opencv/modules/ml/doc/k_nearest_neighbors.rst

K-Nearest Neighbors
===================

.. highlight:: cpp

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.

CvKNearest
----------
.. ocv:class:: CvKNearest

The class implements K-Nearest Neighbors model as described in the beginning of this section. 

CvKNearest::CvKNearest
----------------------
Default and training constructors.

.. ocv:function:: CvKNearest::CvKNearest()

.. ocv:function:: CvKNearest::CvKNearest( const Mat& trainData, const Mat& responses, const Mat& sampleIdx=Mat(), bool isRegression=false, int max_k=32 )

.. ocv:function:: CvKNearest::CvKNearest( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool isRegression=false, int max_k=32 )

See :ocv:func:`CvKNearest::train` for additional parameters descriptions.

CvKNearest::train
-----------------
Trains the model.

.. ocv:function:: bool CvKNearest::train( const Mat& trainData, const Mat& responses, const Mat& sampleIdx=Mat(), bool isRegression=false, int maxK=32, bool updateBase=false )

.. ocv:function:: bool CvKNearest::train( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool is_regression=false, int maxK=32, bool updateBase=false )

.. ocv:pyfunction:: cv2.KNearest.train(trainData, responses[, sampleIdx[, isRegression[, maxK[, updateBase]]]]) -> retval

    :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`.

    :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:`CvStatModel::train` approach with the following limitations: 

* Only ``CV_ROW_SAMPLE`` data layout is supported.
* Input variables are all ordered.
* Output variables can be either categorical ( ``is_regression=false`` ) or ordered ( ``is_regression=true`` ).
* Variable subsets (``var_idx``) and missing measurements are not supported.

CvKNearest::find_nearest
------------------------
Finds the neighbors and predicts responses for input vectors.

.. ocv:function:: float CvKNearest::find_nearest( const Mat& samples, int k, Mat* results=0, const float** neighbors=0, Mat* neighborResponses=0, Mat* dist=0 ) const

.. ocv:function:: float CvKNearest::find_nearest( const Mat& samples, int k, Mat& results, Mat& neighborResponses, Mat& dists) const

.. ocv:function:: float CvKNearest::find_nearest( const CvMat* samples, int k, CvMat* results=0, const float** neighbors=0, CvMat* neighborResponses=0, CvMat* dist=0 ) const

.. ocv:pyfunction:: cv2.KNearest.find_nearest(samples, k[, results[, neighborResponses[, dists]]]) -> retval, results, neighborResponses, dists


    :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`.

    :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 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.

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.

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.

.. ocv:function:: bool CvKNearest::is_regression() const



The sample below (currently using the obsolete ``CvMat`` structures) demonstrates the use of the k-nearest classifier for 2D point classification: ::

    #include "ml.h"
    #include "highgui.h"

    int main( int argc, char** argv )
    {
        const int K = 10;
        int i, j, k, accuracy;
        float response;
        int train_sample_count = 100;
        CvRNG rng_state = cvRNG(-1);
        CvMat* trainData = cvCreateMat( train_sample_count, 2, CV_32FC1 );
        CvMat* trainClasses = cvCreateMat( train_sample_count, 1, CV_32FC1 );
        IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );
        float _sample[2];
        CvMat sample = cvMat( 1, 2, CV_32FC1, _sample );
        cvZero( img );

        CvMat trainData1, trainData2, trainClasses1, trainClasses2;

        // form the training samples
        cvGetRows( trainData, &trainData1, 0, train_sample_count/2 );
        cvRandArr( &rng_state, &trainData1, CV_RAND_NORMAL, cvScalar(200,200), cvScalar(50,50) );

        cvGetRows( trainData, &trainData2, train_sample_count/2, train_sample_count );
        cvRandArr( &rng_state, &trainData2, CV_RAND_NORMAL, cvScalar(300,300), cvScalar(50,50) );

        cvGetRows( trainClasses, &trainClasses1, 0, train_sample_count/2 );
        cvSet( &trainClasses1, cvScalar(1) );

        cvGetRows( trainClasses, &trainClasses2, train_sample_count/2, train_sample_count );
        cvSet( &trainClasses2, cvScalar(2) );

        // learn classifier
        CvKNearest knn( trainData, trainClasses, 0, false, K );
        CvMat* nearests = cvCreateMat( 1, K, CV_32FC1);

        for( i = 0; i < img->height; i++ )
        {
            for( j = 0; j < img->width; j++ )
            {
                sample.data.fl[0] = (float)j;
                sample.data.fl[1] = (float)i;

                // estimate the response and get the neighbors' labels
                response = knn.find_nearest(&sample,K,0,0,nearests,0);

                // compute the number of neighbors representing the majority
                for( k = 0, accuracy = 0; k < K; k++ )
                {
                    if( nearests->data.fl[k] == response)
                        accuracy++;
                }
                // highlight the pixel depending on the accuracy (or confidence)
                cvSet2D( img, i, j, response == 1 ?
                    (accuracy > 5 ? CV_RGB(180,0,0) : CV_RGB(180,120,0)) :
                    (accuracy > 5 ? CV_RGB(0,180,0) : CV_RGB(120,120,0)) );
            }
        }

        // display the original training samples
        for( i = 0; i < train_sample_count/2; i++ )
        {
            CvPoint pt;
            pt.x = cvRound(trainData1.data.fl[i*2]);
            pt.y = cvRound(trainData1.data.fl[i*2+1]);
            cvCircle( img, pt, 2, CV_RGB(255,0,0), CV_FILLED );
            pt.x = cvRound(trainData2.data.fl[i*2]);
            pt.y = cvRound(trainData2.data.fl[i*2+1]);
            cvCircle( img, pt, 2, CV_RGB(0,255,0), CV_FILLED );
        }

        cvNamedWindow( "classifier result", 1 );
        cvShowImage( "classifier result", img );
        cvWaitKey(0);

        cvReleaseMat( &trainClasses );
        cvReleaseMat( &trainData );
        return 0;
    }