kscience/kmath
8
0
Fork 1
Code Issues Pull Requests 6 Packages Projects Releases 16 Wiki Activity

Merge pull request #4 from margarita0303/streaming_lm_algorithm

Browse Source 
tests changed
This commit is contained in:
Margarita 2023-06-06 00:40:14 +03:00 committed by GitHub
commit 2ead722620
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 105 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticDifficultTest.kt
View File

@ -52,8 +52,8 @@ fun main() {
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 opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-2, 11.0, 9.0, 1.0)
// val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-3, 11.0, 9.0, 1.0)
val result = DoubleTensorAlgebra.lm(
::funcDifficultForLm,

5
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticMiddleTest.kt
View File

@ -12,6 +12,7 @@ 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.internal.LMSettings
import kotlin.math.roundToInt
fun main() {
@ -52,7 +53,7 @@ fun main() {
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 opts = doubleArrayOf(3.0, 10000.0, 1e-3, 1e-3, 1e-3, 1e-3, 1e-15, 11.0, 9.0, 1.0)
val result = DoubleTensorAlgebra.lm(
::funcMiddleForLm,
@ -76,7 +77,7 @@ fun main() {
}
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

15
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamingLmTest.kt
View File

@ -6,20 +6,27 @@
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 space.kscience.kmath.tensors.LevenbergMarquardt.*
import kotlin.math.roundToInt
suspend fun main(){
val startData = getStartDataForFuncEasy()
val startData = getStartDataForFuncDifficult()
// Создание потока:
val lmFlow = streamLm(::funcEasyForLm, startData, 1000, 10)
val lmFlow = streamLm(::funcDifficultForLm, startData, 0, 100)
var initialTime = System.currentTimeMillis()
var lastTime: Long
val launches = mutableListOf<Long>()
// Запуск потока
lmFlow.collect { parameters ->
lastTime = System.currentTimeMillis()
launches.add(lastTime - initialTime)
initialTime = lastTime
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()
}
}
println("Average without first is: ${launches.subList(1, launches.size - 1).average()}")
}

87
examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/functionsToOptimize.kt
View File

@ -10,6 +10,7 @@ import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.component1
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.max
import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.plus
@ -17,6 +18,7 @@ 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
import kotlin.math.roundToInt
public data class StartDataLm (
var lm_matx_y_dat: MutableStructure2D<Double>,
@ -88,6 +90,91 @@ fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>,
return y_hat.as2D()
}
fun getStartDataForFuncDifficult(): StartDataLm {
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, 1e-3, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
return StartDataLm(y_dat, 1, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
}
fun getStartDataForFuncMiddle(): StartDataLm {
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] + 10.0)
}
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)
var example_number = 1
return StartDataLm(y_dat, example_number, p_init, t, y_dat, weight, dp, p_min.as2D(), p_max.as2D(), consts, opts)
}
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,

2
kmath-tensors/build.gradle.kts
View File

@ -7,7 +7,7 @@ kscience{
js {
browser {
testTask {
useMocha().timeout = "100000"
useMocha().timeout = "0"
}
}
}

4
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestLmAlgorithm.kt
View File

@ -245,7 +245,7 @@ class TestLmAlgorithm {
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 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,
@ -261,7 +261,5 @@ class TestLmAlgorithm {
10,
1
)
// assertEquals(1.15, (result.result_chi_sq * 1e2).roundToLong() / 1e2)
}
}