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Added Levenberg-Marquardt algorithm and svd Golub-Kahan #513

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margarita0303 merged 35 commits from dev into dev 2023-06-19 16:11:59 +03:00
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92
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticDifficultTest.kt Normal file
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@ -0,0 +1,92 @@
/*
* Copyright 2018-2023 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.LevenbergMarquardt.StaticLm
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
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.internal.LMSettings
import kotlin.math.roundToInt
fun main() {
val NData = 200
var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
for (i in 0 until NData) {
t_example[i, 0] = t_example[i, 0] * (i + 1) - 104
}
val Nparams = 15
var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
for (i in 0 until Nparams) {
p_example[i, 0] = p_example[i, 0] + i - 25
}
val settings = LMSettings(0, 0, 1)
var y_hat = funcDifficultForLm(t_example, p_example, settings)
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)
}
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 dp = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
).as2D()
var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / -50.0)
val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / 50.0)
val consts = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
).as2D()
val opts = doubleArrayOf(3.0, 10000.0, 1e-2, 0.015, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
// val opts = doubleArrayOf(3.0, 10000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-3, 11.0, 9.0, 1.0)
val result = DoubleTensorAlgebra.lm(
::funcDifficultForLm,
p_init.as2D(),
t,
y_dat,
weight,
dp,
p_min.as2D(),
p_max.as2D(),
consts,
opts,
10,
1
)
println("Parameters:")
for (i in 0 until result.result_parameters.shape.component1()) {
val x = (result.result_parameters[i, 0] * 10000).roundToInt() / 10000.0
print("$x ")
}
println()
println("Y true and y received:")
var y_hat_after = funcDifficultForLm(t_example, result.result_parameters, settings)
for (i in 0 until y_hat.shape.component1()) {
val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
println("$x $y")
}
println("Сhi_sq:")
println(result.result_chi_sq)
println("Number of iterations:")
println(result.iterations)
}

56
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticEasyTest.kt Normal file
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@ -0,0 +1,56 @@
/*
* Copyright 2018-2023 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.LevenbergMarquardt.StaticLm
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
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.BroadcastDoubleTensorAlgebra
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
import space.kscience.kmath.tensors.core.internal.LMSettings
import kotlin.math.roundToInt
fun main() {
val startedData = getStartDataForFuncEasy()
val result = DoubleTensorAlgebra.lm(
::funcEasyForLm,
DoubleTensorAlgebra.ones(ShapeND(intArrayOf(4, 1))).as2D(),
startedData.t,
startedData.y_dat,
startedData.weight,
startedData.dp,
startedData.p_min,
startedData.p_max,
startedData.consts,
startedData.opts,
10,
startedData.example_number
)
println("Parameters:")
for (i in 0 until result.result_parameters.shape.component1()) {
val x = (result.result_parameters[i, 0] * 10000).roundToInt() / 10000.0
print("$x ")
}
println()
println("Y true and y received:")
var y_hat_after = funcDifficultForLm(startedData.t, result.result_parameters, LMSettings(0, 0, startedData.example_number))
for (i in 0 until startedData.y_dat.shape.component1()) {
val x = (startedData.y_dat[i, 0] * 10000).roundToInt() / 10000.0
val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
println("$x $y")
}
println("Сhi_sq:")
println(result.result_chi_sq)
println("Number of iterations:")
println(result.iterations)
}

91
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticMiddleTest.kt Normal file
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@ -0,0 +1,91 @@
/*
* Copyright 2018-2023 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.LevenbergMarquardt.StaticLm
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
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.internal.LMSettings
import kotlin.math.roundToInt
fun main() {
val NData = 100
var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
for (i in 0 until NData) {
t_example[i, 0] = t_example[i, 0] * (i + 1)
}
val Nparams = 20
var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
for (i in 0 until Nparams) {
p_example[i, 0] = p_example[i, 0] + i - 25
}
val settings = LMSettings(0, 0, 1)
var y_hat = funcMiddleForLm(t_example, p_example, settings)
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(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 }
).as2D()
val dp = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
).as2D()
var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / -50.0)
val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / 50.0)
val consts = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
).as2D()
val opts = doubleArrayOf(3.0, 10000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
val result = DoubleTensorAlgebra.lm(
::funcMiddleForLm,
p_init.as2D(),
t,
y_dat,
weight,
dp,
p_min.as2D(),
p_max.as2D(),
consts,
opts,
10,
1
)
println("Parameters:")
for (i in 0 until result.result_parameters.shape.component1()) {
val x = (result.result_parameters[i, 0] * 10000).roundToInt() / 10000.0
print("$x ")
}
println()
println("Y true and y received:")
var y_hat_after = funcMiddleForLm(t_example, result.result_parameters, settings)
for (i in 0 until y_hat.shape.component1()) {
val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
println("$x $y")
}
println("Сhi_sq:")
println(result.result_chi_sq)
println("Number of iterations:")
println(result.iterations)
}

26
examples/src/main/kotlin/space/kscience/kmath/tensors/StreamingLm/streamingLmMain.kt → examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamLm.kt
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@ -3,20 +3,20 @@
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.StreamingLm
package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
import kotlinx.coroutines.delay
import kotlinx.coroutines.flow.*
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.internal.LMSettings
import kotlin.math.roundToInt
import kotlin.random.Random
import kotlin.reflect.KFunction3
fun streamLm(lm_func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
startData: StartDataLm, launchFrequencyInMs: Long): Flow<MutableStructure2D<Double>> = flow{
startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int): Flow<MutableStructure2D<Double>> = flow{
var example_number = startData.example_number
var p_init = startData.p_init
@ -29,7 +29,10 @@ fun streamLm(lm_func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<
val consts = startData.consts
val opts = startData.opts
while (true) {
var steps = numberOfLaunches
val isEndless = (steps <= 0)
while (isEndless || steps > 0) {
val result = DoubleTensorAlgebra.lm(
lm_func,
p_init,
@ -48,6 +51,7 @@ fun streamLm(lm_func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<
delay(launchFrequencyInMs)
p_init = result.result_parameters
y_dat = generateNewYDat(y_dat, 0.1)
if (!isEndless) steps -= 1
}
}
@ -59,18 +63,4 @@ fun generateNewYDat(y_dat: MutableStructure2D<Double>, delta: Double): MutableSt
y_dat_new[i, 0] = y_dat[i, 0] + randomEps
}
return y_dat_new
}
suspend fun main(){
val startData = getStartDataForFunc1()
// Создание потока:
val lmFlow = streamLm(::func1ForLm, startData, 1000)
// Запуск потока
lmFlow.collect { parameters ->
for (i in 0 until parameters.shape.component1()) {
val x = (parameters[i, 0] * 10000).roundToInt() / 10000.0
print("$x ")
if (i == parameters.shape.component1() - 1) println()
}
}
}

25
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamingLmTest.kt Normal file
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@ -0,0 +1,25 @@
/*
* Copyright 2018-2023 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
import space.kscience.kmath.nd.*
import space.kscience.kmath.tensors.LevenbergMarquardt.funcEasyForLm
import space.kscience.kmath.tensors.LevenbergMarquardt.getStartDataForFuncEasy
import kotlin.math.roundToInt
suspend fun main(){
val startData = getStartDataForFuncEasy()
// Создание потока:
val lmFlow = streamLm(::funcEasyForLm, startData, 1000, 10)
// Запуск потока
lmFlow.collect { parameters ->
for (i in 0 until parameters.shape.component1()) {
val x = (parameters[i, 0] * 10000).roundToInt() / 10000.0
print("$x ")
if (i == parameters.shape.component1() - 1) println()
}
}
}

39
examples/src/main/kotlin/space/kscience/kmath/tensors/StreamingLm/functionsToOptimize.kt → examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/functionsToOptimize.kt
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@ -3,7 +3,7 @@
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.StreamingLm
package space.kscience.kmath.tensors.LevenbergMarquardt
import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.ShapeND
@ -15,6 +15,7 @@ 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.asDoubleTensor
import space.kscience.kmath.tensors.core.internal.LMSettings
public data class StartDataLm (
@ -31,7 +32,39 @@ public data class StartDataLm (
var opts: DoubleArray
)
fun func1ForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
fun funcDifficultForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
val mt = t.max()
for(i in 0 until p.shape.component1()){
y_hat = y_hat.plus( (t.times(1.0 / mt)).times(p[i, 0]) )
}
for(i in 0 until 4){
y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, settings).asDoubleTensor()
}
return y_hat.as2D()
}
fun funcMiddleForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
val mt = t.max()
for(i in 0 until p.shape.component1()){
y_hat += (t.times(1.0 / mt)).times(p[i, 0])
}
for(i in 0 until 5){
y_hat = funcEasyForLm(y_hat.as2D(), p, settings).asDoubleTensor()
}
return y_hat.as2D()
}
fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
@ -55,7 +88,7 @@ fun func1ForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, set
return y_hat.as2D()
}
fun getStartDataForFunc1(): StartDataLm {
fun getStartDataForFuncEasy(): StartDataLm {
val lm_matx_y_dat = doubleArrayOf(
19.6594, 18.6096, 17.6792, 17.2747, 16.3065, 17.1458, 16.0467, 16.7023, 15.7809, 15.9807,
14.7620, 15.1128, 16.0973, 15.1934, 15.8636, 15.4763, 15.6860, 15.1895, 15.3495, 16.6054,

6
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
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@ -765,12 +765,12 @@ public open class DoubleTensorAlgebra :
var weight = weight_input
if (nargin < 5) {
weight = fromArray(ShapeND(intArrayOf(1, 1)), doubleArrayOf((y_dat.transpose().dot(y_dat)).as1D()[0])).as2D()
fromArray(ShapeND(intArrayOf(1, 1)), doubleArrayOf(1.0)).as2D()
}
var dp = dp_input
if (nargin < 6) {
dp = fromArray(ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.001)).as2D()
dp = fromArray(ShapeND(intArrayOf(1, 1)), doubleArrayOf(-0.001)).as2D()
}
var p_min = p_min_input
@ -1023,6 +1023,8 @@ public open class DoubleTensorAlgebra :
// println(" !! Maximum Number of Iterations Reached Without Convergence !!")
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.noConvergence
resultInfo.epsilon = 0.0
print("noConvergence, MaxIter = ")
println(MaxIter)
stop = true
}
} // --- End of Main Loop

267
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestLmAlgorithm.kt Normal file
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@ -0,0 +1,267 @@
/*
* Copyright 2018-2023 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors.core
import space.kscience.kmath.nd.MutableStructure2D
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
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.internal.LMSettings
import kotlin.math.roundToLong
import kotlin.test.Test
import kotlin.test.assertEquals
class TestLmAlgorithm {
companion object {
fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): 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]) +
(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) {
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> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
val mt = t.max()
for(i in 0 until p.shape.component1()){
y_hat += (t.times(1.0 / mt)).times(p[i, 0])
}
for(i in 0 until 5){
y_hat = funcEasyForLm(y_hat.as2D(), p, settings).asDoubleTensor()
}
return y_hat.as2D()
}
fun funcDifficultForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
val m = t.shape.component1()
var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
val mt = t.max()
for(i in 0 until p.shape.component1()){
y_hat = y_hat.plus( (t.times(1.0 / mt)).times(p[i, 0]) )
}
for(i in 0 until 4){
y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, settings).asDoubleTensor()
}
return y_hat.as2D()
}
}
@Test
fun testLMEasy() = DoubleTensorAlgebra {
val lm_matx_y_dat = doubleArrayOf(
19.6594, 18.6096, 17.6792, 17.2747, 16.3065, 17.1458, 16.0467, 16.7023, 15.7809, 15.9807,
14.7620, 15.1128, 16.0973, 15.1934, 15.8636, 15.4763, 15.6860, 15.1895, 15.3495, 16.6054,
16.2247, 15.9854, 16.1421, 17.0960, 16.7769, 17.1997, 17.2767, 17.5882, 17.5378, 16.7894,
17.7648, 18.2512, 18.1581, 16.7037, 17.8475, 17.9081, 18.3067, 17.9632, 18.2817, 19.1427,
18.8130, 18.5658, 18.0056, 18.4607, 18.5918, 18.2544, 18.3731, 18.7511, 19.3181, 17.3066,
17.9632, 19.0513, 18.7528, 18.2928, 18.5967, 17.8567, 17.7859, 18.4016, 18.9423, 18.4959,
17.8000, 18.4251, 17.7829, 17.4645, 17.5221, 17.3517, 17.4637, 17.7563, 16.8471, 17.4558,
17.7447, 17.1487, 17.3183, 16.8312, 17.7551, 17.0942, 15.6093, 16.4163, 15.3755, 16.6725,
16.2332, 16.2316, 16.2236, 16.5361, 15.3721, 15.3347, 15.5815, 15.6319, 14.4538, 14.6044,
14.7665, 13.3718, 15.0587, 13.8320, 14.7873, 13.6824, 14.2579, 14.2154, 13.5818, 13.8157
)
var example_number = 1
val p_init = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(4, 1)), doubleArrayOf(5.0, 2.0, 0.2, 10.0)
).as2D()
var t = ones(ShapeND(intArrayOf(100, 1))).as2D()
for (i in 0 until 100) {
t[i, 0] = t[i, 0] * (i + 1)
}
val y_dat = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(100, 1)), lm_matx_y_dat
).as2D()
val weight = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 4.0 }
).as2D()
val dp = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
).as2D()
val p_min = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(4, 1)), doubleArrayOf(-50.0, -20.0, -2.0, -100.0)
).as2D()
val p_max = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(4, 1)), doubleArrayOf(50.0, 20.0, 2.0, 100.0)
).as2D()
val consts = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
).as2D()
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, consts, opts, 10, example_number)
assertEquals(13, result.iterations)
assertEquals(31, result.func_calls)
assertEquals(1, result.example_number)
assertEquals(0.9131368192633, (result.result_chi_sq * 1e13).roundToLong() / 1e13)
assertEquals(3.7790980 * 1e-7, (result.result_lambda * 1e13).roundToLong() / 1e13)
assertEquals(result.typeOfConvergence, LinearOpsTensorAlgebra.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()
val receivedParameters = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(4, 1)), doubleArrayOf(result.result_parameters[0, 0], result.result_parameters[1, 0],
result.result_parameters[2, 0], result.result_parameters[3, 0])
).as2D()
assertEquals(expectedParameters[0, 0], receivedParameters[0, 0])
assertEquals(expectedParameters[1, 0], receivedParameters[1, 0])
assertEquals(expectedParameters[2, 0], receivedParameters[2, 0])
assertEquals(expectedParameters[3, 0], receivedParameters[3, 0])
}
@Test
fun TestLMMiddle() = DoubleTensorAlgebra {
val NData = 100
var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
for (i in 0 until NData) {
t_example[i, 0] = t_example[i, 0] * (i + 1)
}
val Nparams = 20
var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
for (i in 0 until Nparams) {
p_example[i, 0] = p_example[i, 0] + i - 25
}
val settings = LMSettings(0, 0, 1)
var y_hat = funcMiddleForLm(t_example, p_example, settings)
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)
}
var t = t_example
val y_dat = y_hat
val weight = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { 1.0 }
).as2D()
val dp = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
).as2D()
var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / -50.0)
val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / 50.0)
val consts = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
).as2D()
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,
p_init.as2D(),
t,
y_dat,
weight,
dp,
p_min.as2D(),
p_max.as2D(),
consts,
opts,
10,
1
)
}
@Test
fun TestLMDifficult() = DoubleTensorAlgebra {
val NData = 200
var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
for (i in 0 until NData) {
t_example[i, 0] = t_example[i, 0] * (i + 1) - 104
}
val Nparams = 15
var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
for (i in 0 until Nparams) {
p_example[i, 0] = p_example[i, 0] + i - 25
}
val settings = LMSettings(0, 0, 1)
var y_hat = funcDifficultForLm(t_example, p_example, settings)
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)
}
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 dp = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
).as2D()
var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / -50.0)
val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
p_min = p_min.div(1.0 / 50.0)
val consts = BroadcastDoubleTensorAlgebra.fromArray(
ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
).as2D()
val opts = doubleArrayOf(3.0, 7000.0, 1e-2, 1e-1, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
val result = DoubleTensorAlgebra.lm(
::funcDifficultForLm,
p_init.as2D(),
t,
y_dat,
weight,
dp,
p_min.as2D(),
p_max.as2D(),
consts,
opts,
10,
1
)
// assertEquals(1.15, (result.result_chi_sq * 1e2).roundToLong() / 1e2)
}
}