View Source Evision.ML.StatModel (Evision v0.1.7)

Link to this section Summary

cv

Clears the algorithm state

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.

Reads algorithm parameters from a file storage

Positional Arguments

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

simplified API for language bindings

simplified API for language bindings

cv.ml

Computes error on the training or test dataset

Computes error on the training or test dataset

Python prototype (for reference):

Returns the number of variables in training samples

Returns true if the model is classifier

Returns true if the model is trained

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

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

Trains the statistical model

Trains the statistical model

Trains the statistical model

Functions

Raising version of clear/1.

Raising version of empty/1.

Raising version of getVarCount/1.

Raising version of isClassifier/1.

Raising version of isTrained/1.

Raising version of predict/2.

Raising version of read/2.

Raising version of save/2.

Raising version of train/2.

Raising version of write/2.

Raising version of write/3.

Link to this section cv

Clears the algorithm state

Python prototype (for reference):

clear() -> None

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.

Python prototype (for reference):

getDefaultName() -> retval

Reads algorithm parameters from a file storage

Positional Arguments
  • fn_: FileNode

Python prototype (for reference):

read(fn_) -> None
Positional Arguments

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

Python prototype (for reference):

save(filename) -> None

simplified API for language bindings

Positional Arguments
  • fs: Ptr<FileStorage>
Keyword Arguments

Has overloading in C++

Python prototype (for reference):

write(fs[, name]) -> None

simplified API for language bindings

Positional Arguments
  • fs: Ptr<FileStorage>
Keyword Arguments

Has overloading in C++

Python prototype (for reference):

write(fs[, name]) -> None

Link to this section cv.ml

Link to this function

calcError(self, data, test)

View Source

Computes error on the training or test dataset

Positional Arguments
  • data: Ptr<TrainData>.

    the training data

  • test: bool.

    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.

Return

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%).

Python prototype (for reference):

calcError(data, test[, resp]) -> retval, resp
Link to this function

calcError(self, data, test, opts)

View Source

Computes error on the training or test dataset

Positional Arguments
  • data: Ptr<TrainData>.

    the training data

  • test: bool.

    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.

Return

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%).

Python prototype (for reference):

calcError(data, test[, resp]) -> retval, resp

Python prototype (for reference):

empty() -> retval

Returns the number of variables in training samples

Python prototype (for reference):

getVarCount() -> retval

Returns true if the model is classifier

Python prototype (for reference):

isClassifier() -> retval

Returns true if the model is trained

Python prototype (for reference):

isTrained() -> retval

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

Positional Arguments
  • samples: Evision.Mat.

    The input samples, floating-point matrix

Keyword Arguments
  • flags: int.

    The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Return
  • results: Evision.Mat.

    The optional output matrix of results.

Python prototype (for reference):

predict(samples[, results[, flags]]) -> retval, results
Link to this function

predict(self, samples, opts)

View Source

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

Positional Arguments
  • samples: Evision.Mat.

    The input samples, floating-point matrix

Keyword Arguments
  • flags: int.

    The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Return
  • results: Evision.Mat.

    The optional output matrix of results.

Python prototype (for reference):

predict(samples[, results[, flags]]) -> retval, results

Trains the statistical model

Positional Arguments
  • trainData: Ptr<TrainData>.

    training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.

Keyword Arguments
  • flags: int.

    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).

Python prototype (for reference):

train(trainData[, flags]) -> retval
Link to this function

train(self, trainData, opts)

View Source

Trains the statistical model

Positional Arguments
  • trainData: Ptr<TrainData>.

    training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.

Keyword Arguments
  • flags: int.

    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).

Python prototype (for reference):

train(trainData[, flags]) -> retval
Link to this function

train(self, samples, layout, responses)

View Source

Trains the statistical model

Positional Arguments
  • samples: Evision.Mat.

    training samples

  • layout: int.

    See ml::SampleTypes.

  • responses: Evision.Mat.

    vector of responses associated with the training samples.

Python prototype (for reference):

train(samples, layout, responses) -> retval

Link to this section Functions

Link to this function

calcError!(self, data, test)

View Source

Raising version of calcError/3.

Link to this function

calcError!(self, data, test, opts)

View Source

Raising version of calcError/4.

Raising version of clear/1.

Raising version of empty/1.

Raising version of getDefaultName/1.

Raising version of getVarCount/1.

Raising version of isClassifier/1.

Raising version of isTrained/1.

Raising version of predict/2.

Link to this function

predict!(self, samples, opts)

View Source

Raising version of predict/3.

Raising version of read/2.

Raising version of save/2.

Raising version of train/2.

Link to this function

train!(self, trainData, opts)

View Source

Raising version of train/3.

Link to this function

train!(self, samples, layout, responses)

View Source

Raising version of train/4.

Raising version of write/2.

Raising version of write/3.