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A

ai.kognition.pilecv4j.nr - package ai.kognition.pilecv4j.nr

E

error - Variable in class ai.kognition.pilecv4j.nr.Minimizer.FinalPosition

eval(Pointer, Pointer) - Method in interface ai.kognition.pilecv4j.nr.MinimizerAPI.EvalCallback

F

ftol - Static variable in class ai.kognition.pilecv4j.nr.Minimizer

Default float tolerance.

func(double[]) - Method in class ai.kognition.pilecv4j.nr.LinearRegression

Deprecated.

func(double[]) - Method in class ai.kognition.pilecv4j.nr.LinearRegressionWithKnownSlope

func(double[]) - Method in interface ai.kognition.pilecv4j.nr.Minimizer.Func

func(double[]) - Method in class ai.kognition.pilecv4j.nr.SimpleLinearRegression

G

getFinalPostion() - Method in class ai.kognition.pilecv4j.nr.Minimizer

Return the final domain value of the minimized solution.

L

LIBNAME - Static variable in class ai.kognition.pilecv4j.nr.MinimizerAPI

LinearRegression - Class in ai.kognition.pilecv4j.nr

Deprecated.

LinearRegression(double[], double[]) - Constructor for class ai.kognition.pilecv4j.nr.LinearRegression

Deprecated.

LinearRegressionWithKnownSlope - Class in ai.kognition.pilecv4j.nr

This class will do a linear regression by minimizing the squared error between the points provided to the constructor and the line specified by y = m[0]x + m[1]

LinearRegressionWithKnownSlope(double, double[], double[]) - Constructor for class ai.kognition.pilecv4j.nr.LinearRegressionWithKnownSlope

M

minimize(double[]) - Method in class ai.kognition.pilecv4j.nr.Minimizer

Minimize the function that the Minimizer was instantiated with using the identity matrix as the starting position.

minimize(double[], double[][]) - Method in class ai.kognition.pilecv4j.nr.Minimizer

Minimize the function that the Minimizer was instantiated with using the supplied starting position.

minimize(Minimizer.Func, double[]) - Static method in class ai.kognition.pilecv4j.nr.Minimizer

Minimizer - Class in ai.kognition.pilecv4j.nr

This class encapsulates the running of Powell's Method on a given function in order to determine a local minimum.

Minimizer(Minimizer.Func) - Constructor for class ai.kognition.pilecv4j.nr.Minimizer

Construct the minimizer with the function to be minimized.

Minimizer.FinalPosition - Class in ai.kognition.pilecv4j.nr

Minimizer.Func - Interface in ai.kognition.pilecv4j.nr

Interface representing the function/lambda to be minimized.

MinimizerAPI - Class in ai.kognition.pilecv4j.nr

MinimizerAPI() - Constructor for class ai.kognition.pilecv4j.nr.MinimizerAPI

MinimizerAPI.EvalCallback - Interface in ai.kognition.pilecv4j.nr

MinimizerException - Exception in ai.kognition.pilecv4j.nr

MinimizerException() - Constructor for exception ai.kognition.pilecv4j.nr.MinimizerException

MinimizerException(String) - Constructor for exception ai.kognition.pilecv4j.nr.MinimizerException

MinimizerException(String, Throwable) - Constructor for exception ai.kognition.pilecv4j.nr.MinimizerException

P

pilecv4j_image_dominimize(MinimizerAPI.EvalCallback, int, double[], double[], double, double[], int[]) - Static method in class ai.kognition.pilecv4j.nr.MinimizerAPI

pilecv4j_image_nrGetErrorMessage() - Static method in class ai.kognition.pilecv4j.nr.MinimizerAPI

position - Variable in class ai.kognition.pilecv4j.nr.Minimizer.FinalPosition

S

SimpleLinearRegression - Class in ai.kognition.pilecv4j.nr

This class will do a linear regression by minimizing the squared error between the points provided to the constructor and the line specified by y = m[0]x + m[1]

SimpleLinearRegression(double[], double[]) - Constructor for class ai.kognition.pilecv4j.nr.SimpleLinearRegression

T

toString() - Method in class ai.kognition.pilecv4j.nr.Minimizer.FinalPosition

X

x - Variable in class ai.kognition.pilecv4j.nr.SimpleLinearRegression

Y

y - Variable in class ai.kognition.pilecv4j.nr.SimpleLinearRegression

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