Package org.opencv.ml
Class LogisticRegression
- java.lang.Object
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- org.opencv.core.Algorithm
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- org.opencv.ml.StatModel
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- org.opencv.ml.LogisticRegression
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public class LogisticRegression extends StatModel
Implements Logistic Regression classifier. SEE: REF: ml_intro_lr
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Field Summary
Fields Modifier and Type Field Description static intBATCHstatic intMINI_BATCHstatic intREG_DISABLEstatic intREG_L1static intREG_L2-
Fields inherited from class org.opencv.ml.StatModel
COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL
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Constructor Summary
Constructors Modifier Constructor Description protectedLogisticRegression(long addr)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static LogisticRegression__fromPtr__(long addr)static LogisticRegressioncreate()Creates empty model.protected voidfinalize()Matget_learnt_thetas()This function returns the trained parameters arranged across rows.intgetIterations()SEE: setIterationsdoublegetLearningRate()SEE: setLearningRateintgetMiniBatchSize()SEE: setMiniBatchSizeintgetRegularization()SEE: setRegularizationTermCriteriagetTermCriteria()SEE: setTermCriteriaintgetTrainMethod()SEE: setTrainMethodstatic LogisticRegressionload(String filepath)Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk.static LogisticRegressionload(String filepath, String nodeName)Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk.floatpredict(Mat samples)Predicts responses for input samples and returns a float type.floatpredict(Mat samples, Mat results)Predicts responses for input samples and returns a float type.floatpredict(Mat samples, Mat results, int flags)Predicts responses for input samples and returns a float type.voidsetIterations(int val)getIterations SEE: getIterationsvoidsetLearningRate(double val)getLearningRate SEE: getLearningRatevoidsetMiniBatchSize(int val)getMiniBatchSize SEE: getMiniBatchSizevoidsetRegularization(int val)getRegularization SEE: getRegularizationvoidsetTermCriteria(TermCriteria val)getTermCriteria SEE: getTermCriteriavoidsetTrainMethod(int val)getTrainMethod SEE: getTrainMethod-
Methods inherited from class org.opencv.ml.StatModel
calcError, empty, getVarCount, isClassifier, isTrained, train, train, train
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Methods inherited from class org.opencv.core.Algorithm
clear, getDefaultName, getNativeObjAddr, save
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Field Detail
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REG_DISABLE
public static final int REG_DISABLE
- See Also:
- Constant Field Values
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REG_L1
public static final int REG_L1
- See Also:
- Constant Field Values
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REG_L2
public static final int REG_L2
- See Also:
- Constant Field Values
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BATCH
public static final int BATCH
- See Also:
- Constant Field Values
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MINI_BATCH
public static final int MINI_BATCH
- See Also:
- Constant Field Values
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Method Detail
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__fromPtr__
public static LogisticRegression __fromPtr__(long addr)
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get_learnt_thetas
public Mat get_learnt_thetas()
This function returns the trained parameters arranged across rows. For a two class classifcation problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.- Returns:
- automatically generated
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create
public static LogisticRegression create()
Creates empty model. Creates Logistic Regression model with parameters given.- Returns:
- automatically generated
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load
public static LogisticRegression load(String filepath, String nodeName)
Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier- Parameters:
filepath- path to serialized LogisticRegressionnodeName- name of node containing the classifier- Returns:
- automatically generated
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load
public static LogisticRegression load(String filepath)
Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier- Parameters:
filepath- path to serialized LogisticRegression- Returns:
- automatically generated
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getTermCriteria
public TermCriteria getTermCriteria()
SEE: setTermCriteria- Returns:
- automatically generated
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getLearningRate
public double getLearningRate()
SEE: setLearningRate- Returns:
- automatically generated
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predict
public float predict(Mat samples, Mat results, int flags)
Predicts responses for input samples and returns a float type.- Overrides:
predictin classStatModel- Parameters:
samples- The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.results- Predicted labels as a column matrix of type CV_32S.flags- Not used.- Returns:
- automatically generated
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predict
public float predict(Mat samples, Mat results)
Predicts responses for input samples and returns a float type.- Overrides:
predictin classStatModel- Parameters:
samples- The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.results- Predicted labels as a column matrix of type CV_32S.- Returns:
- automatically generated
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predict
public float predict(Mat samples)
Predicts responses for input samples and returns a float type.
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getIterations
public int getIterations()
SEE: setIterations- Returns:
- automatically generated
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getMiniBatchSize
public int getMiniBatchSize()
SEE: setMiniBatchSize- Returns:
- automatically generated
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getRegularization
public int getRegularization()
SEE: setRegularization- Returns:
- automatically generated
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getTrainMethod
public int getTrainMethod()
SEE: setTrainMethod- Returns:
- automatically generated
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setIterations
public void setIterations(int val)
getIterations SEE: getIterations- Parameters:
val- automatically generated
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setLearningRate
public void setLearningRate(double val)
getLearningRate SEE: getLearningRate- Parameters:
val- automatically generated
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setMiniBatchSize
public void setMiniBatchSize(int val)
getMiniBatchSize SEE: getMiniBatchSize- Parameters:
val- automatically generated
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setRegularization
public void setRegularization(int val)
getRegularization SEE: getRegularization- Parameters:
val- automatically generated
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setTermCriteria
public void setTermCriteria(TermCriteria val)
getTermCriteria SEE: getTermCriteria- Parameters:
val- automatically generated
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setTrainMethod
public void setTrainMethod(int val)
getTrainMethod SEE: getTrainMethod- Parameters:
val- automatically generated
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