* Update weights of the SOM using Kohonen algorithm * * \param[in] x data point * \param[in,out] W weights matrix * \param[in,out] D temporary vector to store distances * \param[in] num_out number of output points * \param[in] num_features number of features per input sample * \param[in] alpha learning rate \f$0<\alpha\le1\f$ * \param[in] R neighborhood range */
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