\n";
        return EXIT_FAILURE;
    }
    
    //Load the Softmax model from a file
    if( !softmax.load("SoftmaxModel.grt") ){
        cout << "Failed to load the classifier model!\n";
        return EXIT_FAILURE;
    }
    
    //Use the test dataset to test the softmax 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
        if( !softmax.predict( inputVector ) ){
            cout << "Failed to perform prediction for test sample: " << i <<"\n";
            return EXIT_FAILURE;
        }
        
        //Get the predicted class label
        UINT predictedClassLabel = softmax.getPredictedClassLabel();
        VectorFloat classLikelihoods = softmax.getClassLikelihoods();
        VectorFloat classDistances = softmax.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;
}