本文整理汇总了C++中MatrixDouble::scale方法的典型用法代码示例。如果您正苦于以下问题：C++ MatrixDouble::scale方法的具体用法？C++ MatrixDouble::scale怎么用？C++ MatrixDouble::scale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MatrixDouble的用法示例。
在下文中一共展示了MatrixDouble::scale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: trainModel
bool KMeans::trainModel(MatrixDouble &data){
if( numClusters == 0 ){
errorLog << "trainModel(MatrixDouble &data) - Failed to train model. NumClusters is zero!" << endl;
return false;
}
if( clusters.getNumRows() != numClusters ){
errorLog << "trainModel(MatrixDouble &data) - Failed to train model. The number of rows in the cluster matrix does not match the number of clusters! You should need to initalize the clusters matrix first before calling this function!" << endl;
return false;
}
if( clusters.getNumCols() != numInputDimensions ){
errorLog << "trainModel(MatrixDouble &data) - Failed to train model. The number of columns in the cluster matrix does not match the number of input dimensions! You should need to initalize the clusters matrix first before calling this function!" << endl;
return false;
}
Timer timer;
UINT currentIter = 0;
UINT numChanged = 0;
bool keepTraining = true;
double theta = 0;
double lastTheta = 0;
double delta = 0;
double startTime = 0;
thetaTracker.clear();
finalTheta = 0;
numTrainingIterationsToConverge = 0;
trained = false;
converged = false;
//Scale the data if needed
ranges = data.getRanges();
if( useScaling ){
data.scale(0,1);
}
//Init the assign and count vectors
//Assign is set to K+1 so that the nChanged values in the eStep at the first iteration will be updated correctly
for(UINT m=0; m<numTrainingSamples; m++) assign[m] = numClusters+1;
for(UINT k=0; k<numClusters; k++) count[k] = 0;
//Run the training loop
timer.start();
while( keepTraining ){
startTime = timer.getMilliSeconds();
//Compute the E step
numChanged = estep( data );
//Compute the M step
mstep( data );
//Update the iteration counter
currentIter++;
//Compute theta if needed
if( computeTheta ){
theta = calculateTheta(data);
delta = lastTheta - theta;
lastTheta = theta;
}else theta = delta = 0;
//Check convergance
if( numChanged == 0 && currentIter > minNumEpochs ){ converged = true; keepTraining = false; }
if( currentIter >= maxNumEpochs ){ keepTraining = false; }
if( fabs( delta ) < minChange && computeTheta && currentIter > minNumEpochs ){ converged = true; keepTraining = false; }
if( computeTheta ) thetaTracker.push_back( theta );
trainingLog << "Epoch: " << currentIter << "/" << maxNumEpochs;
trainingLog << " Epoch time: " << (timer.getMilliSeconds()-startTime)/1000.0 << " seconds";
trainingLog << " Theta: " << theta << " Delta: " << delta << endl;
}
trainingLog << "Model Trained at epoch: " << currentIter << " with a theta value of: " << theta << endl;
finalTheta = theta;
numTrainingIterationsToConverge = currentIter;
trained = true;
return true;
}