forked from kscience/kmath
the input data is placed in a separate class, to which the documentation is written
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@ -12,7 +12,8 @@ import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.lm
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import space.kscience.kmath.tensors.core.LMInput
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import space.kscience.kmath.tensors.core.levenbergMarquardt
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import kotlin.math.roundToInt
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fun main() {
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@ -39,9 +40,7 @@ fun main() {
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var t = t_example
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val y_dat = y_hat
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val weight = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 / Nparams * 1.0 - 0.085 }
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).as2D()
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val weight = 1.0 / Nparams * 1.0 - 0.085
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val dp = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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).as2D()
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@ -52,8 +51,7 @@ fun main() {
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val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-2, 11.0, 9.0, 1.0)
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// val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-3, 11.0, 9.0, 1.0)
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val result = DoubleTensorAlgebra.lm(
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::funcDifficultForLm,
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val inputData = LMInput(::funcDifficultForLm,
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p_init.as2D(),
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t,
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y_dat,
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@ -61,10 +59,14 @@ fun main() {
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dp,
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p_min.as2D(),
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p_max.as2D(),
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opts,
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opts[1].toInt(),
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doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
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doubleArrayOf(opts[6], opts[7], opts[8]),
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opts[9].toInt(),
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10,
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1
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)
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1)
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val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
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println("Parameters:")
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for (i in 0 until result.resultParameters.shape.component1()) {
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@ -12,14 +12,13 @@ import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
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import space.kscience.kmath.tensors.LevenbergMarquardt.funcEasyForLm
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import space.kscience.kmath.tensors.LevenbergMarquardt.getStartDataForFuncEasy
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.lm
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import space.kscience.kmath.tensors.core.LMInput
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import space.kscience.kmath.tensors.core.levenbergMarquardt
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import kotlin.math.roundToInt
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fun main() {
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val startedData = getStartDataForFuncEasy()
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val result = DoubleTensorAlgebra.lm(
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::funcEasyForLm,
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val inputData = LMInput(::funcEasyForLm,
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DoubleTensorAlgebra.ones(ShapeND(intArrayOf(4, 1))).as2D(),
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startedData.t,
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startedData.y_dat,
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@ -27,10 +26,14 @@ fun main() {
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startedData.dp,
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startedData.p_min,
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startedData.p_max,
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startedData.opts,
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startedData.opts[1].toInt(),
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doubleArrayOf(startedData.opts[2], startedData.opts[3], startedData.opts[4], startedData.opts[5]),
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doubleArrayOf(startedData.opts[6], startedData.opts[7], startedData.opts[8]),
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startedData.opts[9].toInt(),
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10,
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startedData.example_number
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)
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startedData.example_number)
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val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
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println("Parameters:")
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for (i in 0 until result.resultParameters.shape.component1()) {
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@ -12,7 +12,8 @@ import space.kscience.kmath.tensors.LevenbergMarquardt.funcMiddleForLm
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.lm
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import space.kscience.kmath.tensors.core.LMInput
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import space.kscience.kmath.tensors.core.levenbergMarquardt
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import kotlin.math.roundToInt
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fun main() {
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val NData = 100
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@ -38,9 +39,7 @@ fun main() {
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var t = t_example
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val y_dat = y_hat
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val weight = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 }
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).as2D()
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val weight = 1.0
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val dp = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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).as2D()
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@ -50,8 +49,7 @@ fun main() {
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p_min = p_min.div(1.0 / 50.0)
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val opts = doubleArrayOf(3.0, 7000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
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val result = DoubleTensorAlgebra.lm(
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::funcMiddleForLm,
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val inputData = LMInput(::funcMiddleForLm,
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p_init.as2D(),
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t,
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y_dat,
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@ -59,10 +57,14 @@ fun main() {
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dp,
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p_min.as2D(),
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p_max.as2D(),
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opts,
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opts[1].toInt(),
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doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
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doubleArrayOf(opts[6], opts[7], opts[8]),
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opts[9].toInt(),
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10,
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1
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)
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1)
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val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
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println("Parameters:")
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for (i in 0 until result.resultParameters.shape.component1()) {
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@ -11,7 +11,8 @@ import space.kscience.kmath.nd.*
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import space.kscience.kmath.tensors.LevenbergMarquardt.StartDataLm
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.zeros
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.lm
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import space.kscience.kmath.tensors.core.LMInput
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import space.kscience.kmath.tensors.core.levenbergMarquardt
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import kotlin.random.Random
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import kotlin.reflect.KFunction3
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@ -31,9 +32,7 @@ fun streamLm(lm_func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<
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var steps = numberOfLaunches
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val isEndless = (steps <= 0)
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while (isEndless || steps > 0) {
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val result = DoubleTensorAlgebra.lm(
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lm_func,
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val inputData = LMInput(lm_func,
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p_init,
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t,
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y_dat,
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@ -41,10 +40,15 @@ fun streamLm(lm_func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<
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dp,
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p_min,
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p_max,
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opts,
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opts[1].toInt(),
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doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
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doubleArrayOf(opts[6], opts[7], opts[8]),
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opts[9].toInt(),
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10,
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example_number
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)
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example_number)
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while (isEndless || steps > 0) {
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val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
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emit(result.resultParameters)
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delay(launchFrequencyInMs)
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p_init = result.resultParameters
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@ -24,7 +24,7 @@ public data class StartDataLm (
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var p_init: MutableStructure2D<Double>,
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var t: MutableStructure2D<Double>,
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var y_dat: MutableStructure2D<Double>,
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var weight: MutableStructure2D<Double>,
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var weight: Double,
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var dp: MutableStructure2D<Double>,
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var p_min: MutableStructure2D<Double>,
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var p_max: MutableStructure2D<Double>,
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@ -113,9 +113,7 @@ fun getStartDataForFuncDifficult(): StartDataLm {
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var t = t_example
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val y_dat = y_hat
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val weight = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 / Nparams * 1.0 - 0.085 }
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).as2D()
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val weight = 1.0 / Nparams * 1.0 - 0.085
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val dp = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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).as2D()
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@ -154,9 +152,7 @@ fun getStartDataForFuncMiddle(): StartDataLm {
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}
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var t = t_example
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val y_dat = y_hat
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val weight = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 }
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).as2D()
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val weight = 1.0
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val dp = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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).as2D()
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@ -202,9 +198,7 @@ fun getStartDataForFuncEasy(): StartDataLm {
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ShapeND(intArrayOf(100, 1)), lm_matx_y_dat
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).as2D()
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val weight = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 4.0 }
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).as2D()
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val weight = 4.0
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val dp = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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@ -19,21 +19,21 @@ import kotlin.math.pow
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import kotlin.reflect.KFunction3
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/**
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* Type of convergence achieved as a result of executing the Levenberg-Marquardt algorithm
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* Type of convergence achieved as a result of executing the Levenberg-Marquardt algorithm.
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*
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* InGradient: gradient convergence achieved
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* (max(J^T W dy) < epsilon1 = opts[2],
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* (max(J^T W dy) < epsilon1,
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* where J - Jacobi matrix (dy^/dp) for the current approximation y^,
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* W - weight matrix from input, dy = (y - y^(p)))
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* W - weight matrix from input, dy = (y - y^(p))).
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* InParameters: convergence in parameters achieved
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* (max(h_i / p_i) < epsilon2 = opts[3],
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* where h_i - offset for parameter p_i on the current iteration)
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* (max(h_i / p_i) < epsilon2,
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* where h_i - offset for parameter p_i on the current iteration).
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* InReducedChiSquare: chi-squared convergence achieved
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* (chi squared value divided by (m - n + 1) < epsilon2 = opts[4],
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* where n - number of parameters, m - amount of points
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* NoConvergence: the maximum number of iterations has been reached without reaching any convergence
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* (chi squared value divided by (m - n + 1) < epsilon2,
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* where n - number of parameters, m - amount of points).
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* NoConvergence: the maximum number of iterations has been reached without reaching any convergence.
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*/
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public enum class TypeOfConvergence{
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public enum class TypeOfConvergence {
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InGradient,
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InParameters,
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InReducedChiSquare,
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@ -41,14 +41,14 @@ public enum class TypeOfConvergence{
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}
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/**
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* Class for the data obtained as a result of the execution of the Levenberg-Marquardt algorithm
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* The data obtained as a result of the execution of the Levenberg-Marquardt algorithm.
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*
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* iterations: number of completed iterations
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* funcCalls: the number of evaluations of the input function during execution
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* resultChiSq: chi squared value on final parameters
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* resultLambda: final lambda parameter used to calculate the offset
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* resultParameters: final parameters
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* typeOfConvergence: type of convergence
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* iterations: number of completed iterations.
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* funcCalls: the number of evaluations of the input function during execution.
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* resultChiSq: chi squared value on final parameters.
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* resultLambda: final lambda parameter used to calculate the offset.
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* resultParameters: final parameters.
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* typeOfConvergence: type of convergence.
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*/
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public data class LMResultInfo (
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var iterations:Int,
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@ -59,26 +59,65 @@ public data class LMResultInfo (
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var typeOfConvergence: TypeOfConvergence,
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)
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public fun DoubleTensorAlgebra.lm(
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func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, Int, MutableStructure2D<Double>>,
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pInput: MutableStructure2D<Double>, tInput: MutableStructure2D<Double>, yDatInput: MutableStructure2D<Double>,
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weightInput: MutableStructure2D<Double>, dpInput: MutableStructure2D<Double>, pMinInput: MutableStructure2D<Double>,
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pMaxInput: MutableStructure2D<Double>, optsInput: DoubleArray, nargin: Int, exampleNumber: Int): LMResultInfo {
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/**
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* Input data for the Levenberg-Marquardt function.
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*
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* func: function of n independent variables x, m parameters an example number,
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* rotating a vector of n values y, in which each of the y_i is calculated at its x_i with the given parameters.
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* startParameters: starting parameters.
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* independentVariables: independent variables, for each of which the real value is known.
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* realValues: real values obtained with given independent variables but unknown parameters.
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* weight: measurement error for realValues (denominator in each term of sum of weighted squared errors).
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* pDelta: delta when calculating the derivative with respect to parameters.
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* minParameters: the lower bound of parameter values.
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* maxParameters: upper limit of parameter values.
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* maxIterations: maximum allowable number of iterations.
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* epsilons: epsilon1 - convergence tolerance for gradient,
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* epsilon2 - convergence tolerance for parameters,
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* epsilon3 - convergence tolerance for reduced chi-square,
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* epsilon4 - determines acceptance of a step.
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* lambdas: lambda0 - starting lambda value for parameter offset count,
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* lambdaUp - factor for increasing lambda,
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* lambdaDown - factor for decreasing lambda.
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* updateType: 1: Levenberg-Marquardt lambda update,
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* 2: Quadratic update,
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* 3: Nielsen's lambda update equations.
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* nargin: a value that determines which options to use by default
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* (<5 - use weight by default, <6 - use pDelta by default, <7 - use minParameters by default,
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* <8 - use maxParameters by default, <9 - use updateType by default).
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* exampleNumber: a parameter for a function with which you can choose its behavior.
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*/
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public data class LMInput (
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var func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, Int, MutableStructure2D<Double>>,
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var startParameters: MutableStructure2D<Double>,
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var independentVariables: MutableStructure2D<Double>,
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var realValues: MutableStructure2D<Double>,
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var weight: Double,
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var pDelta: MutableStructure2D<Double>,
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var minParameters: MutableStructure2D<Double>,
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var maxParameters: MutableStructure2D<Double>,
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var maxIterations: Int,
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var epsilons: DoubleArray,
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var lambdas: DoubleArray,
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var updateType: Int,
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var nargin: Int,
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var exampleNumber: Int
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)
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public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultInfo {
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val resultInfo = LMResultInfo(0, 0, 0.0,
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0.0, pInput, TypeOfConvergence.NoConvergence)
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0.0, inputData.startParameters, TypeOfConvergence.NoConvergence)
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val eps = 2.2204e-16
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val settings = LMSettings(0, 0, exampleNumber)
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val settings = LMSettings(0, 0, inputData.exampleNumber)
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settings.funcCalls = 0 // running count of function evaluations
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var p = pInput
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val t = tInput
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var p = inputData.startParameters
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val t = inputData.independentVariables
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val Npar = length(p) // number of parameters
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val Npnt = length(yDatInput) // number of data points
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val Npnt = length(inputData.realValues) // number of data points
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var pOld = zeros(ShapeND(intArrayOf(Npar, 1))).as2D() // previous set of parameters
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var yOld = zeros(ShapeND(intArrayOf(Npnt, 1))).as2D() // previous model, y_old = y_hat(t;p_old)
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var X2 = 1e-3 / eps // a really big initial Chi-sq value
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@ -86,50 +125,55 @@ public fun DoubleTensorAlgebra.lm(
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var J = zeros(ShapeND(intArrayOf(Npnt, Npar))).as2D() // Jacobian matrix
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val DoF = Npnt - Npar // statistical degrees of freedom
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var weight = weightInput
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if (nargin < 5) {
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weight = fromArray(ShapeND(intArrayOf(1, 1)), doubleArrayOf((yDatInput.transpose().dot(yDatInput)).as1D()[0])).as2D()
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var weight = fromArray(ShapeND(intArrayOf(1, 1)), doubleArrayOf(inputData.weight)).as2D()
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if (inputData.nargin < 5) {
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weight = fromArray(ShapeND(intArrayOf(1, 1)), doubleArrayOf((inputData.realValues.transpose().dot(inputData.realValues)).as1D()[0])).as2D()
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}
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var dp = dpInput
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if (nargin < 6) {
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var dp = inputData.pDelta
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if (inputData.nargin < 6) {
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dp = fromArray(ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.001)).as2D()
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}
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var pMin = pMinInput
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if (nargin < 7) {
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pMin = p
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pMin.abs()
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pMin = pMin.div(-100.0).as2D()
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var minParameters = inputData.minParameters
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if (inputData.nargin < 7) {
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minParameters = p
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minParameters.abs()
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minParameters = minParameters.div(-100.0).as2D()
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}
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var pMax = pMaxInput
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if (nargin < 8) {
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pMax = p
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pMax.abs()
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pMax = pMax.div(100.0).as2D()
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var maxParameters = inputData.maxParameters
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if (inputData.nargin < 8) {
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maxParameters = p
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maxParameters.abs()
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maxParameters = maxParameters.div(100.0).as2D()
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}
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var opts = optsInput
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if (nargin < 10) {
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opts = doubleArrayOf(3.0, 10.0 * Npar, 1e-3, 1e-3, 1e-1, 1e-1, 1e-2, 11.0, 9.0, 1.0)
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}
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val prnt = opts[0] // >1 intermediate results; >2 plots
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val maxIterations = opts[1].toInt() // maximum number of iterations
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val epsilon1 = opts[2] // convergence tolerance for gradient
|
||||
val epsilon2 = opts[3] // convergence tolerance for parameters
|
||||
val epsilon3 = opts[4] // convergence tolerance for Chi-square
|
||||
val epsilon4 = opts[5] // determines acceptance of a L-M step
|
||||
val lambda0 = opts[6] // initial value of damping paramter, lambda
|
||||
val lambdaUpFac = opts[7] // factor for increasing lambda
|
||||
val lambdaDnFac = opts[8] // factor for decreasing lambda
|
||||
val updateType = opts[9].toInt() // 1: Levenberg-Marquardt lambda update
|
||||
var maxIterations = inputData.maxIterations
|
||||
var epsilon1 = inputData.epsilons[0] // convergence tolerance for gradient
|
||||
var epsilon2 = inputData.epsilons[1] // convergence tolerance for parameters
|
||||
var epsilon3 = inputData.epsilons[2] // convergence tolerance for Chi-square
|
||||
var epsilon4 = inputData.epsilons[3] // determines acceptance of a L-M step
|
||||
var lambda0 = inputData.lambdas[0] // initial value of damping paramter, lambda
|
||||
var lambdaUpFac = inputData.lambdas[1] // factor for increasing lambda
|
||||
var lambdaDnFac = inputData.lambdas[2] // factor for decreasing lambda
|
||||
var updateType = inputData.updateType // 1: Levenberg-Marquardt lambda update
|
||||
// 2: Quadratic update
|
||||
// 3: Nielsen's lambda update equations
|
||||
if (inputData.nargin < 9) {
|
||||
maxIterations = 10 * Npar
|
||||
epsilon1 = 1e-3
|
||||
epsilon2 = 1e-3
|
||||
epsilon3 = 1e-1
|
||||
epsilon4 = 1e-1
|
||||
lambda0 = 1e-2
|
||||
lambdaUpFac = 11.0
|
||||
lambdaDnFac = 9.0
|
||||
updateType = 1
|
||||
}
|
||||
|
||||
pMin = makeColumn(pMin)
|
||||
pMax = makeColumn(pMax)
|
||||
minParameters = makeColumn(minParameters)
|
||||
maxParameters = makeColumn(maxParameters)
|
||||
|
||||
if (length(makeColumn(dp)) == 1) {
|
||||
dp = ones(ShapeND(intArrayOf(Npar, 1))).div(1 / dp[0, 0]).as2D()
|
||||
@ -146,7 +190,7 @@ public fun DoubleTensorAlgebra.lm(
|
||||
}
|
||||
|
||||
// initialize Jacobian with finite difference calculation
|
||||
var lmMatxAns = lmMatx(func, t, pOld, yOld, 1, J, p, yDatInput, weight, dp, settings)
|
||||
var lmMatxAns = lmMatx(inputData.func, t, pOld, yOld, 1, J, p, inputData.realValues, weight, dp, settings)
|
||||
var JtWJ = lmMatxAns[0]
|
||||
var JtWdy = lmMatxAns[1]
|
||||
X2 = lmMatxAns[2][0, 0]
|
||||
@ -189,9 +233,9 @@ public fun DoubleTensorAlgebra.lm(
|
||||
}
|
||||
|
||||
var pTry = (p + h).as2D() // update the [idx] elements
|
||||
pTry = smallestElementComparison(largestElementComparison(pMin, pTry.as2D()), pMax) // apply constraints
|
||||
pTry = smallestElementComparison(largestElementComparison(minParameters, pTry.as2D()), maxParameters) // apply constraints
|
||||
|
||||
var deltaY = yDatInput.minus(evaluateFunction(func, t, pTry, exampleNumber)) // residual error using p_try
|
||||
var deltaY = inputData.realValues.minus(evaluateFunction(inputData.func, t, pTry, inputData.exampleNumber)) // residual error using p_try
|
||||
|
||||
for (i in 0 until deltaY.shape.component1()) { // floating point error; break
|
||||
for (j in 0 until deltaY.shape.component2()) {
|
||||
@ -214,9 +258,9 @@ public fun DoubleTensorAlgebra.lm(
|
||||
val alpha = JtWdy.transpose().dot(h) / ((X2Try.minus(X2)).div(2.0).plus(2 * JtWdy.transpose().dot(h)))
|
||||
h = h.dot(alpha)
|
||||
pTry = p.plus(h).as2D() // update only [idx] elements
|
||||
pTry = smallestElementComparison(largestElementComparison(pMin, pTry), pMax) // apply constraints
|
||||
pTry = smallestElementComparison(largestElementComparison(minParameters, pTry), maxParameters) // apply constraints
|
||||
|
||||
deltaY = yDatInput.minus(evaluateFunction(func, t, pTry, exampleNumber)) // residual error using p_try
|
||||
deltaY = inputData.realValues.minus(evaluateFunction(inputData.func, t, pTry, inputData.exampleNumber)) // residual error using p_try
|
||||
settings.funcCalls += 1
|
||||
|
||||
X2Try = deltaY.as2D().transpose().dot(deltaY.times(weight)) // Chi-squared error criteria
|
||||
@ -242,7 +286,7 @@ public fun DoubleTensorAlgebra.lm(
|
||||
yOld = yHat.copyToTensor().as2D()
|
||||
p = makeColumn(pTry) // accept p_try
|
||||
|
||||
lmMatxAns = lmMatx(func, t, pOld, yOld, dX2.toInt(), J, p, yDatInput, weight, dp, settings)
|
||||
lmMatxAns = lmMatx(inputData.func, t, pOld, yOld, dX2.toInt(), J, p, inputData.realValues, weight, dp, settings)
|
||||
// decrease lambda ==> Gauss-Newton method
|
||||
|
||||
JtWJ = lmMatxAns[0]
|
||||
@ -268,7 +312,7 @@ public fun DoubleTensorAlgebra.lm(
|
||||
} else { // it IS NOT better
|
||||
X2 = X2Old // do not accept p_try
|
||||
if (settings.iteration % (2 * Npar) == 0) { // rank-1 update of Jacobian
|
||||
lmMatxAns = lmMatx(func, t, pOld, yOld, -1, J, p, yDatInput, weight, dp, settings)
|
||||
lmMatxAns = lmMatx(inputData.func, t, pOld, yOld, -1, J, p, inputData.realValues, weight, dp, settings)
|
||||
JtWJ = lmMatxAns[0]
|
||||
JtWdy = lmMatxAns[1]
|
||||
yHat = lmMatxAns[3]
|
||||
@ -292,14 +336,13 @@ public fun DoubleTensorAlgebra.lm(
|
||||
}
|
||||
}
|
||||
|
||||
if (prnt > 1) {
|
||||
val chiSq = X2 / DoF
|
||||
resultInfo.iterations = settings.iteration
|
||||
resultInfo.funcCalls = settings.funcCalls
|
||||
resultInfo.resultChiSq = chiSq
|
||||
resultInfo.resultLambda = lambda
|
||||
resultInfo.resultParameters = p
|
||||
}
|
||||
|
||||
|
||||
if (abs(JtWdy).max() < epsilon1 && settings.iteration > 2) {
|
||||
resultInfo.typeOfConvergence = TypeOfConvergence.InGradient
|
||||
|
@ -105,9 +105,7 @@ class TestLmAlgorithm {
|
||||
ShapeND(intArrayOf(100, 1)), lm_matx_y_dat
|
||||
).as2D()
|
||||
|
||||
val weight = BroadcastDoubleTensorAlgebra.fromArray(
|
||||
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 4.0 }
|
||||
).as2D()
|
||||
val weight = 4.0
|
||||
|
||||
val dp = BroadcastDoubleTensorAlgebra.fromArray(
|
||||
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
|
||||
@ -123,7 +121,12 @@ class TestLmAlgorithm {
|
||||
|
||||
val opts = doubleArrayOf(3.0, 100.0, 1e-3, 1e-3, 1e-1, 1e-1, 1e-2, 11.0, 9.0, 1.0)
|
||||
|
||||
val result = lm(::funcEasyForLm, p_init, t, y_dat, weight, dp, p_min, p_max, opts, 10, example_number)
|
||||
val inputData = LMInput(::funcEasyForLm, p_init, t, y_dat, weight, dp, p_min, p_max, opts[1].toInt(),
|
||||
doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
|
||||
doubleArrayOf(opts[6], opts[7], opts[8]),
|
||||
opts[9].toInt(), 10, example_number)
|
||||
|
||||
val result = levenbergMarquardt(inputData)
|
||||
assertEquals(13, result.iterations)
|
||||
assertEquals(31, result.funcCalls)
|
||||
assertEquals(0.9131368192633, (result.resultChiSq * 1e13).roundToLong() / 1e13)
|
||||
@ -168,9 +171,7 @@ class TestLmAlgorithm {
|
||||
|
||||
var t = t_example
|
||||
val y_dat = y_hat
|
||||
val weight = BroadcastDoubleTensorAlgebra.fromArray(
|
||||
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 }
|
||||
).as2D()
|
||||
val weight = 1.0
|
||||
val dp = BroadcastDoubleTensorAlgebra.fromArray(
|
||||
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
|
||||
).as2D()
|
||||
@ -180,8 +181,7 @@ class TestLmAlgorithm {
|
||||
p_min = p_min.div(1.0 / 50.0)
|
||||
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,
|
||||
val inputData = LMInput(::funcMiddleForLm,
|
||||
p_init.as2D(),
|
||||
t,
|
||||
y_dat,
|
||||
@ -189,10 +189,14 @@ class TestLmAlgorithm {
|
||||
dp,
|
||||
p_min.as2D(),
|
||||
p_max.as2D(),
|
||||
opts,
|
||||
opts[1].toInt(),
|
||||
doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
|
||||
doubleArrayOf(opts[6], opts[7], opts[8]),
|
||||
opts[9].toInt(),
|
||||
10,
|
||||
1
|
||||
)
|
||||
1)
|
||||
|
||||
val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
|
||||
}
|
||||
|
||||
@Test
|
||||
@ -220,9 +224,7 @@ class TestLmAlgorithm {
|
||||
|
||||
var t = t_example
|
||||
val y_dat = y_hat
|
||||
val weight = BroadcastDoubleTensorAlgebra.fromArray(
|
||||
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 / Nparams * 1.0 - 0.085 }
|
||||
).as2D()
|
||||
val weight = 1.0 / Nparams * 1.0 - 0.085
|
||||
val dp = BroadcastDoubleTensorAlgebra.fromArray(
|
||||
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
|
||||
).as2D()
|
||||
@ -232,8 +234,7 @@ class TestLmAlgorithm {
|
||||
p_min = p_min.div(1.0 / 50.0)
|
||||
val opts = doubleArrayOf(3.0, 7000.0, 1e-2, 1e-3, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
|
||||
|
||||
val result = DoubleTensorAlgebra.lm(
|
||||
::funcDifficultForLm,
|
||||
val inputData = LMInput(::funcDifficultForLm,
|
||||
p_init.as2D(),
|
||||
t,
|
||||
y_dat,
|
||||
@ -241,9 +242,13 @@ class TestLmAlgorithm {
|
||||
dp,
|
||||
p_min.as2D(),
|
||||
p_max.as2D(),
|
||||
opts,
|
||||
opts[1].toInt(),
|
||||
doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
|
||||
doubleArrayOf(opts[6], opts[7], opts[8]),
|
||||
opts[9].toInt(),
|
||||
10,
|
||||
1
|
||||
)
|
||||
1)
|
||||
|
||||
val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
|
||||
}
|
||||
}
|
Loading…
Reference in New Issue
Block a user