Health/Assets/OpenCVForUnity/org/opencv/ml/LogisticRegression.cs

503 lines
15 KiB
C#

using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;
namespace OpenCVForUnity.MlModule
{
// C++: class LogisticRegression
/**
* Implements Logistic Regression classifier.
*
* SEE: REF: ml_intro_lr
*/
public class LogisticRegression : StatModel
{
protected override void Dispose(bool disposing)
{
try
{
if (disposing)
{
}
if (IsEnabledDispose)
{
if (nativeObj != IntPtr.Zero)
ml_LogisticRegression_delete(nativeObj);
nativeObj = IntPtr.Zero;
}
}
finally
{
base.Dispose(disposing);
}
}
protected internal LogisticRegression(IntPtr addr) : base(addr) { }
// internal usage only
public static new LogisticRegression __fromPtr__(IntPtr addr) { return new LogisticRegression(addr); }
// C++: enum cv.ml.LogisticRegression.Methods
public const int BATCH = 0;
public const int MINI_BATCH = 1;
// C++: enum cv.ml.LogisticRegression.RegKinds
public const int REG_DISABLE = -1;
public const int REG_L1 = 0;
public const int REG_L2 = 1;
//
// C++: double cv::ml::LogisticRegression::getLearningRate()
//
/**
* SEE: setLearningRate
* return automatically generated
*/
public double getLearningRate()
{
ThrowIfDisposed();
return ml_LogisticRegression_getLearningRate_10(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setLearningRate(double val)
//
/**
* getLearningRate SEE: getLearningRate
* param val automatically generated
*/
public void setLearningRate(double val)
{
ThrowIfDisposed();
ml_LogisticRegression_setLearningRate_10(nativeObj, val);
}
//
// C++: int cv::ml::LogisticRegression::getIterations()
//
/**
* SEE: setIterations
* return automatically generated
*/
public int getIterations()
{
ThrowIfDisposed();
return ml_LogisticRegression_getIterations_10(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setIterations(int val)
//
/**
* getIterations SEE: getIterations
* param val automatically generated
*/
public void setIterations(int val)
{
ThrowIfDisposed();
ml_LogisticRegression_setIterations_10(nativeObj, val);
}
//
// C++: int cv::ml::LogisticRegression::getRegularization()
//
/**
* SEE: setRegularization
* return automatically generated
*/
public int getRegularization()
{
ThrowIfDisposed();
return ml_LogisticRegression_getRegularization_10(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setRegularization(int val)
//
/**
* getRegularization SEE: getRegularization
* param val automatically generated
*/
public void setRegularization(int val)
{
ThrowIfDisposed();
ml_LogisticRegression_setRegularization_10(nativeObj, val);
}
//
// C++: int cv::ml::LogisticRegression::getTrainMethod()
//
/**
* SEE: setTrainMethod
* return automatically generated
*/
public int getTrainMethod()
{
ThrowIfDisposed();
return ml_LogisticRegression_getTrainMethod_10(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setTrainMethod(int val)
//
/**
* getTrainMethod SEE: getTrainMethod
* param val automatically generated
*/
public void setTrainMethod(int val)
{
ThrowIfDisposed();
ml_LogisticRegression_setTrainMethod_10(nativeObj, val);
}
//
// C++: int cv::ml::LogisticRegression::getMiniBatchSize()
//
/**
* SEE: setMiniBatchSize
* return automatically generated
*/
public int getMiniBatchSize()
{
ThrowIfDisposed();
return ml_LogisticRegression_getMiniBatchSize_10(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setMiniBatchSize(int val)
//
/**
* getMiniBatchSize SEE: getMiniBatchSize
* param val automatically generated
*/
public void setMiniBatchSize(int val)
{
ThrowIfDisposed();
ml_LogisticRegression_setMiniBatchSize_10(nativeObj, val);
}
//
// C++: TermCriteria cv::ml::LogisticRegression::getTermCriteria()
//
/**
* SEE: setTermCriteria
* return automatically generated
*/
public TermCriteria getTermCriteria()
{
ThrowIfDisposed();
double[] tmpArray = new double[3];
ml_LogisticRegression_getTermCriteria_10(nativeObj, tmpArray);
TermCriteria retVal = new TermCriteria(tmpArray);
return retVal;
}
//
// C++: void cv::ml::LogisticRegression::setTermCriteria(TermCriteria val)
//
/**
* getTermCriteria SEE: getTermCriteria
* param val automatically generated
*/
public void setTermCriteria(TermCriteria val)
{
ThrowIfDisposed();
ml_LogisticRegression_setTermCriteria_10(nativeObj, val.type, val.maxCount, val.epsilon);
}
//
// C++: float cv::ml::LogisticRegression::predict(Mat samples, Mat& results = Mat(), int flags = 0)
//
/**
* Predicts responses for input samples and returns a float type.
*
* param 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.
* param results Predicted labels as a column matrix of type CV_32S.
* param flags Not used.
* return automatically generated
*/
public override float predict(Mat samples, Mat results, int flags)
{
ThrowIfDisposed();
if (samples != null) samples.ThrowIfDisposed();
if (results != null) results.ThrowIfDisposed();
return ml_LogisticRegression_predict_10(nativeObj, samples.nativeObj, results.nativeObj, flags);
}
/**
* Predicts responses for input samples and returns a float type.
*
* param 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.
* param results Predicted labels as a column matrix of type CV_32S.
* return automatically generated
*/
public override float predict(Mat samples, Mat results)
{
ThrowIfDisposed();
if (samples != null) samples.ThrowIfDisposed();
if (results != null) results.ThrowIfDisposed();
return ml_LogisticRegression_predict_11(nativeObj, samples.nativeObj, results.nativeObj);
}
/**
* Predicts responses for input samples and returns a float type.
*
* param 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.
* return automatically generated
*/
public override float predict(Mat samples)
{
ThrowIfDisposed();
if (samples != null) samples.ThrowIfDisposed();
return ml_LogisticRegression_predict_12(nativeObj, samples.nativeObj);
}
//
// C++: Mat cv::ml::LogisticRegression::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.
* return automatically generated
*/
public Mat get_learnt_thetas()
{
ThrowIfDisposed();
return new Mat(DisposableObject.ThrowIfNullIntPtr(ml_LogisticRegression_get_1learnt_1thetas_10(nativeObj)));
}
//
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::create()
//
/**
* Creates empty model.
*
* Creates Logistic Regression model with parameters given.
* return automatically generated
*/
public static LogisticRegression create()
{
return LogisticRegression.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(ml_LogisticRegression_create_10()));
}
//
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::load(String filepath, String nodeName = String())
//
/**
* 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
*
* param filepath path to serialized LogisticRegression
* param nodeName name of node containing the classifier
* return automatically generated
*/
public static LogisticRegression load(string filepath, string nodeName)
{
return LogisticRegression.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(ml_LogisticRegression_load_10(filepath, 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
*
* param filepath path to serialized LogisticRegression
* return automatically generated
*/
public static LogisticRegression load(string filepath)
{
return LogisticRegression.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(ml_LogisticRegression_load_11(filepath)));
}
#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
const string LIBNAME = "__Internal";
#else
const string LIBNAME = "opencvforunity";
#endif
// C++: double cv::ml::LogisticRegression::getLearningRate()
[DllImport(LIBNAME)]
private static extern double ml_LogisticRegression_getLearningRate_10(IntPtr nativeObj);
// C++: void cv::ml::LogisticRegression::setLearningRate(double val)
[DllImport(LIBNAME)]
private static extern void ml_LogisticRegression_setLearningRate_10(IntPtr nativeObj, double val);
// C++: int cv::ml::LogisticRegression::getIterations()
[DllImport(LIBNAME)]
private static extern int ml_LogisticRegression_getIterations_10(IntPtr nativeObj);
// C++: void cv::ml::LogisticRegression::setIterations(int val)
[DllImport(LIBNAME)]
private static extern void ml_LogisticRegression_setIterations_10(IntPtr nativeObj, int val);
// C++: int cv::ml::LogisticRegression::getRegularization()
[DllImport(LIBNAME)]
private static extern int ml_LogisticRegression_getRegularization_10(IntPtr nativeObj);
// C++: void cv::ml::LogisticRegression::setRegularization(int val)
[DllImport(LIBNAME)]
private static extern void ml_LogisticRegression_setRegularization_10(IntPtr nativeObj, int val);
// C++: int cv::ml::LogisticRegression::getTrainMethod()
[DllImport(LIBNAME)]
private static extern int ml_LogisticRegression_getTrainMethod_10(IntPtr nativeObj);
// C++: void cv::ml::LogisticRegression::setTrainMethod(int val)
[DllImport(LIBNAME)]
private static extern void ml_LogisticRegression_setTrainMethod_10(IntPtr nativeObj, int val);
// C++: int cv::ml::LogisticRegression::getMiniBatchSize()
[DllImport(LIBNAME)]
private static extern int ml_LogisticRegression_getMiniBatchSize_10(IntPtr nativeObj);
// C++: void cv::ml::LogisticRegression::setMiniBatchSize(int val)
[DllImport(LIBNAME)]
private static extern void ml_LogisticRegression_setMiniBatchSize_10(IntPtr nativeObj, int val);
// C++: TermCriteria cv::ml::LogisticRegression::getTermCriteria()
[DllImport(LIBNAME)]
private static extern void ml_LogisticRegression_getTermCriteria_10(IntPtr nativeObj, double[] retVal);
// C++: void cv::ml::LogisticRegression::setTermCriteria(TermCriteria val)
[DllImport(LIBNAME)]
private static extern void ml_LogisticRegression_setTermCriteria_10(IntPtr nativeObj, int val_type, int val_maxCount, double val_epsilon);
// C++: float cv::ml::LogisticRegression::predict(Mat samples, Mat& results = Mat(), int flags = 0)
[DllImport(LIBNAME)]
private static extern float ml_LogisticRegression_predict_10(IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr results_nativeObj, int flags);
[DllImport(LIBNAME)]
private static extern float ml_LogisticRegression_predict_11(IntPtr nativeObj, IntPtr samples_nativeObj, IntPtr results_nativeObj);
[DllImport(LIBNAME)]
private static extern float ml_LogisticRegression_predict_12(IntPtr nativeObj, IntPtr samples_nativeObj);
// C++: Mat cv::ml::LogisticRegression::get_learnt_thetas()
[DllImport(LIBNAME)]
private static extern IntPtr ml_LogisticRegression_get_1learnt_1thetas_10(IntPtr nativeObj);
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::create()
[DllImport(LIBNAME)]
private static extern IntPtr ml_LogisticRegression_create_10();
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::load(String filepath, String nodeName = String())
[DllImport(LIBNAME)]
private static extern IntPtr ml_LogisticRegression_load_10(string filepath, string nodeName);
[DllImport(LIBNAME)]
private static extern IntPtr ml_LogisticRegression_load_11(string filepath);
// native support for java finalize()
[DllImport(LIBNAME)]
private static extern void ml_LogisticRegression_delete(IntPtr nativeObj);
}
}