Package org.opencv.ml
Class LogisticRegression
java.lang.Object
org.opencv.core.Algorithm
org.opencv.ml.StatModel
org.opencv.ml.LogisticRegression
Implements Logistic Regression classifier.
SEE: REF: ml_intro_lr
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final int
static final int
static final int
static final int
static final int
Fields inherited from class org.opencv.ml.StatModel
COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic LogisticRegression
__fromPtr__
(long addr) static LogisticRegression
create()
Creates empty model.protected void
finalize()
This function returns the trained parameters arranged across rows.int
SEE: setIterationsdouble
SEE: setLearningRateint
SEE: setMiniBatchSizeint
SEE: setRegularizationSEE: setTermCriteriaint
SEE: setTrainMethodstatic LogisticRegression
Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk.static LogisticRegression
Loads and creates a serialized LogisticRegression from a file Use LogisticRegression::save to serialize and store an LogisticRegression to disk.float
Predicts responses for input samples and returns a float type.float
Predicts responses for input samples and returns a float type.float
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
getTermCriteria SEE: getTermCriteriavoid
setTrainMethod
(int val) getTrainMethod SEE: getTrainMethodMethods inherited from class org.opencv.ml.StatModel
calcError, empty, getVarCount, isClassifier, isTrained, train, train, train
Methods inherited from class org.opencv.core.Algorithm
clear, getDefaultName, getNativeObjAddr, save
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Field Details
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BATCH
public static final int BATCH- See Also:
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MINI_BATCH
public static final int MINI_BATCH- See Also:
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REG_DISABLE
public static final int REG_DISABLE- See Also:
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REG_L1
public static final int REG_L1- See Also:
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REG_L2
public static final int REG_L2- See Also:
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Constructor Details
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LogisticRegression
protected LogisticRegression(long addr)
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Method Details
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__fromPtr__
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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
SEE: setTermCriteria- Returns:
- automatically generated
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setTermCriteria
getTermCriteria SEE: getTermCriteria- Parameters:
val
- automatically generated
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predict
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
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
Predicts responses for input samples and returns a float type. -
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
Creates empty model. Creates Logistic Regression model with parameters given.- Returns:
- automatically generated
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load
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
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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finalize
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