Finds the neighbors and predicts responses for input vectors

results = classifier.findNearest(samples, K)
[results,neiResp,dists,f] = classifier.findNearest(samples, K)
[...] = classifier.findNearest(..., 'OptionName', optionValue, ...)

Input

• samples Input samples stored by rows. It is a single-precision floating-point matrix of size nsamples-by-nfeatures.
• K Number of used nearest neighbors. Should be greater than or equal to 1.

Output

• results Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with nsamples elements.
• neiResp Optional output array for corresponding neighbor responses. It is a single-precision floating-point matrix of size nsamples-by-K.
• dists Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of size nsamples-by-K.
• f If only a single input vector is passed, the predicted value is returned by the method. Otherwise it returns the prediction of the first sample.

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.

The function is parallelized with the TBB library.