fixed error for chi_sq and added more complete output for lm
This commit is contained in:
Margarita Lashina
parent
b526f9a476
commit
64e563340a
@ -120,9 +120,18 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
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*/
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public fun solve(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double>
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data class LMResultInfo (
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var iterations:Int,
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var func_calls: Int,
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var example_number: Int,
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var result_chi_sq: Double,
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var result_lambda: Double,
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var result_parameters: MutableStructure2D<Double>
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)
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public fun lm(
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func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
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p_input: MutableStructure2D<Double>, t_input: MutableStructure2D<Double>, y_dat_input: MutableStructure2D<Double>,
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weight_input: MutableStructure2D<Double>, dp_input: MutableStructure2D<Double>, p_min_input: MutableStructure2D<Double>, p_max_input: MutableStructure2D<Double>,
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c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): Double
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c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): LMResultInfo
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}
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@ -721,9 +721,9 @@ public open class DoubleTensorAlgebra :
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func: KFunction3<MutableStructure2D<Double>, MutableStructure2D<Double>, LMSettings, MutableStructure2D<Double>>,
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p_input: MutableStructure2D<Double>, t_input: MutableStructure2D<Double>, y_dat_input: MutableStructure2D<Double>,
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weight_input: MutableStructure2D<Double>, dp_input: MutableStructure2D<Double>, p_min_input: MutableStructure2D<Double>, p_max_input: MutableStructure2D<Double>,
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c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): Double {
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c_input: MutableStructure2D<Double>, opts_input: DoubleArray, nargin: Int, example_number: Int): LinearOpsTensorAlgebra.LMResultInfo {
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var result_chi_sq = 0.0
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var resultInfo = LinearOpsTensorAlgebra.LMResultInfo(0, 0, example_number, 0.0, 0.0, p_input)
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val tensor_parameter = 0
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val eps:Double = 2.2204e-16
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@ -742,7 +742,7 @@ public open class DoubleTensorAlgebra :
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var X2 = 1e-3 / eps // a really big initial Chi-sq value
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var X2_old = 1e-3 / eps // a really big initial Chi-sq value
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var J = zeros(ShapeND(intArrayOf(Npnt, Npar))).as2D() // Jacobian matrix
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val DoF = Npnt - Npar + 1 // statistical degrees of freedom
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val DoF = Npnt - Npar // statistical degrees of freedom
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var corr_p = 0
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var sigma_p = 0
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@ -811,10 +811,8 @@ public open class DoubleTensorAlgebra :
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val lambda_UP_fac = opts[7] // factor for increasing lambda
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val lambda_DN_fac = opts[8] // factor for decreasing lambda
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val Update_Type = opts[9].toInt() // 1: Levenberg-Marquardt lambda update
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// 2: Quadratic update
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// 3: Nielsen's lambda update equations
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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"
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// 2: Quadratic update
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// 3: Nielsen's lambda update equations
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p_min = make_column(p_min)
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p_max = make_column(p_max)
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@ -829,7 +827,7 @@ public open class DoubleTensorAlgebra :
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val y_init = feval(func, t, p, settings) // residual error using p_try
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if (weight.shape.component1() == 1 || variance(weight) == 0.0) { // identical weights vector
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weight = ones(ShapeND(intArrayOf(Npnt, 1))).div(1 / kotlin.math.abs(weight[0, 0])).as2D() // !!! need to check
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weight = ones(ShapeND(intArrayOf(Npnt, 1))).div(1 / abs(weight[0, 0])).as2D() // !!! need to check
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println("using uniform weights for error analysis")
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}
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else {
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@ -864,7 +862,7 @@ public open class DoubleTensorAlgebra :
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X2_old = X2 // previous value of X2
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var cvg_hst = ones(ShapeND(intArrayOf(MaxIter, Npar + 3))) // initialize convergence history
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var h = JtWJ.copyToTensor()
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var h: DoubleTensor
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var dX2 = X2
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while (!stop && settings.iteration <= MaxIter) { //--- Start Main Loop
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settings.iteration += 1
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@ -882,10 +880,6 @@ public open class DoubleTensorAlgebra :
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}
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}
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// big = max(abs(h./p)) > 2; % this is a big step
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// --- Are parameters [p+h] much better than [p] ?
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var p_try = (p + h).as2D() // update the [idx] elements
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p_try = smallest_element_comparison(largest_element_comparison(p_min, p_try.as2D()), p_max) // apply constraints
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@ -961,10 +955,6 @@ public open class DoubleTensorAlgebra :
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lambda * max( 1.0 / 3, 1 - (2 * rho - 1).pow(3) )
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}
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}
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// if (prnt > 2) {
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// eval(plotcmd)
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// }
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}
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else { // it IS NOT better
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X2 = X2_old // do not accept p_try
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@ -994,7 +984,7 @@ public open class DoubleTensorAlgebra :
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if (prnt > 1) {
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val chi_sq = X2 / DoF
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println("Iteration $settings.iteration, func_calls $settings.func_calls | chi_sq=$chi_sq | lambda=$lambda")
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println("Iteration $settings | chi_sq=$chi_sq | lambda=$lambda")
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print("param: ")
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for (pn in 0 until Npar) {
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print(p[pn, 0].toString() + " ")
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@ -1003,7 +993,11 @@ public open class DoubleTensorAlgebra :
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for (pn in 0 until Npar) {
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print((h.as2D()[pn, 0] / p[pn, 0]).toString() + " ")
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}
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result_chi_sq = chi_sq
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resultInfo.iterations = settings.iteration
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resultInfo.func_calls = settings.func_calls
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resultInfo.result_chi_sq = chi_sq
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resultInfo.result_lambda = lambda
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resultInfo.result_parameters = p
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}
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// update convergence history ... save _reduced_ Chi-square
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@ -1076,11 +1070,9 @@ public open class DoubleTensorAlgebra :
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// cvg_hst = cvg_hst(1:iteration,:);
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// }
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return result_chi_sq
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return resultInfo
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}
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}
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public val Double.Companion.tensorAlgebra: DoubleTensorAlgebra get() = DoubleTensorAlgebra
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public val DoubleField.tensorAlgebra: DoubleTensorAlgebra get() = DoubleTensorAlgebra
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public val DoubleField.tensorAlgebra: DoubleTensorAlgebra get() = DoubleTensorAlgebra
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@ -11,6 +11,7 @@ import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.tensors.core.internal.LMSettings
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import space.kscience.kmath.testutils.assertBufferEquals
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import kotlin.math.roundToInt
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import kotlin.math.roundToLong
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import kotlin.test.Test
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import kotlin.test.assertEquals
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import kotlin.test.assertFalse
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@ -283,7 +284,11 @@ internal class TestDoubleTensorAlgebra {
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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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val chi_sq = lm(::lm_func, p_init, t, y_dat, weight, dp, p_min, p_max, consts, opts, 10, example_number)
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assertEquals(0.9131, (chi_sq * 10000).roundToInt() / 10000.0)
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val result = lm(::lm_func, p_init, t, y_dat, weight, dp, p_min, p_max, consts, opts, 10, example_number)
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assertEquals(13, result.iterations)
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assertEquals(31, result.func_calls)
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assertEquals(1, result.example_number)
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assertEquals(0.9131368192633, (result.result_chi_sq * 1e13).roundToLong() / 1e13)
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assertEquals(3.7790980 * 1e-7, (result.result_lambda * 1e13).roundToLong() / 1e13)
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}
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}
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