Merge pull request #3 from margarita0303/streaming_lm_algorithm
Streaming lm algorithm, tests and examples
This commit is contained in:
Margarita
GitHub
commit
3a1817586f
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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.LevenbergMarquardt.StaticLm
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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.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.internal.LMSettings
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import kotlin.math.roundToInt
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fun main() {
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val NData = 200
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var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
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for (i in 0 until NData) {
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t_example[i, 0] = t_example[i, 0] * (i + 1) - 104
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}
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val Nparams = 15
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var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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p_example[i, 0] = p_example[i, 0] + i - 25
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}
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val settings = LMSettings(0, 0, 1)
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var y_hat = funcDifficultForLm(t_example, p_example, settings)
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var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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p_init[i, 0] = (p_example[i, 0] + 0.9)
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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 / Nparams * 1.0 - 0.085 }
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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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var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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p_min = p_min.div(1.0 / -50.0)
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val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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p_min = p_min.div(1.0 / 50.0)
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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, 10000.0, 1e-2, 0.015, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
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// val opts = doubleArrayOf(3.0, 10000.0, 1e-5, 1e-5, 1e-5, 1e-5, 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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p_init.as2D(),
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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.as2D(),
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p_max.as2D(),
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consts,
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opts,
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10,
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1
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)
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println("Parameters:")
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for (i in 0 until result.result_parameters.shape.component1()) {
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val x = (result.result_parameters[i, 0] * 10000).roundToInt() / 10000.0
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print("$x ")
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}
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println()
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println("Y true and y received:")
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var y_hat_after = funcDifficultForLm(t_example, result.result_parameters, settings)
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for (i in 0 until y_hat.shape.component1()) {
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val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
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val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
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println("$x $y")
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}
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println("Сhi_sq:")
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println(result.result_chi_sq)
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println("Number of iterations:")
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println(result.iterations)
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}
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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.LevenbergMarquardt.StaticLm
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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.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.BroadcastDoubleTensorAlgebra
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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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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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DoubleTensorAlgebra.ones(ShapeND(intArrayOf(4, 1))).as2D(),
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startedData.t,
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startedData.y_dat,
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startedData.weight,
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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.consts,
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startedData.opts,
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10,
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startedData.example_number
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)
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println("Parameters:")
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for (i in 0 until result.result_parameters.shape.component1()) {
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val x = (result.result_parameters[i, 0] * 10000).roundToInt() / 10000.0
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print("$x ")
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}
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println()
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println("Y true and y received:")
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var y_hat_after = funcDifficultForLm(startedData.t, result.result_parameters, LMSettings(0, 0, startedData.example_number))
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for (i in 0 until startedData.y_dat.shape.component1()) {
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val x = (startedData.y_dat[i, 0] * 10000).roundToInt() / 10000.0
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val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
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println("$x $y")
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}
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println("Сhi_sq:")
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println(result.result_chi_sq)
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println("Number of iterations:")
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println(result.iterations)
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}
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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.LevenbergMarquardt.StaticLm
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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.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.internal.LMSettings
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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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var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
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for (i in 0 until NData) {
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t_example[i, 0] = t_example[i, 0] * (i + 1)
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}
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val Nparams = 20
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var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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p_example[i, 0] = p_example[i, 0] + i - 25
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}
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val settings = LMSettings(0, 0, 1)
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var y_hat = funcMiddleForLm(t_example, p_example, settings)
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var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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p_init[i, 0] = (p_example[i, 0] + 0.9)
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}
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// val p_init = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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// val p_init = p_example
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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 dp = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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).as2D()
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var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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p_min = p_min.div(1.0 / -50.0)
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val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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p_min = p_min.div(1.0 / 50.0)
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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, 10000.0, 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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p_init.as2D(),
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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.as2D(),
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p_max.as2D(),
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consts,
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opts,
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10,
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1
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)
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println("Parameters:")
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for (i in 0 until result.result_parameters.shape.component1()) {
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val x = (result.result_parameters[i, 0] * 10000).roundToInt() / 10000.0
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print("$x ")
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}
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println()
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println("Y true and y received:")
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var y_hat_after = funcMiddleForLm(t_example, result.result_parameters, settings)
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for (i in 0 until y_hat.shape.component1()) {
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val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
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val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
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println("$x $y")
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}
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println("Сhi_sq:")
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println(result.result_chi_sq)
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println("Number of iterations:")
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println(result.iterations)
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}
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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.LevenbergMarquardt.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.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.internal.LMSettings
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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, numberOfLaunches: Int): 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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var 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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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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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 = result.result_parameters
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y_dat = generateNewYDat(y_dat, 0.1)
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if (!isEndless) steps -= 1
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}
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}
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fun generateNewYDat(y_dat: MutableStructure2D<Double>, delta: Double): MutableStructure2D<Double>{
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val n = y_dat.shape.component1()
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val y_dat_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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y_dat_new[i, 0] = y_dat[i, 0] + randomEps
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}
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return y_dat_new
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}
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@ -0,0 +1,25 @@
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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.LevenbergMarquardt.StreamingLm
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import space.kscience.kmath.nd.*
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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 kotlin.math.roundToInt
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suspend fun main(){
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val startData = getStartDataForFuncEasy()
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// Создание потока:
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val lmFlow = streamLm(::funcEasyForLm, startData, 1000, 10)
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// Запуск потока
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lmFlow.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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/*
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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.LevenbergMarquardt
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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
|
||||||
|
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
|
||||||
|
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 (
|
||||||
|
var lm_matx_y_dat: MutableStructure2D<Double>,
|
||||||
|
var example_number: Int,
|
||||||
|
var p_init: MutableStructure2D<Double>,
|
||||||
|
var t: MutableStructure2D<Double>,
|
||||||
|
var y_dat: MutableStructure2D<Double>,
|
||||||
|
var weight: MutableStructure2D<Double>,
|
||||||
|
var dp: MutableStructure2D<Double>,
|
||||||
|
var p_min: MutableStructure2D<Double>,
|
||||||
|
var p_max: MutableStructure2D<Double>,
|
||||||
|
var consts: MutableStructure2D<Double>,
|
||||||
|
var opts: DoubleArray
|
||||||
|
)
|
||||||
|
|
||||||
|
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)))
|
||||||
|
|
||||||
|
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 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,
|
||||||
|
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 = DoubleTensorAlgebra.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)
|
||||||
|
|
||||||
|
return StartDataLm(y_dat, example_number, p_init, t, y_dat, weight, dp, p_min, p_max, consts, opts)
|
||||||
|
}
|
||||||
@ -4,7 +4,13 @@ plugins {
|
|||||||
|
|
||||||
kscience{
|
kscience{
|
||||||
jvm()
|
jvm()
|
||||||
js()
|
js {
|
||||||
|
browser {
|
||||||
|
testTask {
|
||||||
|
useMocha().timeout = "100000"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
native()
|
native()
|
||||||
|
|
||||||
dependencies {
|
dependencies {
|
||||||
|
|||||||
@ -765,12 +765,12 @@ public open class DoubleTensorAlgebra :
|
|||||||
|
|
||||||
var weight = weight_input
|
var weight = weight_input
|
||||||
if (nargin < 5) {
|
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
|
var dp = dp_input
|
||||||
if (nargin < 6) {
|
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
|
var p_min = p_min_input
|
||||||
@ -1023,6 +1023,8 @@ public open class DoubleTensorAlgebra :
|
|||||||
// println(" !! Maximum Number of Iterations Reached Without Convergence !!")
|
// println(" !! Maximum Number of Iterations Reached Without Convergence !!")
|
||||||
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.noConvergence
|
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.noConvergence
|
||||||
resultInfo.epsilon = 0.0
|
resultInfo.epsilon = 0.0
|
||||||
|
print("noConvergence, MaxIter = ")
|
||||||
|
println(MaxIter)
|
||||||
stop = true
|
stop = true
|
||||||
}
|
}
|
||||||
} // --- End of Main Loop
|
} // --- End of Main Loop
|
||||||
|
|||||||
@ -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)
|
||||||
|
}
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user