ist]]]) -> retval, results, neighborResponses, dist
.   @brief Finds the neighbors and predicts responses for input vectors.
.   
.       @param samples Input samples stored by rows. It is a single-precision floating-point matrix of
.           `<number_of_samples> * k` size.
.       @param k Number of used nearest neighbors. Should be greater than 1.
.       @param results Vector with results of prediction (regression or classification) for each input
.           sample. It is a single-precision floating-point vector with `<number_of_samples>` elements.
.       @param neighborResponses Optional output values for corresponding neighbors. It is a single-
.           precision floating-point matrix of `<number_of_samples> * k` size.
.       @param dist Optional output distances from the input vectors to the corresponding neighbors. It
.           is a single-precision floating-point matrix of `<number_of_samples> * k` size.
.   
.       For each input vector (a row of the matrix samples), the method finds the k nearest neighbors.
.       In case of regression, the predicted result is a mean value of the particular vector's neighbor
.       responses. In case of classification, the class is determined by voting.
.   
.       For each input vector, the neighbors are sorted by their distances to the vector.
.   
.       In case of C++ interface you can use output pointers to empty matrices and the function will
.       allocate memory itself.
.   
.       If only a single input vector is passed, all output matrices are optional and the predicted
.       value is returned by the method.
.   
.       The function is parallelized with the TBB library.