Package org.opencv.ml

Class StatModel

    • Constructor Detail

      • StatModel

        protected StatModel​(long addr)

    • Method Detail

      • __fromPtr__

        public static StatModel __fromPtr__​(long addr)

      • empty

        public boolean empty()
        Description copied from class: Algorithm
        Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
        Overrides:
        empty in class Algorithm
        Returns:
        automatically generated

      • isClassifier

        public boolean isClassifier()
        Returns true if the model is classifier
        Returns:
        automatically generated

      • isTrained

        public boolean isTrained()
        Returns true if the model is trained
        Returns:
        automatically generated

      • train

        public boolean train​(Mat samples,
                     int layout,
                     Mat responses)
        Trains the statistical model
        Parameters:
        samples - training samples
        layout - See ml::SampleTypes.
        responses - vector of responses associated with the training samples.
        Returns:
        automatically generated

      • train

        public boolean train​(TrainData trainData,
                     int flags)
        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).
        Returns:
        automatically generated

      • train

        public boolean train​(TrainData trainData)
        Trains the statistical model
        Parameters:
        trainData - training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create. new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
        Returns:
        automatically generated

      • calcError

        public float calcError​(TrainData data,
                       boolean test,
                       Mat 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%).
        Returns:
        automatically generated

      • predict

        public float predict​(Mat samples,
                     Mat results,
                     int flags)
        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.
        Returns:
        automatically generated

      • predict

        public float predict​(Mat samples,
                     Mat results)
        Predicts response(s) for the provided sample(s)
        Parameters:
        samples - The input samples, floating-point matrix
        results - The optional output matrix of results.
        Returns:
        automatically generated

      • predict

        public float predict​(Mat samples)
        Predicts response(s) for the provided sample(s)
        Parameters:
        samples - The input samples, floating-point matrix
        Returns:
        automatically generated

      • getVarCount

        public int getVarCount()
        Returns the number of variables in training samples
        Returns:
        automatically generated

      • finalize

        protected void finalize()
                 throws java.lang.Throwable
        Overrides:
        finalize in class Algorithm
        Throws:
        java.lang.Throwable