KNearest Class Reference
[Machine Learning]

Predict options.

Enumerator:

RAW_OUTPUT

makes the method return the raw results (the sum), not the class label

Reimplemented in DTrees.

Definition at line 303 of file ml.hpp.

Implementations of KNearest algorithm.

Definition at line 467 of file ml.hpp.

Computes error on the training or test dataset.

Parameters:

data	the training data
test	if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.
resp	the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0-100%).

Clears the algorithm state.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3030 of file core.hpp.

Creates the empty model.

The static method creates empty KNearest classifier. It should be then trained using StatModel::train method.

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Reimplemented from Algorithm.

Finds the neighbors and predicts responses for input vectors.

Parameters:

samples	Input samples stored by rows. It is a single-precision floating-point matrix of `<number_of_samples> * k` size.
k	Number of used nearest neighbors. Should be greater than 1.
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.
neighborResponses	Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of `<number_of_samples> * k` size.
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.

Algorithm type, one of KNearest::Types.

See also:

setAlgorithmType

Default number of neighbors to use in predict method.

See also:

setDefaultK

Returns the algorithm string identifier.

This string is used as top level xml/yml node tag when the object is saved to a file or string.

Parameter for KDTree implementation.

See also:

setEmax

Whether classification or regression model should be trained.

See also:

setIsClassifier

Returns the number of variables in training samples.

Returns true if the model is classifier.

Returns true if the model is trained.

Loads algorithm from the file.

Parameters:

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

Definition at line 3074 of file core.hpp.

Loads algorithm from a String.

Parameters:

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

Definition at line 3094 of file core.hpp.

Predicts response(s) for the provided sample(s)

Parameters:

samples	The input samples, floating-point matrix
results	The optional output matrix of results.
flags	The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Implemented in LogisticRegression.

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     cv::FileStorage fsRead("example.xml", FileStorage::READ);
     Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3055 of file core.hpp.

Reads algorithm parameters from a file storage.

Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3038 of file core.hpp.

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Algorithm type, one of KNearest::Types.

See also:

getAlgorithmType

Default number of neighbors to use in predict method.

See also:

getDefaultK

Parameter for KDTree implementation.

See also:

getEmax

Whether classification or regression model should be trained.

See also:

getIsClassifier

Trains the statistical model.

Parameters:

trainData	training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
flags	optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Create and train model with default parameters.

The class must implement static `create()` method with no parameters or with all default parameter values

Definition at line 364 of file ml.hpp.

Trains the statistical model.

Parameters:

samples	training samples
layout	See ml::SampleTypes.
responses	vector of responses associated with the training samples.

Stores algorithm parameters in a file storage.

Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3034 of file core.hpp.