void SoftmaxRegressionFunction::InitializeWeights(
arma::mat &weights,
const size_t featureSize,
const size_t numClasses,
const bool fitIntercept)
{
// Initialize values to 0.005 * r. 'r' is a matrix of random values taken from
// a Gaussian distribution with mean zero and variance one.
// If the fitIntercept flag is true, parameters.col(0) is the intercept.
if (fitIntercept)
weights.randn(numClasses, featureSize + 1);
else
weights.randn(numClasses, featureSize);
weights *= 0.005;
}
/**
* Initialize the dictionary randomly from a normal distribution, such that
* each atom has a norm of 1. This is simple enough to be included with the
* definition.
*
* @param data Dataset to use for initialization.
* @param atoms Number of atoms (columns) in the dictionary.
* @param dictionary Dictionary to initialize.
*/
static void Initialize(const arma::mat& data,
const size_t atoms,
arma::mat& dictionary)
{
// Create random dictionary.
dictionary.randn(data.n_rows, atoms);
// Normalize each atom.
for (size_t j = 0; j < atoms; ++j)
dictionary.col(j) /= norm(dictionary.col(j), 2);
}