Package org.opencv.ml

Class StatModel

java.lang.Object
org.opencv.core.Algorithm
org.opencv.ml.StatModel
Direct Known Subclasses:
ANN_MLP, DTrees, EM, KNearest, LogisticRegression, NormalBayesClassifier, SVM, SVMSGD
public class StatModel
extends Algorithm
Base class for statistical models in OpenCV ML.

  • Field Details

  • Constructor Details

  • Method Details

    • __fromPtr__

      public static StatModel __fromPtr__​(long addr)

    • getVarCount

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

    • 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

    • isTrained

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

    • isClassifier

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

    • 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

    • 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

    • finalize

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