forked from kscience/kmath
added helper functions for levenberg-marquardt algorithm
This commit is contained in:
Margarita Lashina
parent
10f84bd630
commit
19c1af1874
@ -12,7 +12,14 @@ import space.kscience.kmath.structures.IntBuffer
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import space.kscience.kmath.structures.asBuffer
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import space.kscience.kmath.structures.indices
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import space.kscience.kmath.tensors.core.*
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.dot
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.minus
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.times
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import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.transposed
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra.Companion.plus
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import kotlin.math.abs
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import kotlin.math.max
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import kotlin.math.min
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import kotlin.math.sqrt
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@ -308,3 +315,224 @@ internal fun DoubleTensorAlgebra.svdHelper(
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matrixV.source[i] = vBuffer[i]
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}
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}
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data class LMSettings (
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var iteration:Int,
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var func_calls: Int,
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var example_number:Int
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)
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/* matrix -> column of all elemnets */
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fun make_column(tensor: MutableStructure2D<Double>) : MutableStructure2D<Double> {
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val shape = intArrayOf(tensor.shape.component1() * tensor.shape.component2(), 1)
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var buffer = DoubleArray(tensor.shape.component1() * tensor.shape.component2())
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for (i in 0 until tensor.shape.component1()) {
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for (j in 0 until tensor.shape.component2()) {
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buffer[i * tensor.shape.component2() + j] = tensor[i, j]
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}
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}
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var column = BroadcastDoubleTensorAlgebra.fromArray(ShapeND(shape), buffer).as2D()
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return column
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}
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/* column length */
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fun length(column: MutableStructure2D<Double>) : Int {
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return column.shape.component1()
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}
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fun MutableStructure2D<Double>.abs() {
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for (i in 0 until this.shape.component1()) {
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for (j in 0 until this.shape.component2()) {
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this[i, j] = abs(this[i, j])
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}
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}
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}
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fun abs(input: MutableStructure2D<Double>): MutableStructure2D<Double> {
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val tensor = BroadcastDoubleTensorAlgebra.ones(
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ShapeND(
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intArrayOf(
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input.shape.component1(),
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input.shape.component2()
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)
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)
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).as2D()
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for (i in 0 until tensor.shape.component1()) {
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for (j in 0 until tensor.shape.component2()) {
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tensor[i, j] = abs(input[i, j])
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}
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}
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return tensor
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}
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fun diag(input: MutableStructure2D<Double>): MutableStructure2D<Double> {
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val tensor = BroadcastDoubleTensorAlgebra.ones(ShapeND(intArrayOf(input.shape.component1(), 1))).as2D()
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for (i in 0 until tensor.shape.component1()) {
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tensor[i, 0] = input[i, i]
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}
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return tensor
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}
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fun make_matrx_with_diagonal(column: MutableStructure2D<Double>): MutableStructure2D<Double> {
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val size = column.shape.component1()
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val tensor = BroadcastDoubleTensorAlgebra.zeros(ShapeND(intArrayOf(size, size))).as2D()
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for (i in 0 until size) {
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tensor[i, i] = column[i, 0]
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}
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return tensor
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}
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fun lm_eye(size: Int): MutableStructure2D<Double> {
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val column = BroadcastDoubleTensorAlgebra.ones(ShapeND(intArrayOf(size, 1))).as2D()
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return make_matrx_with_diagonal(column)
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}
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fun largest_element_comparison(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double> {
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val a_sizeX = a.shape.component1()
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val a_sizeY = a.shape.component2()
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val b_sizeX = b.shape.component1()
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val b_sizeY = b.shape.component2()
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val tensor = BroadcastDoubleTensorAlgebra.zeros(ShapeND(intArrayOf(max(a_sizeX, b_sizeX), max(a_sizeY, b_sizeY)))).as2D()
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for (i in 0 until tensor.shape.component1()) {
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for (j in 0 until tensor.shape.component2()) {
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if (i < a_sizeX && i < b_sizeX && j < a_sizeY && j < b_sizeY) {
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tensor[i, j] = max(a[i, j], b[i, j])
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}
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else if (i < a_sizeX && j < a_sizeY) {
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tensor[i, j] = a[i, j]
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}
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else {
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tensor[i, j] = b[i, j]
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}
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}
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}
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return tensor
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}
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fun smallest_element_comparison(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double> {
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val a_sizeX = a.shape.component1()
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val a_sizeY = a.shape.component2()
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val b_sizeX = b.shape.component1()
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val b_sizeY = b.shape.component2()
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val tensor = BroadcastDoubleTensorAlgebra.zeros(ShapeND(intArrayOf(max(a_sizeX, b_sizeX), max(a_sizeY, b_sizeY)))).as2D()
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for (i in 0 until tensor.shape.component1()) {
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for (j in 0 until tensor.shape.component2()) {
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if (i < a_sizeX && i < b_sizeX && j < a_sizeY && j < b_sizeY) {
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tensor[i, j] = min(a[i, j], b[i, j])
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}
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else if (i < a_sizeX && j < a_sizeY) {
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tensor[i, j] = a[i, j]
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}
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else {
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tensor[i, j] = b[i, j]
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}
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}
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}
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return tensor
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}
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fun get_zero_indices(column: MutableStructure2D<Double>, epsilon: Double = 0.000001): MutableStructure2D<Double>? {
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var idx = emptyArray<Double>()
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for (i in 0 until column.shape.component1()) {
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if (abs(column[i, 0]) > epsilon) {
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idx += (i + 1.0)
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}
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}
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if (idx.size > 0) {
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return BroadcastDoubleTensorAlgebra.fromArray(ShapeND(intArrayOf(idx.size, 1)), idx.toDoubleArray()).as2D()
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}
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return null
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}
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fun feval(func: (MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings) -> MutableStructure2D<Double>,
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t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, settings: LMSettings)
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: MutableStructure2D<Double>
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{
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return func(t, p, settings)
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}
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fun lm_matx(func: (MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings) -> MutableStructure2D<Double>,
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t: MutableStructure2D<Double>, p_old: MutableStructure2D<Double>, y_old: MutableStructure2D<Double>,
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dX2: Int, J_input: MutableStructure2D<Double>, p: MutableStructure2D<Double>,
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y_dat: MutableStructure2D<Double>, weight: MutableStructure2D<Double>, dp:MutableStructure2D<Double>, settings:LMSettings) : Array<MutableStructure2D<Double>>
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{
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// default: dp = 0.001
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val Npnt = length(y_dat) // number of data points
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val Npar = length(p) // number of parameters
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val y_hat = feval(func, t, p, settings) // evaluate model using parameters 'p'
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settings.func_calls += 1
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var J = J_input
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if (settings.iteration % (2 * Npar) == 0 || dX2 > 0) {
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J = lm_FD_J(func, t, p, y_hat, dp, settings).as2D() // finite difference
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}
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else {
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J = lm_Broyden_J(p_old, y_old, J, p, y_hat).as2D() // rank-1 update
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}
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val delta_y = y_dat.minus(y_hat)
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val Chi_sq = delta_y.transposed().dot( delta_y.times(weight) ).as2D()
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val JtWJ = J.transposed().dot ( J.times( weight.dot(BroadcastDoubleTensorAlgebra.ones(ShapeND(intArrayOf(1, Npar)))) ) ).as2D()
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val JtWdy = J.transposed().dot( weight.times(delta_y) ).as2D()
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return arrayOf(JtWJ,JtWdy,Chi_sq,y_hat,J)
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}
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fun lm_Broyden_J(p_old: MutableStructure2D<Double>, y_old: MutableStructure2D<Double>, J_input: MutableStructure2D<Double>,
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p: MutableStructure2D<Double>, y: MutableStructure2D<Double>): MutableStructure2D<Double> {
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var J = J_input.copyToTensor()
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val h = p.minus(p_old)
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val increase = y.minus(y_old).minus( J.dot(h) ).dot(h.transposed()).div( (h.transposed().dot(h)).as2D()[0, 0] )
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J = J.plus(increase)
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return J.as2D()
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}
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fun lm_FD_J(func: (MutableStructure2D<Double>, MutableStructure2D<Double>, settings: LMSettings) -> MutableStructure2D<Double>,
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t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, y: MutableStructure2D<Double>,
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dp: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
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// default: dp = 0.001 * ones(1,n)
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val m = length(y) // number of data points
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val n = length(p) // number of parameters
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val ps = p.copyToTensor().as2D()
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val J = BroadcastDoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, n))).as2D() // initialize Jacobian to Zero
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val del = BroadcastDoubleTensorAlgebra.zeros(ShapeND(intArrayOf(n, 1))).as2D()
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for (j in 0 until n) {
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del[j, 0] = dp[j, 0] * (1 + abs(p[j, 0])) // parameter perturbation
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p[j, 0] = ps[j, 0] + del[j, 0] // perturb parameter p(j)
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val epsilon = 0.0000001
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if (kotlin.math.abs(del[j, 0]) > epsilon) {
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val y1 = feval(func, t, p, settings)
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settings.func_calls += 1
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if (dp[j, 0] < 0) { // backwards difference
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for (i in 0 until J.shape.component1()) {
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J[i, j] = (y1.as2D().minus(y).as2D())[i, 0] / del[j, 0]
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}
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}
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else {
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// Do tests for it
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println("Potential mistake")
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p[j, 0] = ps[j, 0] - del[j, 0] // central difference, additional func call
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for (i in 0 until J.shape.component1()) {
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J[i, j] = (y1.as2D().minus(feval(func, t, p, settings)).as2D())[i, 0] / (2 * del[j, 0])
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}
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settings.func_calls += 1
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}
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}
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p[j, 0] = ps[j, 0] // restore p(j)
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}
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return J.as2D()
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}
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