/**
* Set value in row and column.
*/
int Table::setValue(int value, int row, int col) {
int pos;
int ret = -1;
if (validated && row >= 0 && row < height) {
// was the value added
if (!lines[row]->setValue(value, col)) {
// check if not violates table
if ((value ? fullRow(row) : emptyRow(row)) ||
(value ? fullColumn(col) : emptyColumn(col)) ||
duplicatedRow(row, &pos))
lines[row]->setValue((value ? 0 : 1), col);
else
ret = 0; // no error
}
}
return ret;
}
// virtual
void GNaiveBayes::trainSparse(GSparseMatrix& features, GMatrix& labels)
{
if(features.rows() != labels.rows())
throw Ex("Expected the features and labels to have the same number of rows");
size_t featureDims = features.cols();
GUniformRelation featureRel(featureDims, 2);
beginIncrementalLearning(featureRel, labels.relation());
GVec fullRow(featureDims);
for(size_t n = 0; n < features.rows(); n++)
{
features.fullRow(fullRow, n);
for(size_t i = 0; i < featureDims; i++)
{
if(fullRow[i] < 1e-6)
fullRow[i] = 0.0;
else
fullRow[i] = 1.0;
}
trainIncremental(fullRow, labels[n]);
}
}
