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 int
BATCH
static int
MINI_BATCH
static int
REG_DISABLE
static int
REG_L1
static int
REG_L2
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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 protected
LogisticRegression(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 LogisticRegression
create()
Creates empty model.protected void
finalize()
Mat
get_learnt_thetas()
This function returns the trained parameters arranged across rows.int
getIterations()
SEE: setIterationsdouble
getLearningRate()
SEE: setLearningRateint
getMiniBatchSize()
SEE: setMiniBatchSizeint
getRegularization()
SEE: setRegularizationTermCriteria
getTermCriteria()
SEE: setTermCriteriaint
getTrainMethod()
SEE: setTrainMethodstatic LogisticRegression
load(java.lang.String filepath)
Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk.static LogisticRegression
load(java.lang.String filepath, java.lang.String nodeName)
Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk.float
predict(Mat samples)
Predicts responses for input samples and returns a float type.float
predict(Mat samples, Mat results)
Predicts responses for input samples and returns a float type.float
predict(Mat samples, Mat results, int flags)
Predicts responses for input samples and returns a float type.void
setIterations(int val)
getIterations SEE: getIterationsvoid
setLearningRate(double val)
getLearningRate SEE: getLearningRatevoid
setMiniBatchSize(int val)
getMiniBatchSize SEE: getMiniBatchSizevoid
setRegularization(int val)
getRegularization SEE: getRegularizationvoid
setTermCriteria(TermCriteria val)
getTermCriteria SEE: getTermCriteriavoid
setTrainMethod(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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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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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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Method Detail
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__fromPtr__
public static LogisticRegression __fromPtr__(long addr)
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getLearningRate
public double getLearningRate()
SEE: setLearningRate- Returns:
- automatically generated
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setLearningRate
public void setLearningRate(double val)
getLearningRate SEE: getLearningRate- Parameters:
val
- automatically generated
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getIterations
public int getIterations()
SEE: setIterations- Returns:
- automatically generated
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setIterations
public void setIterations(int val)
getIterations SEE: getIterations- Parameters:
val
- automatically generated
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getRegularization
public int getRegularization()
SEE: setRegularization- Returns:
- automatically generated
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setRegularization
public void setRegularization(int val)
getRegularization SEE: getRegularization- Parameters:
val
- automatically generated
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getTrainMethod
public int getTrainMethod()
SEE: setTrainMethod- Returns:
- automatically generated
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setTrainMethod
public void setTrainMethod(int val)
getTrainMethod SEE: getTrainMethod- Parameters:
val
- automatically generated
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getMiniBatchSize
public int getMiniBatchSize()
SEE: setMiniBatchSize- Returns:
- automatically generated
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setMiniBatchSize
public void setMiniBatchSize(int val)
getMiniBatchSize SEE: getMiniBatchSize- Parameters:
val
- automatically generated
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getTermCriteria
public TermCriteria getTermCriteria()
SEE: setTermCriteria- Returns:
- automatically generated
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setTermCriteria
public void setTermCriteria(TermCriteria val)
getTermCriteria SEE: getTermCriteria- Parameters:
val
- 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:
predict
in 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:
predict
in 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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get_learnt_thetas
public Mat get_learnt_thetas()
This function returns the trained parameters arranged across rows. For a two class classification 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(java.lang.String filepath, java.lang.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(java.lang.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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