The algorithm caches all training samples and predicts the response for a new sample by analyzing a certain number (**K**) of the nearest neighbors of the sample using voting, calculating weighted sum, and so on. The method is sometimes referred to as "learning by example" because for prediction it looks for the feature vector with a known response that is closest to the given vector.
:param params:The model parameters: default number of neighbors to use in predict method (in ``KNearest::findNearest`` this number must be passed explicitly) and the flag on whether classification or regression model should be trained.
The static method creates empty KNearest classifier. It should be then trained using ``train`` method (see ``StatModel::train``). Alternatively, you can load boost model from file using ``StatModel::load<KNearest>(filename)``.
: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.
