forked from kscience/kmath
added streaming version of LM
This commit is contained in:
Margarita Lashina
parent
a18fa01100
commit
ce16946105
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/*
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* Copyright 2018-2023 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.tensors.StreamingLm
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import space.kscience.kmath.nd.MutableStructure2D
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import space.kscience.kmath.nd.ShapeND
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import space.kscience.kmath.nd.as2D
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import space.kscience.kmath.nd.component1
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.max
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.plus
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.pow
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.times
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import space.kscience.kmath.tensors.core.internal.LMSettings
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public data class StartDataLm (
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var lm_matx_y_dat: MutableStructure2D<Double>,
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var example_number: Int,
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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 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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var consts: MutableStructure2D<Double>,
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var opts: DoubleArray
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)
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fun func1ForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
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val m = t.shape.component1()
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var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
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if (settings.example_number == 1) {
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y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0]))).times(p[0, 0]) + t.times(p[2, 0]).times(
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DoubleTensorAlgebra.exp((t.times(-1.0 / p[3, 0])))
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)
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}
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else if (settings.example_number == 2) {
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val mt = t.max()
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y_hat = (t.times(1.0 / mt)).times(p[0, 0]) +
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(t.times(1.0 / mt)).pow(2).times(p[1, 0]) +
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(t.times(1.0 / mt)).pow(3).times(p[2, 0]) +
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(t.times(1.0 / mt)).pow(4).times(p[3, 0])
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}
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else if (settings.example_number == 3) {
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y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0])))
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.times(p[0, 0]) + DoubleTensorAlgebra.sin((t.times(1.0 / p[3, 0]))).times(p[2, 0])
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}
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return y_hat.as2D()
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}
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fun getStartDataForFunc1(): StartDataLm {
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val lm_matx_y_dat = doubleArrayOf(
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19.6594, 18.6096, 17.6792, 17.2747, 16.3065, 17.1458, 16.0467, 16.7023, 15.7809, 15.9807,
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14.7620, 15.1128, 16.0973, 15.1934, 15.8636, 15.4763, 15.6860, 15.1895, 15.3495, 16.6054,
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16.2247, 15.9854, 16.1421, 17.0960, 16.7769, 17.1997, 17.2767, 17.5882, 17.5378, 16.7894,
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17.7648, 18.2512, 18.1581, 16.7037, 17.8475, 17.9081, 18.3067, 17.9632, 18.2817, 19.1427,
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18.8130, 18.5658, 18.0056, 18.4607, 18.5918, 18.2544, 18.3731, 18.7511, 19.3181, 17.3066,
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17.9632, 19.0513, 18.7528, 18.2928, 18.5967, 17.8567, 17.7859, 18.4016, 18.9423, 18.4959,
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17.8000, 18.4251, 17.7829, 17.4645, 17.5221, 17.3517, 17.4637, 17.7563, 16.8471, 17.4558,
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17.7447, 17.1487, 17.3183, 16.8312, 17.7551, 17.0942, 15.6093, 16.4163, 15.3755, 16.6725,
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16.2332, 16.2316, 16.2236, 16.5361, 15.3721, 15.3347, 15.5815, 15.6319, 14.4538, 14.6044,
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14.7665, 13.3718, 15.0587, 13.8320, 14.7873, 13.6824, 14.2579, 14.2154, 13.5818, 13.8157
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)
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var example_number = 1
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val p_init = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(4, 1)), doubleArrayOf(5.0, 2.0, 0.2, 10.0)
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).as2D()
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var t = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(100, 1))).as2D()
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for (i in 0 until 100) {
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t[i, 0] = t[i, 0] * (i + 1)
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}
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val y_dat = BroadcastDoubleTensorAlgebra.fromArray(
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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 dp = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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).as2D()
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val p_min = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(4, 1)), doubleArrayOf(-50.0, -20.0, -2.0, -100.0)
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).as2D()
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val p_max = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(4, 1)), doubleArrayOf(50.0, 20.0, 2.0, 100.0)
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).as2D()
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val consts = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), doubleArrayOf(0.0)
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).as2D()
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val opts = doubleArrayOf(3.0, 100.0, 1e-3, 1e-3, 1e-1, 1e-1, 1e-2, 11.0, 9.0, 1.0)
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return StartDataLm(y_dat, example_number, p_init, t, y_dat, weight, dp, p_min, p_max, consts, opts)
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}
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@ -0,0 +1,75 @@
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/*
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* Copyright 2018-2023 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.tensors.StreamingLm
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import kotlinx.coroutines.delay
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import kotlinx.coroutines.flow.*
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import space.kscience.kmath.nd.*
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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.internal.LMSettings
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import kotlin.math.roundToInt
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import kotlin.random.Random
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import kotlin.reflect.KFunction3
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fun streamLm(lm_func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
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startData: StartDataLm, launchFrequencyInMs: Long): Flow<MutableStructure2D<Double>> = flow{
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var example_number = startData.example_number
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var p_init = startData.p_init
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var t = startData.t
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val y_dat = startData.y_dat
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val weight = startData.weight
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val dp = startData.dp
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val p_min = startData.p_min
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val p_max = startData.p_max
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val consts = startData.consts
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val opts = startData.opts
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while (true) {
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val result = DoubleTensorAlgebra.lm(
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lm_func,
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p_init,
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t,
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y_dat,
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weight,
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dp,
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p_min,
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p_max,
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consts,
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opts,
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10,
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example_number
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)
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emit(result.result_parameters)
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delay(launchFrequencyInMs)
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p_init = generateNewParameters(p_init, 0.1)
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}
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}
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fun generateNewParameters(p: MutableStructure2D<Double>, delta: Double): MutableStructure2D<Double>{
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val n = p.shape.component1()
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val p_new = zeros(ShapeND(intArrayOf(n, 1))).as2D()
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for (i in 0 until n) {
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val randomEps = Random.nextDouble(delta + delta) - delta
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p_new[i, 0] = p[i, 0] + randomEps
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}
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return p_new
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}
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suspend fun main(){
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val startData = getStartDataForFunc1()
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// Создание потока:
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val numberFlow = streamLm(::func1ForLm, startData, 1000)
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// Запуск потока
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numberFlow.collect { parameters ->
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for (i in 0 until parameters.shape.component1()) {
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val x = (parameters[i, 0] * 10000).roundToInt() / 10000.0
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print("$x ")
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if (i == parameters.shape.component1() - 1) println()
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}
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}
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}
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