Base class for statistical models in OpenCV ML.
More...
#include <opencv2/ml.hpp>
Base class for statistical models in OpenCV ML.
◆ Flags
Predict options
Enumerator |
---|
UPDATE_MODEL | |
RAW_OUTPUT | makes the method return the raw results (the sum), not the class label
|
COMPRESSED_INPUT | |
PREPROCESSED_INPUT | |
◆ calcError()
Python: |
---|
| retval, resp | = | cv.ml_StatModel.calcError( | data, test[, resp] | ) |
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%).
◆ empty()
virtual bool cv::ml::StatModel::empty |
( |
| ) |
const |
|
virtual |
Python: |
---|
| retval | = | cv.ml_StatModel.empty( | | ) |
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
Reimplemented from cv::Algorithm.
◆ getVarCount()
virtual int cv::ml::StatModel::getVarCount |
( |
| ) |
const |
|
pure virtual |
Python: |
---|
| retval | = | cv.ml_StatModel.getVarCount( | | ) |
Returns the number of variables in training samples.
◆ isClassifier()
virtual bool cv::ml::StatModel::isClassifier |
( |
| ) |
const |
|
pure virtual |
Python: |
---|
| retval | = | cv.ml_StatModel.isClassifier( | | ) |
Returns true if the model is classifier.
◆ isTrained()
virtual bool cv::ml::StatModel::isTrained |
( |
| ) |
const |
|
pure virtual |
Python: |
---|
| retval | = | cv.ml_StatModel.isTrained( | | ) |
Returns true if the model is trained.
◆ predict()
Python: |
---|
| retval, results | = | cv.ml_StatModel.predict( | samples[, results[, flags]] | ) |
◆ train() [1/3]
template<typename _Tp >
Python: |
---|
| retval | = | cv.ml_StatModel.train( | trainData[, flags] | ) |
| retval | = | cv.ml_StatModel.train( | samples, layout, responses | ) |
Create and train model with default parameters.
The class must implement static create()
method with no parameters or with all default parameter values
◆ train() [2/3]
virtual bool cv::ml::StatModel::train |
( |
const Ptr< TrainData > & |
trainData, |
|
|
int |
flags = 0 |
|
) |
| |
|
virtual |
Python: |
---|
| retval | = | cv.ml_StatModel.train( | trainData[, flags] | ) |
| retval | = | cv.ml_StatModel.train( | samples, layout, responses | ) |
◆ train() [3/3]
Python: |
---|
| retval | = | cv.ml_StatModel.train( | trainData[, flags] | ) |
| retval | = | cv.ml_StatModel.train( | samples, layout, responses | ) |
Trains the statistical model.
- Parameters
-
samples | training samples |
layout | See ml::SampleTypes. |
responses | vector of responses associated with the training samples. |
The documentation for this class was generated from the following file: