made class for settings private and removed settings as input from a custom function

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticDifficultTest.kt

@@ -12,7 +12,6 @@ import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.LMSettings
 import space.kscience.kmath.tensors.core.lm
 import kotlin.math.roundToInt
 
@@ -29,9 +28,9 @@ fun main() {
         p_example[i, 0] = p_example[i, 0] + i - 25
     }
 
-    val settings = LMSettings(0, 0, 1)
+    val exampleNumber = 1
 
-    var y_hat =  funcDifficultForLm(t_example, p_example, settings)
+    var y_hat =  funcDifficultForLm(t_example, p_example, exampleNumber)
 
     var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
     for (i in 0 until Nparams) {
@@ -72,14 +71,14 @@ fun main() {
     )
 
     println("Parameters:")
-    for (i in 0 until result.result_parameters.shape.component1()) {
+    for (i in 0 until result.resultParameters.shape.component1()) {
-        val x = (result.result_parameters[i, 0] * 10000).roundToInt() / 10000.0
+        val x = (result.resultParameters[i, 0] * 10000).roundToInt() / 10000.0
         print("$x ")
     }
     println()
 
     println("Y true and y received:")
-    var y_hat_after =  funcDifficultForLm(t_example, result.result_parameters, settings)
+    var y_hat_after =  funcDifficultForLm(t_example, result.resultParameters, exampleNumber)
     for (i in 0 until y_hat.shape.component1()) {
         val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
         val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
@@ -87,7 +86,7 @@ fun main() {
     }
 
     println("Сhi_sq:")
-    println(result.result_chi_sq)
+    println(result.resultChiSq)
     println("Number of iterations:")
     println(result.iterations)
 }

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticEasyTest.kt

@@ -12,7 +12,6 @@ import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
 import space.kscience.kmath.tensors.LevenbergMarquardt.funcEasyForLm
 import space.kscience.kmath.tensors.LevenbergMarquardt.getStartDataForFuncEasy
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.LMSettings
 import space.kscience.kmath.tensors.core.lm
 import kotlin.math.roundToInt
 
@@ -35,14 +34,14 @@ fun main() {
     )
 
     println("Parameters:")
-    for (i in 0 until result.result_parameters.shape.component1()) {
+    for (i in 0 until result.resultParameters.shape.component1()) {
-        val x = (result.result_parameters[i, 0] * 10000).roundToInt() / 10000.0
+        val x = (result.resultParameters[i, 0] * 10000).roundToInt() / 10000.0
         print("$x ")
     }
     println()
 
     println("Y true and y received:")
-    var y_hat_after =  funcDifficultForLm(startedData.t, result.result_parameters, LMSettings(0, 0, startedData.example_number))
+    var y_hat_after =  funcDifficultForLm(startedData.t, result.resultParameters, startedData.example_number)
     for (i in 0 until startedData.y_dat.shape.component1()) {
         val x = (startedData.y_dat[i, 0] * 10000).roundToInt() / 10000.0
         val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
@@ -50,7 +49,7 @@ fun main() {
     }
 
     println("Сhi_sq:")
-    println(result.result_chi_sq)
+    println(result.resultChiSq)
     println("Number of iterations:")
     println(result.iterations)
 }

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticMiddleTest.kt

@@ -12,8 +12,6 @@ import space.kscience.kmath.tensors.LevenbergMarquardt.funcMiddleForLm
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
-import space.kscience.kmath.tensors.core.LMSettings
 import space.kscience.kmath.tensors.core.lm
 import kotlin.math.roundToInt
 fun main() {
@@ -29,16 +27,15 @@ fun main() {
         p_example[i, 0] = p_example[i, 0] + i - 25
     }
 
-    val settings = LMSettings(0, 0, 1)
+    val exampleNumber = 1
 
-    var y_hat =  funcMiddleForLm(t_example, p_example, settings)
+    var y_hat =  funcMiddleForLm(t_example, p_example, exampleNumber)
 
     var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
     for (i in 0 until Nparams) {
         p_init[i, 0] = (p_example[i, 0] + 0.9)
     }
-//    val p_init = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
+
-//    val p_init = p_example
     var t = t_example
     val y_dat = y_hat
     val weight = BroadcastDoubleTensorAlgebra.fromArray(
@@ -54,7 +51,7 @@ fun main() {
     val consts = BroadcastDoubleTensorAlgebra.fromArray(
         ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
     ).as2D()
-    val opts = doubleArrayOf(3.0, 10000.0, 1e-3, 1e-3, 1e-3, 1e-3, 1e-15, 11.0, 9.0, 1.0)
+    val opts = doubleArrayOf(3.0, 7000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
 
     val result = DoubleTensorAlgebra.lm(
         ::funcMiddleForLm,
@@ -72,14 +69,14 @@ fun main() {
     )
 
     println("Parameters:")
-    for (i in 0 until result.result_parameters.shape.component1()) {
+    for (i in 0 until result.resultParameters.shape.component1()) {
-        val x = (result.result_parameters[i, 0] * 10000).roundToInt() / 10000.0
+        val x = (result.resultParameters[i, 0] * 10000).roundToInt() / 10000.0
         print("$x ")
     }
     println()
 
 
-    var y_hat_after =  funcMiddleForLm(t_example, result.result_parameters, settings)
+    var y_hat_after =  funcMiddleForLm(t_example, result.resultParameters, exampleNumber)
     for (i in 0 until y_hat.shape.component1()) {
         val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
         val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
@@ -87,7 +84,7 @@ fun main() {
     }
 
     println("Сhi_sq:")
-    println(result.result_chi_sq)
+    println(result.resultChiSq)
     println("Number of iterations:")
     println(result.iterations)
 }

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamLm.kt

@@ -11,12 +11,11 @@ import space.kscience.kmath.nd.*
 import space.kscience.kmath.tensors.LevenbergMarquardt.StartDataLm
 import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.zeros
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
-import space.kscience.kmath.tensors.core.LMSettings
 import space.kscience.kmath.tensors.core.lm
 import kotlin.random.Random
 import kotlin.reflect.KFunction3
 
-fun streamLm(lm_func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
+fun streamLm(lm_func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, Int, MutableStructure2D<Double>>,
              startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int): Flow<MutableStructure2D<Double>> = flow{
 
     var example_number = startData.example_number
@@ -48,9 +47,9 @@ fun streamLm(lm_func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<
             10,
             example_number
         )
-        emit(result.result_parameters)
+        emit(result.resultParameters)
         delay(launchFrequencyInMs)
-        p_init = result.result_parameters
+        p_init = result.resultParameters
         y_dat = generateNewYDat(y_dat, 0.1)
         if (!isEndless) steps -= 1
     }

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/functionsToOptimize.kt

@@ -16,7 +16,6 @@ import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.max
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.plus
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.pow
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
-import space.kscience.kmath.tensors.core.LMSettings
 import space.kscience.kmath.tensors.core.asDoubleTensor
 
 public data class StartDataLm (
@@ -33,23 +32,31 @@ public data class StartDataLm (
     var opts: DoubleArray
 )
 
-fun funcDifficultForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
+fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
     val m = t.shape.component1()
-    var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
+    var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
 
-    val mt = t.max()
+    if (exampleNumber == 1) {
-    for(i in 0 until p.shape.component1()){
+        y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0]))).times(p[0, 0]) + t.times(p[2, 0]).times(
-        y_hat = y_hat.plus( (t.times(1.0 / mt)).times(p[i, 0]) )
+            DoubleTensorAlgebra.exp((t.times(-1.0 / p[3, 0])))
+        )
     }
-
+    else if (exampleNumber == 2) {
-    for(i in 0 until 4){
+        val mt = t.max()
-        y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, settings).asDoubleTensor()
+        y_hat = (t.times(1.0 / mt)).times(p[0, 0]) +
+                (t.times(1.0 / mt)).pow(2).times(p[1, 0]) +
+                (t.times(1.0 / mt)).pow(3).times(p[2, 0]) +
+                (t.times(1.0 / mt)).pow(4).times(p[3, 0])
+    }
+    else if (exampleNumber == 3) {
+        y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0])))
+            .times(p[0, 0]) + DoubleTensorAlgebra.sin((t.times(1.0 / p[3, 0]))).times(p[2, 0])
     }
 
     return y_hat.as2D()
 }
 
-fun funcMiddleForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
+fun funcMiddleForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
     val m = t.shape.component1()
     var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
 
@@ -59,36 +66,29 @@ fun funcMiddleForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>
     }
 
     for(i in 0 until 5){
-        y_hat = funcEasyForLm(y_hat.as2D(), p, settings).asDoubleTensor()
+        y_hat = funcEasyForLm(y_hat.as2D(), p, exampleNumber).asDoubleTensor()
     }
 
     return y_hat.as2D()
 }
 
-fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
+fun funcDifficultForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
     val m = t.shape.component1()
     var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
 
-    if (settings.example_number == 1) {
-        y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0]))).times(p[0, 0]) + t.times(p[2, 0]).times(
-            DoubleTensorAlgebra.exp((t.times(-1.0 / p[3, 0])))
-        )
-    }
-    else if (settings.example_number == 2) {
     val mt = t.max()
-        y_hat = (t.times(1.0 / mt)).times(p[0, 0]) +
+    for(i in 0 until p.shape.component1()){
-                (t.times(1.0 / mt)).pow(2).times(p[1, 0]) +
+        y_hat = y_hat.plus( (t.times(1.0 / mt)).times(p[i, 0]) )
-                (t.times(1.0 / mt)).pow(3).times(p[2, 0]) +
-                (t.times(1.0 / mt)).pow(4).times(p[3, 0])
     }
-    else if (settings.example_number == 3) {
+
-        y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0])))
+    for(i in 0 until 4){
-            .times(p[0, 0]) + DoubleTensorAlgebra.sin((t.times(1.0 / p[3, 0]))).times(p[2, 0])
+        y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, exampleNumber).asDoubleTensor()
     }
 
     return y_hat.as2D()
 }
 
+
 fun getStartDataForFuncDifficult(): StartDataLm  {
     val NData = 200
     var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
@@ -102,9 +102,9 @@ fun getStartDataForFuncDifficult(): StartDataLm  {
         p_example[i, 0] = p_example[i, 0] + i - 25
     }
 
-    val settings = LMSettings(0, 0, 1)
+    val exampleNumber = 1
 
-    var y_hat =  funcDifficultForLm(t_example, p_example, settings)
+    var y_hat =  funcDifficultForLm(t_example, p_example, exampleNumber)
 
     var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
     for (i in 0 until Nparams) {
@@ -144,9 +144,9 @@ fun getStartDataForFuncMiddle(): StartDataLm  {
         p_example[i, 0] = p_example[i, 0] + i - 25
     }
 
-    val settings = LMSettings(0, 0, 1)
+    val exampleNumber = 1
 
-    var y_hat =  funcMiddleForLm(t_example, p_example, settings)
+    var y_hat =  funcMiddleForLm(t_example, p_example, exampleNumber)
 
     var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
     for (i in 0 until Nparams) {

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/LevenbergMarquardtAlgorithm.kt

@@ -40,36 +40,39 @@ public enum class TypeOfConvergence{
     NoConvergence
 }
 
+/**
+ * Class for the data obtained as a result of the execution of the Levenberg-Marquardt algorithm
+ *
+ *  iterations: number of completed iterations
+ *  funcCalls: the number of evaluations of the input function during execution
+ *  resultChiSq: chi squared value on final parameters
+ *  resultLambda: final lambda parameter used to calculate the offset
+ *  resultParameters: final parameters
+ *  typeOfConvergence: type of convergence
+ */
 public data class LMResultInfo (
     var iterations:Int,
-    var func_calls: Int,
+    var funcCalls: Int,
-    var example_number: Int,
+    var resultChiSq: Double,
-    var result_chi_sq: Double,
+    var resultLambda: Double,
-    var result_lambda: Double,
+    var resultParameters: MutableStructure2D<Double>,
-    var result_parameters: MutableStructure2D<Double>,
     var typeOfConvergence: TypeOfConvergence,
-    var epsilon: Double
-)
-
-public data class LMSettings (
-    var iteration:Int,
-    var func_calls: Int,
-    var example_number:Int
 )
 
 public fun DoubleTensorAlgebra.lm(
-    func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
+    func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, Int, 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>,
     c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): LMResultInfo {
 
-    val resultInfo = LMResultInfo(0, 0, example_number, 0.0,
+
-        0.0, p_input, TypeOfConvergence.NoConvergence, 0.0)
+    val resultInfo = LMResultInfo(0, 0, 0.0,
+        0.0, p_input, TypeOfConvergence.NoConvergence)
 
     val eps:Double = 2.2204e-16
 
     val settings = LMSettings(0, 0, example_number)
-    settings.func_calls = 0 // running count of function evaluations
+    settings.funcCalls = 0 // running count of function evaluations
 
     var p = p_input
     val y_dat = y_dat_input
@@ -160,7 +163,7 @@ public fun DoubleTensorAlgebra.lm(
     val idx = get_zero_indices(dp)                 // indices of the parameters to be fit
     val Nfit = idx?.shape?.component1()            // number of parameters to fit
     var stop = false                               // termination flag
-    val y_init = feval(func, t, p, settings)       // residual error using p_try
+    val y_init = feval(func, t, p, example_number)       // residual error using p_try
 
     if (weight.shape.component1() == 1 || variance(weight) == 0.0) { // identical weights vector
         weight = ones(ShapeND(intArrayOf(Npnt, 1))).div(1 / kotlin.math.abs(weight[0, 0])).as2D()
@@ -219,7 +222,7 @@ public fun DoubleTensorAlgebra.lm(
         var p_try = (p + h).as2D()  // update the [idx] elements
         p_try = smallest_element_comparison(largest_element_comparison(p_min, p_try.as2D()), p_max)  // apply constraints
 
-        var delta_y = y_dat.minus(feval(func, t, p_try, settings))   // residual error using p_try
+        var delta_y = y_dat.minus(feval(func, t, p_try, example_number))   // residual error using p_try
 
         for (i in 0 until delta_y.shape.component1()) {  // floating point error; break
             for (j in 0 until delta_y.shape.component2()) {
@@ -230,7 +233,7 @@ public fun DoubleTensorAlgebra.lm(
             }
         }
 
-        settings.func_calls += 1
+        settings.funcCalls += 1
 
         val tmp = delta_y.times(weight)
         var X2_try = delta_y.as2D().transpose().dot(tmp)     // Chi-squared error criteria
@@ -244,8 +247,8 @@ public fun DoubleTensorAlgebra.lm(
             p_try = p.plus(h).as2D() // update only [idx] elements
             p_try = smallest_element_comparison(largest_element_comparison(p_min, p_try), p_max) // apply constraints
 
-            var delta_y = y_dat.minus(feval(func, t, p_try, settings))   // residual error using p_try
+            var delta_y = y_dat.minus(feval(func, t, p_try, example_number))   // residual error using p_try
-            settings.func_calls += 1
+            settings.funcCalls += 1
 
             val tmp = delta_y.times(weight)
             X2_try = delta_y.as2D().transpose().dot(tmp)     // Chi-squared error criteria
@@ -320,10 +323,10 @@ public fun DoubleTensorAlgebra.lm(
         if (prnt > 1) {
             val chi_sq = X2 / DoF
             resultInfo.iterations = settings.iteration
-            resultInfo.func_calls = settings.func_calls
+            resultInfo.funcCalls = settings.funcCalls
-            resultInfo.result_chi_sq = chi_sq
+            resultInfo.resultChiSq = chi_sq
-            resultInfo.result_lambda = lambda
+            resultInfo.resultLambda = lambda
-            resultInfo.result_parameters = p
+            resultInfo.resultParameters = p
         }
 
         // update convergence history ... save _reduced_ Chi-square
@@ -331,30 +334,32 @@ public fun DoubleTensorAlgebra.lm(
 
         if (abs(JtWdy).max()!! < epsilon_1 && settings.iteration > 2) {
             resultInfo.typeOfConvergence = TypeOfConvergence.InGradient
-            resultInfo.epsilon = epsilon_1
             stop = true
         }
         if ((abs(h.as2D()).div(abs(p) + 1e-12)).max() < epsilon_2  &&  settings.iteration > 2) {
             resultInfo.typeOfConvergence = TypeOfConvergence.InParameters
-            resultInfo.epsilon = epsilon_2
             stop = true
         }
         if (X2 / DoF < epsilon_3 && settings.iteration > 2) {
             resultInfo.typeOfConvergence = TypeOfConvergence.InReducedChiSquare
-            resultInfo.epsilon = epsilon_3
             stop = true
         }
         if (settings.iteration == MaxIter) {
             resultInfo.typeOfConvergence = TypeOfConvergence.NoConvergence
-            resultInfo.epsilon = 0.0
             stop = true
         }
     }
     return resultInfo
 }
 
+private data class LMSettings (
+    var iteration:Int,
+    var funcCalls: Int,
+    var exampleNumber:Int
+)
+
 /* matrix -> column of all elemnets */
-public fun make_column(tensor: MutableStructure2D<Double>) : MutableStructure2D<Double> {
+private fun make_column(tensor: MutableStructure2D<Double>) : MutableStructure2D<Double> {
     val shape = intArrayOf(tensor.shape.component1() * tensor.shape.component2(), 1)
     val buffer = DoubleArray(tensor.shape.component1() * tensor.shape.component2())
     for (i in 0 until tensor.shape.component1()) {
@@ -367,11 +372,11 @@ public fun make_column(tensor: MutableStructure2D<Double>) : MutableStructure2D<
 }
 
 /* column length */
-public fun length(column: MutableStructure2D<Double>) : Int {
+private fun length(column: MutableStructure2D<Double>) : Int {
     return column.shape.component1()
 }
 
-public fun MutableStructure2D<Double>.abs() {
+private fun MutableStructure2D<Double>.abs() {
     for (i in 0 until this.shape.component1()) {
         for (j in 0 until this.shape.component2()) {
             this[i, j] = kotlin.math.abs(this[i, j])
@@ -379,7 +384,7 @@ public fun MutableStructure2D<Double>.abs() {
     }
 }
 
-public fun abs(input: MutableStructure2D<Double>): MutableStructure2D<Double> {
+private fun abs(input: MutableStructure2D<Double>): MutableStructure2D<Double> {
     val tensor = BroadcastDoubleTensorAlgebra.ones(
         ShapeND(
             intArrayOf(
@@ -396,7 +401,7 @@ public fun abs(input: MutableStructure2D<Double>): MutableStructure2D<Double> {
     return tensor
 }
 
-public fun diag(input: MutableStructure2D<Double>): MutableStructure2D<Double> {
+private fun diag(input: MutableStructure2D<Double>): MutableStructure2D<Double> {
     val tensor = BroadcastDoubleTensorAlgebra.ones(ShapeND(intArrayOf(input.shape.component1(), 1))).as2D()
     for (i in 0 until tensor.shape.component1()) {
         tensor[i, 0] = input[i, i]
@@ -404,7 +409,7 @@ public fun diag(input: MutableStructure2D<Double>): MutableStructure2D<Double> {
     return tensor
 }
 
-public fun make_matrx_with_diagonal(column: MutableStructure2D<Double>): MutableStructure2D<Double> {
+private fun make_matrx_with_diagonal(column: MutableStructure2D<Double>): MutableStructure2D<Double> {
     val size = column.shape.component1()
     val tensor = BroadcastDoubleTensorAlgebra.zeros(ShapeND(intArrayOf(size, size))).as2D()
     for (i in 0 until size) {
@@ -413,12 +418,12 @@ public fun make_matrx_with_diagonal(column: MutableStructure2D<Double>): Mutable
     return tensor
 }
 
-public fun lm_eye(size: Int): MutableStructure2D<Double> {
+private fun lm_eye(size: Int): MutableStructure2D<Double> {
     val column = BroadcastDoubleTensorAlgebra.ones(ShapeND(intArrayOf(size, 1))).as2D()
     return make_matrx_with_diagonal(column)
 }
 
-public fun largest_element_comparison(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double> {
+private fun largest_element_comparison(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double> {
     val a_sizeX = a.shape.component1()
     val a_sizeY = a.shape.component2()
     val b_sizeX = b.shape.component1()
@@ -440,7 +445,7 @@ public fun largest_element_comparison(a: MutableStructure2D<Double>, b: MutableS
     return tensor
 }
 
-public fun smallest_element_comparison(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double> {
+private fun smallest_element_comparison(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double> {
     val a_sizeX = a.shape.component1()
     val a_sizeY = a.shape.component2()
     val b_sizeX = b.shape.component1()
@@ -462,7 +467,7 @@ public fun smallest_element_comparison(a: MutableStructure2D<Double>, b: Mutable
     return tensor
 }
 
-public fun get_zero_indices(column: MutableStructure2D<Double>, epsilon: Double = 0.000001): MutableStructure2D<Double>? {
+private fun get_zero_indices(column: MutableStructure2D<Double>, epsilon: Double = 0.000001): MutableStructure2D<Double>? {
     var idx = emptyArray<Double>()
     for (i in 0 until column.shape.component1()) {
         if (kotlin.math.abs(column[i, 0]) > epsilon) {
@@ -475,14 +480,14 @@ public fun get_zero_indices(column: MutableStructure2D<Double>, epsilon: Double
     return null
 }
 
-public fun feval(func: (MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings) ->  MutableStructure2D<Double>,
+private fun feval(func: (MutableStructure2D<Double>, MutableStructure2D<Double>, Int) ->  MutableStructure2D<Double>,
-                 t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings)
+                 t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int)
         : MutableStructure2D<Double>
 {
-    return func(t, p, settings)
+    return func(t, p, exampleNumber)
 }
 
-public fun lm_matx(func: (MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings) -> MutableStructure2D<Double>,
+private fun lm_matx(func: (MutableStructure2D<Double>, MutableStructure2D<Double>, Int) -> MutableStructure2D<Double>,
                    t: MutableStructure2D<Double>, p_old: MutableStructure2D<Double>, y_old: MutableStructure2D<Double>,
                    dX2: Int, J_input: MutableStructure2D<Double>, p: MutableStructure2D<Double>,
                    y_dat: MutableStructure2D<Double>, weight: MutableStructure2D<Double>, dp:MutableStructure2D<Double>, settings:LMSettings) : Array<MutableStructure2D<Double>>
@@ -492,8 +497,8 @@ public fun lm_matx(func: (MutableStructure2D<Double>, MutableStructure2D<Double>
     val Npnt = length(y_dat)               // number of data points
     val Npar = length(p)                   // number of parameters
 
-    val y_hat = feval(func, t, p, settings)          // evaluate model using parameters 'p'
+    val y_hat = feval(func, t, p, settings.exampleNumber)          // evaluate model using parameters 'p'
-    settings.func_calls += 1
+    settings.funcCalls += 1
 
     var J = J_input
 
@@ -513,7 +518,7 @@ public fun lm_matx(func: (MutableStructure2D<Double>, MutableStructure2D<Double>
     return arrayOf(JtWJ,JtWdy,Chi_sq,y_hat,J)
 }
 
-public fun lm_Broyden_J(p_old: MutableStructure2D<Double>, y_old: MutableStructure2D<Double>, J_input: MutableStructure2D<Double>,
+private fun lm_Broyden_J(p_old: MutableStructure2D<Double>, y_old: MutableStructure2D<Double>, J_input: MutableStructure2D<Double>,
                         p: MutableStructure2D<Double>, y: MutableStructure2D<Double>): MutableStructure2D<Double> {
     var J = J_input.copyToTensor()
 
@@ -524,7 +529,7 @@ public fun lm_Broyden_J(p_old: MutableStructure2D<Double>, y_old: MutableStructu
     return J.as2D()
 }
 
-public fun lm_FD_J(func: (MutableStructure2D<Double>, MutableStructure2D<Double>, settings: LMSettings) -> MutableStructure2D<Double>,
+private fun lm_FD_J(func: (MutableStructure2D<Double>, MutableStructure2D<Double>, exampleNumber: Int) -> MutableStructure2D<Double>,
                    t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, y: MutableStructure2D<Double>,
                    dp: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
     // default: dp = 0.001 * ones(1,n)
@@ -543,8 +548,8 @@ public fun lm_FD_J(func: (MutableStructure2D<Double>, MutableStructure2D<Double>
 
         val epsilon = 0.0000001
         if (kotlin.math.abs(del[j, 0]) > epsilon) {
-            val y1 = feval(func, t, p, settings)
+            val y1 = feval(func, t, p, settings.exampleNumber)
-            settings.func_calls += 1
+            settings.funcCalls += 1
 
             if (dp[j, 0] < 0) { // backwards difference
                 for (i in 0 until J.shape.component1()) {
@@ -556,9 +561,9 @@ public fun lm_FD_J(func: (MutableStructure2D<Double>, MutableStructure2D<Double>
                 println("Potential mistake")
                 p[j, 0] = ps[j, 0] - del[j, 0] // central difference, additional func call
                 for (i in 0 until J.shape.component1()) {
-                    J[i, j] = (y1.as2D().minus(feval(func, t, p, settings)).as2D())[i, 0] / (2 * del[j, 0])
+                    J[i, j] = (y1.as2D().minus(feval(func, t, p, settings.exampleNumber)).as2D())[i, 0] / (2 * del[j, 0])
                 }
-                settings.func_calls += 1
+                settings.funcCalls += 1
             }
         }
 

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestLmAlgorithm.kt

@@ -20,23 +20,23 @@ import kotlin.test.assertEquals
 
 class TestLmAlgorithm {
     companion object {
-        fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
+        fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
             val m = t.shape.component1()
             var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
 
-            if (settings.example_number == 1) {
+            if (exampleNumber == 1) {
                 y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0]))).times(p[0, 0]) + t.times(p[2, 0]).times(
                     DoubleTensorAlgebra.exp((t.times(-1.0 / p[3, 0])))
                 )
             }
-            else if (settings.example_number == 2) {
+            else if (exampleNumber == 2) {
                 val mt = t.max()
                 y_hat = (t.times(1.0 / mt)).times(p[0, 0]) +
                         (t.times(1.0 / mt)).pow(2).times(p[1, 0]) +
                         (t.times(1.0 / mt)).pow(3).times(p[2, 0]) +
                         (t.times(1.0 / mt)).pow(4).times(p[3, 0])
             }
-            else if (settings.example_number == 3) {
+            else if (exampleNumber == 3) {
                 y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0])))
                     .times(p[0, 0]) + DoubleTensorAlgebra.sin((t.times(1.0 / p[3, 0]))).times(p[2, 0])
             }
@@ -44,7 +44,7 @@ class TestLmAlgorithm {
             return y_hat.as2D()
         }
 
-        fun funcMiddleForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
+        fun funcMiddleForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
             val m = t.shape.component1()
             var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
 
@@ -54,13 +54,13 @@ class TestLmAlgorithm {
             }
 
             for(i in 0 until 5){
-                y_hat = funcEasyForLm(y_hat.as2D(), p, settings).asDoubleTensor()
+                y_hat = funcEasyForLm(y_hat.as2D(), p, exampleNumber).asDoubleTensor()
             }
 
             return y_hat.as2D()
         }
 
-        fun funcDifficultForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
+        fun funcDifficultForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
             val m = t.shape.component1()
             var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
 
@@ -70,12 +70,11 @@ class TestLmAlgorithm {
             }
 
             for(i in 0 until 4){
-                y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, settings).asDoubleTensor()
+                y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, exampleNumber).asDoubleTensor()
             }
 
             return y_hat.as2D()
         }
-
     }
     @Test
     fun testLMEasy() = DoubleTensorAlgebra {
@@ -130,18 +129,17 @@ class TestLmAlgorithm {
 
         val result = lm(::funcEasyForLm, p_init, t, y_dat, weight, dp, p_min, p_max, consts, opts, 10, example_number)
         assertEquals(13, result.iterations)
-        assertEquals(31, result.func_calls)
+        assertEquals(31, result.funcCalls)
-        assertEquals(1, result.example_number)
+        assertEquals(0.9131368192633, (result.resultChiSq * 1e13).roundToLong() / 1e13)
-        assertEquals(0.9131368192633, (result.result_chi_sq * 1e13).roundToLong() / 1e13)
+        assertEquals(3.7790980 * 1e-7, (result.resultLambda * 1e13).roundToLong() / 1e13)
-        assertEquals(3.7790980 * 1e-7, (result.result_lambda * 1e13).roundToLong() / 1e13)
         assertEquals(result.typeOfConvergence, TypeOfConvergence.InParameters)
         val expectedParameters = BroadcastDoubleTensorAlgebra.fromArray(
             ShapeND(intArrayOf(4, 1)), doubleArrayOf(20.527230909086, 9.833627103230, 0.997571256572, 50.174445822506)
         ).as2D()
-        result.result_parameters = result.result_parameters.map { x -> (x * 1e12).toLong() / 1e12}.as2D()
+        result.resultParameters = result.resultParameters.map { x -> (x * 1e12).toLong() / 1e12}.as2D()
         val receivedParameters = BroadcastDoubleTensorAlgebra.fromArray(
-            ShapeND(intArrayOf(4, 1)), doubleArrayOf(result.result_parameters[0, 0], result.result_parameters[1, 0],
+            ShapeND(intArrayOf(4, 1)), doubleArrayOf(result.resultParameters[0, 0], result.resultParameters[1, 0],
-                result.result_parameters[2, 0], result.result_parameters[3, 0])
+                result.resultParameters[2, 0], result.resultParameters[3, 0])
         ).as2D()
         assertEquals(expectedParameters[0, 0], receivedParameters[0, 0])
         assertEquals(expectedParameters[1, 0], receivedParameters[1, 0])
@@ -163,9 +161,9 @@ class TestLmAlgorithm {
             p_example[i, 0] = p_example[i, 0] + i - 25
         }
 
-        val settings = LMSettings(0, 0, 1)
+        val exampleNumber = 1
 
-        var y_hat =  funcMiddleForLm(t_example, p_example, settings)
+        var y_hat =  funcMiddleForLm(t_example, p_example, exampleNumber)
 
         var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
         for (i in 0 until Nparams) {
@@ -219,9 +217,9 @@ class TestLmAlgorithm {
             p_example[i, 0] = p_example[i, 0] + i - 25
         }
 
-        val settings = LMSettings(0, 0, 1)
+        val exampleNumber = 1
 
-        var y_hat =  funcDifficultForLm(t_example, p_example, settings)
+        var y_hat =  funcDifficultForLm(t_example, p_example, exampleNumber)
 
         var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
         for (i in 0 until Nparams) {