The algorithm caches all training samples and predicts the response for a new sample by analyzing a certain number (
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.
**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.
CvKNearest
CvKNearest
----------
----------
.. ocv:class:: CvKNearest
.. ocv:class:: CvKNearest
K-Nearest Neighbors model. ::
The class implements K-Nearest Neighbors model as described in the beginning of this section.
:param isRegression: Type of the problem: ``true`` for regression and ``false`` for classification.
:param maxK: Number of maximum neighbors that may be passed to the method :ocv:func:`CvKNearest::find_nearest`.
The method trains the K-Nearest model. It follows the conventions of the generic ``train`` approach with the following limitations:
:param updateBase: Specifies whether the model is trained from scratch (``update_base=false``), or it is updated using the new training data (``update_base=true``). In the latter case, the parameter ``maxK`` must not be larger than the original value.
The method trains the K-Nearest model. It follows the conventions of the generic :ocv:func:`CvStataModel::train` approach with the following limitations:
* Only ``CV_ROW_SAMPLE`` data layout is supported.
* Only ``CV_ROW_SAMPLE`` data layout is supported.
* Input variables are all ordered.
* Input variables are all ordered.
* Output variables can be either categorical ( ``is_regression=false`` ) or ordered ( ``is_regression=true`` ).
* Output variables can be either categorical ( ``is_regression=false`` ) or ordered ( ``is_regression=true`` ).
* Variable subsets ( ``var_idx`` ) and missing measurements are not supported.
* Variable subsets (``var_idx``) and missing measurements are not supported.
The parameter ``_max_k`` specifies the number of maximum neighbors that may be passed to the method ``find_nearest`` .
The parameter ``_update_base`` specifies whether the model is trained from scratch
( ``_update_base=false`` ), or it is updated using the new training data ( ``_update_base=true`` ). In the latter case, the parameter ``_max_k`` must not be larger than the original value.
CvKNearest::find_nearest
CvKNearest::find_nearest
------------------------
------------------------
Finds the neighbors for input vectors.
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 :math:`number\_of\_samples \times number\_of\_features` size.
:param k: Number of used nearest neighbors. It must satisfy constraint: :math:`k \le` :ocv:func:`CvKNearest::get_max_k`.
For each input vector (a row of the matrix ``_samples`` ), the method finds the
: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.
:math:`\texttt{k} \le
\texttt{get\_max\_k()}` nearest neighbor. 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 a custom classification/regression prediction, the method can optionally return pointers to the neighbor vectors themselves ( ``neighbors`` , an array of ``k*_samples->rows`` pointers), their corresponding output values ( ``neighbor_responses`` , a vector of ``k*_samples->rows`` elements), and the distances from the input vectors to the neighbors ( ``dist`` , also a vector of ``k*_samples->rows`` elements).
:param neighbors: Optional output pointers to the neighbor vectors themselves. It is an array of ``k*samples->rows`` pointers.
:param neighborResponses: Optional output values for corresponding ``neighbors``. It is a single-precision floating-point matrix of :math:`number\_of\_samples \times k` size.
:param dist: Optional output distances from the input vectors to the corresponding ``neighbors``. It is a single-precision floating-point matrix of :math:`number\_of\_samples \times 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.
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.
If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method.
CvKNearest::get_max_k
---------------------
Returns the number of maximum neighbors that may be passed to the method :ocv:func:`CvKNearest::find_nearest`.
.. ocv:function:: int CvKNearest::get_max_k() const
CvKNearest::get_var_count
-------------------------
Returns the number of used features (variables count).
.. ocv:function:: int CvKNearest::get_var_count() const
CvKNearest::get_sample_count
----------------------------
Returns the total number of train samples.
.. ocv:function:: int CvKNearest::get_sample_count() const
CvKNearest::is_regression
-------------------------
Returns type of the problem: ``true`` for regression and ``false`` for classification.
