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This commit is contained in:
Margarita Lashina 2023-06-06 20:07:42 +03:00
parent c017d58265
commit 963e14b00a
3 changed files with 26 additions and 26 deletions
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2
kmath-symja/build.gradle.kts
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@ -10,7 +10,7 @@ plugins {
description = "Symja integration module" description = "Symja integration module"
dependencies { dependencies {
api("org.matheclipse:matheclipse-core:2.0.0-SNAPSHOT") { api("org.matheclipse:matheclipse-core:2.0.0") {
// Incorrect transitive dependencies // Incorrect transitive dependencies
exclude("org.apfloat", "apfloat") exclude("org.apfloat", "apfloat")
exclude("org.hipparchus", "hipparchus-clustering") exclude("org.hipparchus", "hipparchus-clustering")

18
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
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@ -111,22 +111,4 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
* @return the square matrix x which is the solution of the equation. * @return the square matrix x which is the solution of the equation.
*/ */
public fun solve(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double> public fun solve(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double>
public enum class TypeOfConvergence{
inRHS_JtWdy,
inParameters,
inReducedChi_square,
noConvergence
}
public data class LMResultInfo (
var iterations:Int,
var func_calls: Int,
var example_number: Int,
var result_chi_sq: Double,
var result_lambda: Double,
var result_parameters: MutableStructure2D<Double>,
var typeOfConvergence: TypeOfConvergence,
var epsilon: Double
)
} }

32
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/LevenbergMarquardtAlgorithm.kt
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@ -19,14 +19,32 @@ import kotlin.math.min
import kotlin.math.pow import kotlin.math.pow
import kotlin.reflect.KFunction3 import kotlin.reflect.KFunction3
public enum class TypeOfConvergence{
inRHS_JtWdy,
inParameters,
inReducedChi_square,
noConvergence
}
public data class LMResultInfo (
var iterations:Int,
var func_calls: Int,
var example_number: Int,
var result_chi_sq: Double,
var result_lambda: Double,
var result_parameters: MutableStructure2D<Double>,
var typeOfConvergence: TypeOfConvergence,
var epsilon: Double
)
public fun DoubleTensorAlgebra.lm( public fun DoubleTensorAlgebra.lm(
func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>, func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
p_input: MutableStructure2D<Double>, t_input: MutableStructure2D<Double>, y_dat_input: MutableStructure2D<Double>, p_input: MutableStructure2D<Double>, t_input: MutableStructure2D<Double>, y_dat_input: MutableStructure2D<Double>,
weight_input: MutableStructure2D<Double>, dp_input: MutableStructure2D<Double>, p_min_input: MutableStructure2D<Double>, p_max_input: MutableStructure2D<Double>, weight_input: MutableStructure2D<Double>, dp_input: MutableStructure2D<Double>, p_min_input: MutableStructure2D<Double>, p_max_input: MutableStructure2D<Double>,
c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): LinearOpsTensorAlgebra.LMResultInfo { c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): LMResultInfo {
val resultInfo = LinearOpsTensorAlgebra.LMResultInfo(0, 0, example_number, 0.0, val resultInfo = LMResultInfo(0, 0, example_number, 0.0,
0.0, p_input, LinearOpsTensorAlgebra.TypeOfConvergence.noConvergence, 0.0) 0.0, p_input, TypeOfConvergence.noConvergence, 0.0)
val eps:Double = 2.2204e-16 val eps:Double = 2.2204e-16
@ -303,27 +321,27 @@ public fun DoubleTensorAlgebra.lm(
if (abs(JtWdy).max()!! < epsilon_1 && settings.iteration > 2) { if (abs(JtWdy).max()!! < epsilon_1 && settings.iteration > 2) {
// println(" **** Convergence in r.h.s. (\"JtWdy\") ****") // println(" **** Convergence in r.h.s. (\"JtWdy\") ****")
// println(" **** epsilon_1 = $epsilon_1") // println(" **** epsilon_1 = $epsilon_1")
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.inRHS_JtWdy resultInfo.typeOfConvergence = TypeOfConvergence.inRHS_JtWdy
resultInfo.epsilon = epsilon_1 resultInfo.epsilon = epsilon_1
stop = true stop = true
} }
if ((abs(h.as2D()).div(abs(p) + 1e-12)).max() < epsilon_2 && settings.iteration > 2) { if ((abs(h.as2D()).div(abs(p) + 1e-12)).max() < epsilon_2 && settings.iteration > 2) {
// println(" **** Convergence in Parameters ****") // println(" **** Convergence in Parameters ****")
// println(" **** epsilon_2 = $epsilon_2") // println(" **** epsilon_2 = $epsilon_2")
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.inParameters resultInfo.typeOfConvergence = TypeOfConvergence.inParameters
resultInfo.epsilon = epsilon_2 resultInfo.epsilon = epsilon_2
stop = true stop = true
} }
if (X2 / DoF < epsilon_3 && settings.iteration > 2) { if (X2 / DoF < epsilon_3 && settings.iteration > 2) {
// println(" **** Convergence in reduced Chi-square **** ") // println(" **** Convergence in reduced Chi-square **** ")
// println(" **** epsilon_3 = $epsilon_3") // println(" **** epsilon_3 = $epsilon_3")
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.inReducedChi_square resultInfo.typeOfConvergence = TypeOfConvergence.inReducedChi_square
resultInfo.epsilon = epsilon_3 resultInfo.epsilon = epsilon_3
stop = true stop = true
} }
if (settings.iteration == MaxIter) { if (settings.iteration == MaxIter) {
// println(" !! Maximum Number of Iterations Reached Without Convergence !!") // println(" !! Maximum Number of Iterations Reached Without Convergence !!")
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.noConvergence resultInfo.typeOfConvergence = TypeOfConvergence.noConvergence
resultInfo.epsilon = 0.0 resultInfo.epsilon = 0.0
stop = true stop = true
} }