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@ -10,7 +10,7 @@ plugins {
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description = "Symja integration module"
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dependencies {
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api("org.matheclipse:matheclipse-core:2.0.0-SNAPSHOT") {
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api("org.matheclipse:matheclipse-core:2.0.0") {
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// Incorrect transitive dependencies
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exclude("org.apfloat", "apfloat")
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exclude("org.hipparchus", "hipparchus-clustering")
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@ -111,22 +111,4 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
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* @return the square matrix x which is the solution of the equation.
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*/
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public fun solve(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double>
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public enum class TypeOfConvergence{
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inRHS_JtWdy,
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inParameters,
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inReducedChi_square,
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noConvergence
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}
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public data class LMResultInfo (
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var iterations:Int,
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var func_calls: Int,
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var example_number: Int,
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var result_chi_sq: Double,
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var result_lambda: Double,
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var result_parameters: MutableStructure2D<Double>,
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var typeOfConvergence: TypeOfConvergence,
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var epsilon: Double
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)
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}
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@ -19,14 +19,32 @@ import kotlin.math.min
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import kotlin.math.pow
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import kotlin.reflect.KFunction3
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public enum class TypeOfConvergence{
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inRHS_JtWdy,
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inParameters,
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inReducedChi_square,
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noConvergence
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}
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public data class LMResultInfo (
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var iterations:Int,
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var func_calls: Int,
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var example_number: Int,
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var result_chi_sq: Double,
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var result_lambda: Double,
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var result_parameters: MutableStructure2D<Double>,
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var typeOfConvergence: TypeOfConvergence,
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var epsilon: Double
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)
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public fun DoubleTensorAlgebra.lm(
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func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
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p_input: MutableStructure2D<Double>, t_input: MutableStructure2D<Double>, y_dat_input: MutableStructure2D<Double>,
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weight_input: MutableStructure2D<Double>, dp_input: MutableStructure2D<Double>, p_min_input: MutableStructure2D<Double>, p_max_input: MutableStructure2D<Double>,
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c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): LinearOpsTensorAlgebra.LMResultInfo {
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c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): LMResultInfo {
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val resultInfo = LinearOpsTensorAlgebra.LMResultInfo(0, 0, example_number, 0.0,
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0.0, p_input, LinearOpsTensorAlgebra.TypeOfConvergence.noConvergence, 0.0)
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val resultInfo = LMResultInfo(0, 0, example_number, 0.0,
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0.0, p_input, TypeOfConvergence.noConvergence, 0.0)
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val eps:Double = 2.2204e-16
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@ -303,27 +321,27 @@ public fun DoubleTensorAlgebra.lm(
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if (abs(JtWdy).max()!! < epsilon_1 && settings.iteration > 2) {
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// println(" **** Convergence in r.h.s. (\"JtWdy\") ****")
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// println(" **** epsilon_1 = $epsilon_1")
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resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.inRHS_JtWdy
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resultInfo.typeOfConvergence = TypeOfConvergence.inRHS_JtWdy
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resultInfo.epsilon = epsilon_1
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stop = true
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}
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if ((abs(h.as2D()).div(abs(p) + 1e-12)).max() < epsilon_2 && settings.iteration > 2) {
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// println(" **** Convergence in Parameters ****")
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// println(" **** epsilon_2 = $epsilon_2")
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resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.inParameters
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resultInfo.typeOfConvergence = TypeOfConvergence.inParameters
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resultInfo.epsilon = epsilon_2
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stop = true
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}
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if (X2 / DoF < epsilon_3 && settings.iteration > 2) {
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// println(" **** Convergence in reduced Chi-square **** ")
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// println(" **** epsilon_3 = $epsilon_3")
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resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.inReducedChi_square
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resultInfo.typeOfConvergence = TypeOfConvergence.inReducedChi_square
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resultInfo.epsilon = epsilon_3
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stop = true
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}
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if (settings.iteration == MaxIter) {
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// println(" !! Maximum Number of Iterations Reached Without Convergence !!")
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resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.noConvergence
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resultInfo.typeOfConvergence = TypeOfConvergence.noConvergence
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resultInfo.epsilon = 0.0
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stop = true
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}
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