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fixed error for chi_sq and added more complete output for lm

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This commit is contained in:
Margarita Lashina 2023-05-07 17:26:59 +03:00
parent b526f9a476
commit 64e563340a
3 changed files with 32 additions and 26 deletions
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11
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt
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@ -120,9 +120,18 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
*/
public fun solve(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double>
data class LMResultInfo (
var iterations:Int,
var func_calls: Int,
var example_number: Int,
var result_chi_sq: Double,
var result_lambda: Double,
var result_parameters: MutableStructure2D<Double>
)
public fun lm(
func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
p_input: MutableStructure2D<Double>, t_input: MutableStructure2D<Double>, y_dat_input: MutableStructure2D<Double>,
weight_input: MutableStructure2D<Double>, dp_input: MutableStructure2D<Double>, p_min_input: MutableStructure2D<Double>, p_max_input: MutableStructure2D<Double>,
c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): Double
c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): LMResultInfo
}

32
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
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@ -721,9 +721,9 @@ public open class DoubleTensorAlgebra :
func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
p_input: MutableStructure2D<Double>, t_input: MutableStructure2D<Double>, y_dat_input: MutableStructure2D<Double>,
weight_input: MutableStructure2D<Double>, dp_input: MutableStructure2D<Double>, p_min_input: MutableStructure2D<Double>, p_max_input: MutableStructure2D<Double>,
c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): Double {
c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): LinearOpsTensorAlgebra.LMResultInfo {
var result_chi_sq = 0.0
var resultInfo = LinearOpsTensorAlgebra.LMResultInfo(0, 0, example_number, 0.0, 0.0, p_input)
val tensor_parameter = 0
val eps:Double = 2.2204e-16
@ -742,7 +742,7 @@ public open class DoubleTensorAlgebra :
var X2 = 1e-3 / eps // a really big initial Chi-sq value
var X2_old = 1e-3 / eps // a really big initial Chi-sq value
var J = zeros(ShapeND(intArrayOf(Npnt, Npar))).as2D() // Jacobian matrix
val DoF = Npnt - Npar + 1 // statistical degrees of freedom
val DoF = Npnt - Npar // statistical degrees of freedom
var corr_p = 0
var sigma_p = 0
@ -814,8 +814,6 @@ public open class DoubleTensorAlgebra :
// 2: Quadratic update
// 3: Nielsen's lambda update equations
val plotcmd = "figure(102); plot(t(:,1),y_init,''-k'',t(:,1),y_hat,''-b'',t(:,1),y_dat,''o'',''color'',[0,0.6,0],''MarkerSize'',4); title(sprintf(''\\chi^2_\\nu = %f'',X2/DoF)); drawnow"
p_min = make_column(p_min)
p_max = make_column(p_max)
@ -829,7 +827,7 @@ public open class DoubleTensorAlgebra :
val y_init = feval(func, t, p, settings) // residual error using p_try
if (weight.shape.component1() == 1 || variance(weight) == 0.0) { // identical weights vector
weight = ones(ShapeND(intArrayOf(Npnt, 1))).div(1 / kotlin.math.abs(weight[0, 0])).as2D() // !!! need to check
weight = ones(ShapeND(intArrayOf(Npnt, 1))).div(1 / abs(weight[0, 0])).as2D() // !!! need to check
println("using uniform weights for error analysis")
}
else {
@ -864,7 +862,7 @@ public open class DoubleTensorAlgebra :
X2_old = X2 // previous value of X2
var cvg_hst = ones(ShapeND(intArrayOf(MaxIter, Npar + 3))) // initialize convergence history
var h = JtWJ.copyToTensor()
var h: DoubleTensor
var dX2 = X2
while (!stop && settings.iteration <= MaxIter) { //--- Start Main Loop
settings.iteration += 1
@ -882,10 +880,6 @@ public open class DoubleTensorAlgebra :
}
}
// big = max(abs(h./p)) > 2; % this is a big step
// --- Are parameters [p+h] much better than [p] ?
var p_try = (p + h).as2D() // update the [idx] elements
p_try = smallest_element_comparison(largest_element_comparison(p_min, p_try.as2D()), p_max) // apply constraints
@ -961,10 +955,6 @@ public open class DoubleTensorAlgebra :
lambda * max( 1.0 / 3, 1 - (2 * rho - 1).pow(3) )
}
}
// if (prnt > 2) {
// eval(plotcmd)
// }
}
else { // it IS NOT better
X2 = X2_old // do not accept p_try
@ -994,7 +984,7 @@ public open class DoubleTensorAlgebra :
if (prnt > 1) {
val chi_sq = X2 / DoF
println("Iteration $settings.iteration, func_calls $settings.func_calls | chi_sq=$chi_sq | lambda=$lambda")
println("Iteration $settings | chi_sq=$chi_sq | lambda=$lambda")
print("param: ")
for (pn in 0 until Npar) {
print(p[pn, 0].toString() + " ")
@ -1003,7 +993,11 @@ public open class DoubleTensorAlgebra :
for (pn in 0 until Npar) {
print((h.as2D()[pn, 0] / p[pn, 0]).toString() + " ")
}
result_chi_sq = chi_sq
resultInfo.iterations = settings.iteration
resultInfo.func_calls = settings.func_calls
resultInfo.result_chi_sq = chi_sq
resultInfo.result_lambda = lambda
resultInfo.result_parameters = p
}
// update convergence history ... save _reduced_ Chi-square
@ -1076,11 +1070,9 @@ public open class DoubleTensorAlgebra :
// cvg_hst = cvg_hst(1:iteration,:);
// }
return result_chi_sq
return resultInfo
}
}
public val Double.Companion.tensorAlgebra: DoubleTensorAlgebra get() = DoubleTensorAlgebra
public val DoubleField.tensorAlgebra: DoubleTensorAlgebra get() = DoubleTensorAlgebra

9
kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
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@ -11,6 +11,7 @@ import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.internal.LMSettings
import space.kscience.kmath.testutils.assertBufferEquals
import kotlin.math.roundToInt
import kotlin.math.roundToLong
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertFalse
@ -283,7 +284,11 @@ internal class TestDoubleTensorAlgebra {
val opts = doubleArrayOf(3.0, 100.0, 1e-3, 1e-3, 1e-1, 1e-1, 1e-2, 11.0, 9.0, 1.0)
val chi_sq = lm(::lm_func, p_init, t, y_dat, weight, dp, p_min, p_max, consts, opts, 10, example_number)
assertEquals(0.9131, (chi_sq * 10000).roundToInt() / 10000.0)
val result = lm(::lm_func, 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)
}
}