advancedskrald/ChessAR/Assets/OpenCVForUnity/org/opencv/ml/LogisticRegression.cs

499 lines
16 KiB
C#
Executable File

using OpenCVForUnity.CoreModule;
using OpenCVForUnity.UtilsModule;
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;
namespace OpenCVForUnity.MlModule
{
// C++: class LogisticRegression
//javadoc: LogisticRegression
public class LogisticRegression : StatModel
{
protected override void Dispose (bool disposing)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
try {
if (disposing) {
}
if (IsEnabledDispose) {
if (nativeObj != IntPtr.Zero)
ml_LogisticRegression_delete (nativeObj);
nativeObj = IntPtr.Zero;
}
} finally {
base.Dispose (disposing);
}
#else
return;
#endif
}
protected internal LogisticRegression (IntPtr addr)
: base (addr)
{
}
// internal usage only
public static new LogisticRegression __fromPtr__ (IntPtr addr)
{
return new LogisticRegression (addr);
}
// C++: enum RegKinds
public const int REG_DISABLE = -1;
public const int REG_L1 = 0;
public const int REG_L2 = 1;
// C++: enum Methods
public const int BATCH = 0;
public const int MINI_BATCH = 1;
//
// C++: Mat cv::ml::LogisticRegression::get_learnt_thetas()
//
//javadoc: LogisticRegression::get_learnt_thetas()
public Mat get_learnt_thetas ()
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat retVal = new Mat (ml_LogisticRegression_get_1learnt_1thetas_10 (nativeObj));
return retVal;
#else
return null;
#endif
}
//
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::create()
//
//javadoc: LogisticRegression::create()
public static LogisticRegression create ()
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
LogisticRegression retVal = LogisticRegression.__fromPtr__ (ml_LogisticRegression_create_10 ());
return retVal;
#else
return null;
#endif
}
//
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::load(String filepath, String nodeName = String())
//
//javadoc: LogisticRegression::load(filepath, nodeName)
public static LogisticRegression load (string filepath, string nodeName)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
LogisticRegression retVal = LogisticRegression.__fromPtr__ (ml_LogisticRegression_load_10 (filepath, nodeName));
return retVal;
#else
return null;
#endif
}
//javadoc: LogisticRegression::load(filepath)
public static LogisticRegression load (string filepath)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
LogisticRegression retVal = LogisticRegression.__fromPtr__ (ml_LogisticRegression_load_11 (filepath));
return retVal;
#else
return null;
#endif
}
//
// C++: TermCriteria cv::ml::LogisticRegression::getTermCriteria()
//
//javadoc: LogisticRegression::getTermCriteria()
public TermCriteria getTermCriteria ()
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
double[] tmpArray = new double[3];
ml_LogisticRegression_getTermCriteria_10 (nativeObj, tmpArray);
TermCriteria retVal = new TermCriteria (tmpArray);
return retVal;
#else
return null;
#endif
}
//
// C++: double cv::ml::LogisticRegression::getLearningRate()
//
//javadoc: LogisticRegression::getLearningRate()
public double getLearningRate ()
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
double retVal = ml_LogisticRegression_getLearningRate_10 (nativeObj);
return retVal;
#else
return -1;
#endif
}
//
// C++: float cv::ml::LogisticRegression::predict(Mat samples, Mat& results = Mat(), int flags = 0)
//
//javadoc: LogisticRegression::predict(samples, results, flags)
public override float predict (Mat samples, Mat results, int flags)
{
ThrowIfDisposed ();
if (samples != null)
samples.ThrowIfDisposed ();
if (results != null)
results.ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
float retVal = ml_LogisticRegression_predict_10 (nativeObj, samples.nativeObj, results.nativeObj, flags);
return retVal;
#else
return -1;
#endif
}
//javadoc: LogisticRegression::predict(samples, results)
public override float predict (Mat samples, Mat results)
{
ThrowIfDisposed ();
if (samples != null)
samples.ThrowIfDisposed ();
if (results != null)
results.ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
float retVal = ml_LogisticRegression_predict_11 (nativeObj, samples.nativeObj, results.nativeObj);
return retVal;
#else
return -1;
#endif
}
//javadoc: LogisticRegression::predict(samples)
public override float predict (Mat samples)
{
ThrowIfDisposed ();
if (samples != null)
samples.ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
float retVal = ml_LogisticRegression_predict_12 (nativeObj, samples.nativeObj);
return retVal;
#else
return -1;
#endif
}
//
// C++: int cv::ml::LogisticRegression::getIterations()
//
//javadoc: LogisticRegression::getIterations()
public int getIterations ()
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
int retVal = ml_LogisticRegression_getIterations_10 (nativeObj);
return retVal;
#else
return -1;
#endif
}
//
// C++: int cv::ml::LogisticRegression::getMiniBatchSize()
//
//javadoc: LogisticRegression::getMiniBatchSize()
public int getMiniBatchSize ()
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
int retVal = ml_LogisticRegression_getMiniBatchSize_10 (nativeObj);
return retVal;
#else
return -1;
#endif
}
//
// C++: int cv::ml::LogisticRegression::getRegularization()
//
//javadoc: LogisticRegression::getRegularization()
public int getRegularization ()
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
int retVal = ml_LogisticRegression_getRegularization_10 (nativeObj);
return retVal;
#else
return -1;
#endif
}
//
// C++: int cv::ml::LogisticRegression::getTrainMethod()
//
//javadoc: LogisticRegression::getTrainMethod()
public int getTrainMethod ()
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
int retVal = ml_LogisticRegression_getTrainMethod_10 (nativeObj);
return retVal;
#else
return -1;
#endif
}
//
// C++: void cv::ml::LogisticRegression::setIterations(int val)
//
//javadoc: LogisticRegression::setIterations(val)
public void setIterations (int val)
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ml_LogisticRegression_setIterations_10 (nativeObj, val);
return;
#else
return;
#endif
}
//
// C++: void cv::ml::LogisticRegression::setLearningRate(double val)
//
//javadoc: LogisticRegression::setLearningRate(val)
public void setLearningRate (double val)
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ml_LogisticRegression_setLearningRate_10 (nativeObj, val);
return;
#else
return;
#endif
}
//
// C++: void cv::ml::LogisticRegression::setMiniBatchSize(int val)
//
//javadoc: LogisticRegression::setMiniBatchSize(val)
public void setMiniBatchSize (int val)
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ml_LogisticRegression_setMiniBatchSize_10 (nativeObj, val);
return;
#else
return;
#endif
}
//
// C++: void cv::ml::LogisticRegression::setRegularization(int val)
//
//javadoc: LogisticRegression::setRegularization(val)
public void setRegularization (int val)
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ml_LogisticRegression_setRegularization_10 (nativeObj, val);
return;
#else
return;
#endif
}
//
// C++: void cv::ml::LogisticRegression::setTermCriteria(TermCriteria val)
//
//javadoc: LogisticRegression::setTermCriteria(val)
public void setTermCriteria (TermCriteria val)
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ml_LogisticRegression_setTermCriteria_10 (nativeObj, val.type, val.maxCount, val.epsilon);
return;
#else
return;
#endif
}
//
// C++: void cv::ml::LogisticRegression::setTrainMethod(int val)
//
//javadoc: LogisticRegression::setTrainMethod(val)
public void setTrainMethod (int val)
{
ThrowIfDisposed ();
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ml_LogisticRegression_setTrainMethod_10 (nativeObj, val);
return;
#else
return;
#endif
}
#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
const string LIBNAME = "__Internal";
#else
const string LIBNAME = "opencvforunity";
#endif
// 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);
// C++: TermCriteria cv::ml::LogisticRegression::getTermCriteria()
[DllImport (LIBNAME)]
private static extern void ml_LogisticRegression_getTermCriteria_10 (IntPtr nativeObj, double[] retVal);
// C++: double cv::ml::LogisticRegression::getLearningRate()
[DllImport (LIBNAME)]
private static extern double ml_LogisticRegression_getLearningRate_10 (IntPtr nativeObj);
// 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++: int cv::ml::LogisticRegression::getIterations()
[DllImport (LIBNAME)]
private static extern int ml_LogisticRegression_getIterations_10 (IntPtr nativeObj);
// C++: int cv::ml::LogisticRegression::getMiniBatchSize()
[DllImport (LIBNAME)]
private static extern int ml_LogisticRegression_getMiniBatchSize_10 (IntPtr nativeObj);
// C++: int cv::ml::LogisticRegression::getRegularization()
[DllImport (LIBNAME)]
private static extern int ml_LogisticRegression_getRegularization_10 (IntPtr nativeObj);
// C++: int cv::ml::LogisticRegression::getTrainMethod()
[DllImport (LIBNAME)]
private static extern int ml_LogisticRegression_getTrainMethod_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++: void cv::ml::LogisticRegression::setLearningRate(double val)
[DllImport (LIBNAME)]
private static extern void ml_LogisticRegression_setLearningRate_10 (IntPtr nativeObj, double val);
// C++: void cv::ml::LogisticRegression::setMiniBatchSize(int val)
[DllImport (LIBNAME)]
private static extern void ml_LogisticRegression_setMiniBatchSize_10 (IntPtr nativeObj, int val);
// C++: void cv::ml::LogisticRegression::setRegularization(int val)
[DllImport (LIBNAME)]
private static extern void ml_LogisticRegression_setRegularization_10 (IntPtr nativeObj, int val);
// 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++: void cv::ml::LogisticRegression::setTrainMethod(int val)
[DllImport (LIBNAME)]
private static extern void ml_LogisticRegression_setTrainMethod_10 (IntPtr nativeObj, int val);
// native support for java finalize()
[DllImport (LIBNAME)]
private static extern void ml_LogisticRegression_delete (IntPtr nativeObj);
}
}