/usr/include/torch/KMeans.h is in libtorch3-dev 3.1-2.2.
This file is owned by root:root, with mode 0o644.
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//
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#ifndef KMEANS_INC
#define KMEANS_INC
#include "DiagonalGMM.h"
namespace Torch {
/** This class can be used to do a "kmeans" on a given set of data.
It has been implemented in the framework of a Distribution that can
be trained with EM. This means that the kmeans distance is in fact
returned by the method logProbability.
Note that as KMeans is a subclass of DiagonalGMM, they share the same
parameter structure. Hence, a DiagonalGMM can be easily initialized by
a KMeans.
@author Samy Bengio (bengio@idiap.ch)
*/
class KMeans : public DiagonalGMM
{
public:
/// for each example, keep the index of the neirest cluster
Sequence* min_cluster;
/// initialize the parameters from the data set to false if you
/// load the data.
bool initialize_parameters;
///
KMeans(int n_inputs, int n_gaussians_);
virtual void setDataSet(DataSet* data_);
virtual void eMIterInitialize();
virtual void frameEMAccPosteriors(int t, real *inputs, real log_posterior);
virtual void eMUpdate();
virtual void frameBackward(int t, real *f_inputs, real *beta_, real *f_outputs, real *alpha_);
virtual void eMSequenceInitialize(Sequence* inputs);
/** note that this method returns in fact the euclidean distance between
the observation and the neirest cluster
*/
virtual real frameLogProbability(int t, real *inputs);
/** similarly to frameLogProbability, this method returns the euclidean
distance between cluster g and the given observation
*/
virtual real frameLogProbabilityOneGaussian(int g, real *inputs);
virtual ~KMeans();
};
}
#endif
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