double predict(Database* db, int roadIndex, const Item& item)
{
double jc = (double)db->getJamCount(roadIndex);
double var = cluster->getVar(db, roadIndex);
//double A = 480;
//double B = 520;
double A = 780;
double B = 820;
if ( midAvg == 0 )
{
midAvg = new KNN(db, LOWER_LIMIT, UPPER_LIMIT, cluster);
midAvg->initialize(db);
}
if ( smallAvg == 0 )
{
smallAvg = new SmallAverage(db, cluster, LOWER_LIMIT);
smallAvg->initialize(db);
}
/* if ( jc >= 30 && var >= 25.0 )
{
return midAvg->predict(db, roadIndex, item);
}*/
if ( jc > B )
{
return midAvg->predict(db, roadIndex, item);
}
// return 50.0;
if ( jc < A )
{
return smallAvg->predict(db, roadIndex, item);
}
double mid = midAvg->predict(db, roadIndex, item);
double small = smallAvg->predict(db, roadIndex, item);
double K = (jc - A)/(B - A);
double res = small*(1.0 - K) + mid*K;
return res;
}
// Tests the learning algorithm on a basic dataset
TEST(KNN, TrainBasicDataset) {
KNN knn;
//Check the module is not trained
EXPECT_TRUE( !knn.getTrained() );
//Generate a basic dataset
const UINT numSamples = 1000;
const UINT numClasses = 10;
const UINT numDimensions = 10;
ClassificationData::generateGaussDataset( "gauss_data.csv", numSamples, numClasses, numDimensions, 10, 1 );
ClassificationData trainingData;
EXPECT_TRUE( trainingData.load( "gauss_data.csv" ) );
ClassificationData testData = trainingData.split( 50 );
//Train the classifier
EXPECT_TRUE( knn.train( trainingData ) );
EXPECT_TRUE( knn.getTrained() );
EXPECT_TRUE( knn.print() );
for(UINT i=0; i<testData.getNumSamples(); i++){
EXPECT_TRUE( knn.predict( testData[i].getSample() ) );
}
EXPECT_TRUE( knn.save( "knn_model.grt" ) );
knn.clear();
EXPECT_TRUE( !knn.getTrained() );
EXPECT_TRUE( knn.load( "knn_model.grt" ) );
EXPECT_TRUE( knn.getTrained() );
for(UINT i=0; i<testData.getNumSamples(); i++){
EXPECT_TRUE( knn.predict( testData[i].getSample() ) );
}
}
