bool SelfOrganizingMap::train_( MatrixFloat &data ){
//Clear any previous models
clear();
const UINT M = data.getNumRows();
const UINT N = data.getNumCols();
numInputDimensions = N;
numOutputDimensions = numClusters*numClusters;
Random rand;
//Setup the neurons
neurons.resize( numClusters, numClusters );
if( neurons.getSize() != numClusters*numClusters ){
errorLog << "train_( MatrixFloat &data ) - Failed to resize neurons matrix, there might not be enough memory!" << std::endl;
return false;
}
//Init the neurons
for(UINT i=0; i<numClusters; i++){
for(UINT j=0; j<numClusters; j++){
neurons[i][j].init( N, 0.5, SOM_MIN_TARGET, SOM_MAX_TARGET );
}
}
//Scale the data if needed
ranges = data.getRanges();
if( useScaling ){
for(UINT i=0; i<M; i++){
for(UINT j=0; j<numInputDimensions; j++){
data[i][j] = scale(data[i][j],ranges[j].minValue,ranges[j].maxValue,SOM_MIN_TARGET,SOM_MAX_TARGET);
}
}
}
Float error = 0;
Float lastError = 0;
Float trainingSampleError = 0;
Float delta = 0;
Float minChange = 0;
Float weightUpdate = 0;
Float alpha = 1.0;
Float neuronDiff = 0;
Float neuronWeightFunction = 0;
Float gamma = 0;
UINT iter = 0;
bool keepTraining = true;
VectorFloat trainingSample;
Vector< UINT > randomTrainingOrder(M);
//In most cases, the training data is grouped into classes (100 samples for class 1, followed by 100 samples for class 2, etc.)
//This can cause a problem for stochastic gradient descent algorithm. To avoid this issue, we randomly shuffle the order of the
//training samples. This random order is then used at each epoch.
for(UINT i=0; i<M; i++){
randomTrainingOrder[i] = i;
}
std::random_shuffle(randomTrainingOrder.begin(), randomTrainingOrder.end());
//Enter the main training loop
while( keepTraining ){
//Update alpha based on the current iteration
alpha = Util::scale(iter,0,maxNumEpochs,alphaStart,alphaEnd);
//Run one epoch of training using the online best-matching-unit algorithm
error = 0;
for(UINT m=0; m<M; m++){
trainingSampleError = 0;
//Get the i'th random training sample
trainingSample = data.getRowVector( randomTrainingOrder[m] );
//Find the best matching unit
Float dist = 0;
Float bestDist = grt_numeric_limits< Float >::max();
UINT bestIndexRow = 0;
UINT bestIndexCol = 0;
for(UINT i=0; i<numClusters; i++){
for(UINT j=0; j<numClusters; j++){
dist = neurons[i][j].getSquaredWeightDistance( trainingSample );
if( dist < bestDist ){
bestDist = dist;
bestIndexRow = i;
bestIndexCol = j;
}
}
}
error += bestDist;
//Update the weights based on the distance to the winning neuron
//Neurons closer to the winning neuron will have their weights update more
const Float bir = bestIndexRow;
const Float bic = bestIndexCol;
for(UINT i=0; i<numClusters; i++){
for(UINT j=0; j<numClusters; j++){
//Update the weights for all the neurons, pulling them a little closer to the input example
neuronDiff = 0;
gamma = 2.0 * grt_sqr( numClusters * sigmaWeight );
neuronWeightFunction = exp( -grt_sqr(bir-i)/gamma ) * exp( -grt_sqr(bic-j)/gamma );
//std::cout << "best index: " << bestIndexRow << " " << bestIndexCol << " bestDist: " << bestDist << " pos: " << i << " " << j << " neuronWeightFunction: " << neuronWeightFunction << std::endl;
for(UINT n=0; n<N; n++){
neuronDiff = trainingSample[n] - neurons[i][j][n];
weightUpdate = neuronWeightFunction * alpha * neuronDiff;
neurons[i][j][n] += weightUpdate;
}
}
}
}
error = error / M;
trainingLog << "iter: " << iter << " average error: " << error << std::endl;
//Compute the error
delta = fabs( error-lastError );
lastError = error;
//Check to see if we should stop
if( delta <= minChange && false ){
converged = true;
keepTraining = false;
}
if( grt_isinf( error ) ){
errorLog << "train_(MatrixFloat &data) - Training failed! Error is NAN!" << std::endl;
return false;
}
if( ++iter >= maxNumEpochs ){
keepTraining = false;
}
trainingLog << "Epoch: " << iter << " Squared Error: " << error << " Delta: " << delta << " Alpha: " << alpha << std::endl;
}
numTrainingIterationsToConverge = iter;
trained = true;
return true;
}
bool SelfOrganizingMap::train_( MatrixFloat &data ){
//Clear any previous models
clear();
const UINT M = data.getNumRows();
const UINT N = data.getNumCols();
numInputDimensions = N;
numOutputDimensions = numClusters;
Random rand;
//Setup the neurons
neurons.resize( numClusters );
if( neurons.size() != numClusters ){
errorLog << "train_( MatrixFloat &data ) - Failed to resize neurons Vector, there might not be enough memory!" << std::endl;
return false;
}
for(UINT j=0; j<numClusters; j++){
//Init the neuron
neurons[j].init( N, 0.5 );
//Set the weights as a random training example
neurons[j].weights = data.getRowVector( rand.getRandomNumberInt(0, M) );
}
//Setup the network weights
switch( networkTypology ){
case RANDOM_NETWORK:
networkWeights.resize(numClusters, numClusters);
//Set the diagonal weights as 1 (as i==j)
for(UINT i=0; i<numClusters; i++){
networkWeights[i][i] = 1;
}
//Randomize the other weights
UINT indexA = 0;
UINT indexB = 0;
Float weight = 0;
for(UINT i=0; i<numClusters*numClusters; i++){
indexA = rand.getRandomNumberInt(0, numClusters);
indexB = rand.getRandomNumberInt(0, numClusters);
//Make sure the two random indexs are the same (as this is a diagonal and should be 1)
if( indexA != indexB ){
//Pick a random weight between these two neurons
weight = rand.getRandomNumberUniform(0,1);
//The weight betwen neurons a and b is the mirrored
networkWeights[indexA][indexB] = weight;
networkWeights[indexB][indexA] = weight;
}
}
break;
}
//Scale the data if needed
ranges = data.getRanges();
if( useScaling ){
for(UINT i=0; i<M; i++){
for(UINT j=0; j<numInputDimensions; j++){
data[i][j] = scale(data[i][j],ranges[j].minValue,ranges[j].maxValue,0,1);
}
}
}
Float error = 0;
Float lastError = 0;
Float trainingSampleError = 0;
Float delta = 0;
Float minChange = 0;
Float weightUpdate = 0;
Float weightUpdateSum = 0;
Float alpha = 1.0;
Float neuronDiff = 0;
UINT iter = 0;
bool keepTraining = true;
VectorFloat trainingSample;
Vector< UINT > randomTrainingOrder(M);
//In most cases, the training data is grouped into classes (100 samples for class 1, followed by 100 samples for class 2, etc.)
//This can cause a problem for stochastic gradient descent algorithm. To avoid this issue, we randomly shuffle the order of the
//training samples. This random order is then used at each epoch.
for(UINT i=0; i<M; i++){
randomTrainingOrder[i] = i;
}
std::random_shuffle(randomTrainingOrder.begin(), randomTrainingOrder.end());
//Enter the main training loop
while( keepTraining ){
//Update alpha based on the current iteration
alpha = Util::scale(iter,0,maxNumEpochs,alphaStart,alphaEnd);
//Run one epoch of training using the online best-matching-unit algorithm
error = 0;
for(UINT i=0; i<M; i++){
trainingSampleError = 0;
//Get the i'th random training sample
trainingSample = data.getRowVector( randomTrainingOrder[i] );
//Find the best matching unit
Float dist = 0;
Float bestDist = grt_numeric_limits< Float >::max();
UINT bestIndex = 0;
for(UINT j=0; j<numClusters; j++){
dist = neurons[j].getSquaredWeightDistance( trainingSample );
if( dist < bestDist ){
bestDist = dist;
bestIndex = j;
}
}
//Update the weights based on the distance to the winning neuron
//Neurons closer to the winning neuron will have their weights update more
for(UINT j=0; j<numClusters; j++){
//Update the weights for the j'th neuron
weightUpdateSum = 0;
neuronDiff = 0;
for(UINT n=0; n<N; n++){
neuronDiff = trainingSample[n] - neurons[j][n];
weightUpdate = networkWeights[bestIndex][j] * alpha * neuronDiff;
neurons[j][n] += weightUpdate;
weightUpdateSum += neuronDiff;
}
trainingSampleError += grt_sqr( weightUpdateSum );
}
error += grt_sqrt( trainingSampleError / numClusters );
}
//Compute the error
delta = fabs( error-lastError );
lastError = error;
//Check to see if we should stop
if( delta <= minChange ){
converged = true;
keepTraining = false;
}
if( grt_isinf( error ) ){
errorLog << "train_(MatrixFloat &data) - Training failed! Error is NAN!" << std::endl;
return false;
}
if( ++iter >= maxNumEpochs ){
keepTraining = false;
}
trainingLog << "Epoch: " << iter << " Squared Error: " << error << " Delta: " << delta << " Alpha: " << alpha << std::endl;
}
numTrainingIterationsToConverge = iter;
trained = true;
return true;
}