done TODOs, deleted prints and added type of convergence to output of lm
This commit is contained in:
Margarita Lashina
parent
64e563340a
commit
cfe8e9bfee
@ -120,13 +120,22 @@ public interface LinearOpsTensorAlgebra<T, A : Field<T>> : TensorPartialDivision
|
||||
*/
|
||||
public fun solve(a: MutableStructure2D<Double>, b: MutableStructure2D<Double>): MutableStructure2D<Double>
|
||||
|
||||
data class LMResultInfo (
|
||||
public enum class TypeOfConvergence{
|
||||
inRHS_JtWdy,
|
||||
inParameters,
|
||||
inReducedChi_square,
|
||||
noConvergence
|
||||
}
|
||||
|
||||
public 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>
|
||||
var result_parameters: MutableStructure2D<Double>,
|
||||
var typeOfConvergence: TypeOfConvergence,
|
||||
var epsilon: Double
|
||||
)
|
||||
|
||||
public fun lm(
|
||||
|
||||
@ -723,9 +723,9 @@ public open class DoubleTensorAlgebra :
|
||||
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): LinearOpsTensorAlgebra.LMResultInfo {
|
||||
|
||||
var resultInfo = LinearOpsTensorAlgebra.LMResultInfo(0, 0, example_number, 0.0, 0.0, p_input)
|
||||
var resultInfo = LinearOpsTensorAlgebra.LMResultInfo(0, 0, example_number, 0.0,
|
||||
0.0, p_input, LinearOpsTensorAlgebra.TypeOfConvergence.noConvergence, 0.0)
|
||||
|
||||
val tensor_parameter = 0
|
||||
val eps:Double = 2.2204e-16
|
||||
|
||||
var settings = LMSettings(0, 0, example_number)
|
||||
@ -751,7 +751,7 @@ public open class DoubleTensorAlgebra :
|
||||
var cvg_hist = 0
|
||||
|
||||
if (length(t) != length(y_dat)) {
|
||||
println("lm.m error: the length of t must equal the length of y_dat")
|
||||
// println("lm.m error: the length of t must equal the length of y_dat")
|
||||
val length_t = length(t)
|
||||
val length_y_dat = length(y_dat)
|
||||
X2 = 0.0
|
||||
@ -761,10 +761,6 @@ public open class DoubleTensorAlgebra :
|
||||
sigma_y = 0
|
||||
R_sq = 0
|
||||
cvg_hist = 0
|
||||
|
||||
// if (tensor_parameter != 0) { // Зачем эта проверка?
|
||||
// return
|
||||
// }
|
||||
}
|
||||
|
||||
var weight = weight_input
|
||||
@ -827,8 +823,8 @@ 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 / abs(weight[0, 0])).as2D() // !!! need to check
|
||||
println("using uniform weights for error analysis")
|
||||
weight = ones(ShapeND(intArrayOf(Npnt, 1))).div(1 / abs(weight[0, 0])).as2D()
|
||||
// println("using uniform weights for error analysis")
|
||||
}
|
||||
else {
|
||||
weight = make_column(weight)
|
||||
@ -844,8 +840,8 @@ public open class DoubleTensorAlgebra :
|
||||
J = lm_matx_ans[4]
|
||||
|
||||
if ( abs(JtWdy).max()!! < epsilon_1 ) {
|
||||
println(" *** Your Initial Guess is Extremely Close to Optimal ***\n")
|
||||
println(" *** epsilon_1 = %e\n$epsilon_1")
|
||||
// println(" *** Your Initial Guess is Extremely Close to Optimal ***\n")
|
||||
// println(" *** epsilon_1 = %e\n$epsilon_1")
|
||||
stop = true
|
||||
}
|
||||
|
||||
@ -885,11 +881,14 @@ public open class DoubleTensorAlgebra :
|
||||
|
||||
var delta_y = y_dat.minus(feval(func, t, p_try, settings)) // residual error using p_try
|
||||
|
||||
// TODO
|
||||
//if ~all(isfinite(delta_y)) // floating point error; break
|
||||
// stop = 1;
|
||||
// break
|
||||
//end
|
||||
for (i in 0 until delta_y.shape.component1()) { // floating point error; break
|
||||
for (j in 0 until delta_y.shape.component2()) {
|
||||
if (delta_y[i, j] == Double.POSITIVE_INFINITY || delta_y[i, j] == Double.NEGATIVE_INFINITY) {
|
||||
stop = true
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
settings.func_calls += 1
|
||||
|
||||
@ -900,17 +899,16 @@ public open class DoubleTensorAlgebra :
|
||||
if (Update_Type == 2) { // Quadratic
|
||||
// One step of quadratic line update in the h direction for minimum X2
|
||||
|
||||
// TODO
|
||||
// val alpha = JtWdy.transpose().dot(h) / ((X2_try.minus(X2)).div(2.0).plus(2 * JtWdy.transpose().dot(h)))
|
||||
// alpha = JtWdy'*h / ( (X2_try - X2)/2 + 2*JtWdy'*h ) ;
|
||||
// h = alpha * h;
|
||||
//
|
||||
// p_try = p + h(idx); % update only [idx] elements
|
||||
// p_try = min(max(p_min,p_try),p_max); % apply constraints
|
||||
//
|
||||
// delta_y = y_dat - feval(func,t,p_try,c); % residual error using p_try
|
||||
// func_calls = func_calls + 1;
|
||||
// тX2_try = delta_y' * ( delta_y .* weight ); % Chi-squared error criteria
|
||||
val alpha = JtWdy.transpose().dot(h) / ( (X2_try.minus(X2)).div(2.0).plus(2 * JtWdy.transpose().dot(h)) )
|
||||
h = h.dot(alpha)
|
||||
p_try = p.plus(h).as2D() // update only [idx] elements
|
||||
p_try = smallest_element_comparison(largest_element_comparison(p_min, p_try), p_max) // apply constraints
|
||||
|
||||
var delta_y = y_dat.minus(feval(func, t, p_try, settings)) // residual error using p_try
|
||||
settings.func_calls += 1
|
||||
|
||||
val tmp = delta_y.times(weight)
|
||||
X2_try = delta_y.as2D().transpose().dot(tmp) // Chi-squared error criteria
|
||||
}
|
||||
|
||||
val rho = when (Update_Type) { // Nielsen
|
||||
@ -924,9 +922,6 @@ public open class DoubleTensorAlgebra :
|
||||
}
|
||||
}
|
||||
|
||||
println()
|
||||
println("rho = " + rho)
|
||||
|
||||
if (rho > epsilon_4) { // it IS significantly better
|
||||
val dX2 = X2.minus(X2_old)
|
||||
X2_old = X2
|
||||
@ -984,15 +979,15 @@ public open class DoubleTensorAlgebra :
|
||||
|
||||
if (prnt > 1) {
|
||||
val chi_sq = X2 / DoF
|
||||
println("Iteration $settings | chi_sq=$chi_sq | lambda=$lambda")
|
||||
print("param: ")
|
||||
for (pn in 0 until Npar) {
|
||||
print(p[pn, 0].toString() + " ")
|
||||
}
|
||||
print("\ndp/p: ")
|
||||
for (pn in 0 until Npar) {
|
||||
print((h.as2D()[pn, 0] / p[pn, 0]).toString() + " ")
|
||||
}
|
||||
// println("Iteration $settings | chi_sq=$chi_sq | lambda=$lambda")
|
||||
// print("param: ")
|
||||
// for (pn in 0 until Npar) {
|
||||
// print(p[pn, 0].toString() + " ")
|
||||
// }
|
||||
// print("\ndp/p: ")
|
||||
// for (pn in 0 until Npar) {
|
||||
// print((h.as2D()[pn, 0] / p[pn, 0]).toString() + " ")
|
||||
// }
|
||||
resultInfo.iterations = settings.iteration
|
||||
resultInfo.func_calls = settings.func_calls
|
||||
resultInfo.result_chi_sq = chi_sq
|
||||
@ -1004,22 +999,30 @@ public open class DoubleTensorAlgebra :
|
||||
// cvg_hst(iteration,:) = [ func_calls p' X2/DoF lambda ];
|
||||
|
||||
if (abs(JtWdy).max()!! < epsilon_1 && settings.iteration > 2) {
|
||||
println(" **** Convergence in r.h.s. (\"JtWdy\") ****")
|
||||
println(" **** epsilon_1 = $epsilon_1")
|
||||
// println(" **** Convergence in r.h.s. (\"JtWdy\") ****")
|
||||
// println(" **** epsilon_1 = $epsilon_1")
|
||||
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.inRHS_JtWdy
|
||||
resultInfo.epsilon = epsilon_1
|
||||
stop = true
|
||||
}
|
||||
if ((abs(h.as2D()).div(abs(p) + 1e-12)).max() < epsilon_2 && settings.iteration > 2) {
|
||||
println(" **** Convergence in Parameters ****")
|
||||
println(" **** epsilon_2 = $epsilon_2")
|
||||
// println(" **** Convergence in Parameters ****")
|
||||
// println(" **** epsilon_2 = $epsilon_2")
|
||||
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.inParameters
|
||||
resultInfo.epsilon = epsilon_2
|
||||
stop = true
|
||||
}
|
||||
if (X2 / DoF < epsilon_3 && settings.iteration > 2) {
|
||||
println(" **** Convergence in reduced Chi-square **** ")
|
||||
println(" **** epsilon_3 = $epsilon_3")
|
||||
// println(" **** Convergence in reduced Chi-square **** ")
|
||||
// println(" **** epsilon_3 = $epsilon_3")
|
||||
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.inReducedChi_square
|
||||
resultInfo.epsilon = epsilon_3
|
||||
stop = true
|
||||
}
|
||||
if (settings.iteration == MaxIter) {
|
||||
println(" !! Maximum Number of Iterations Reached Without Convergence !!")
|
||||
// println(" !! Maximum Number of Iterations Reached Without Convergence !!")
|
||||
resultInfo.typeOfConvergence = LinearOpsTensorAlgebra.TypeOfConvergence.noConvergence
|
||||
resultInfo.epsilon = 0.0
|
||||
stop = true
|
||||
}
|
||||
} // --- End of Main Loop
|
||||
|
||||
@ -8,6 +8,7 @@ package space.kscience.kmath.tensors.core
|
||||
|
||||
import space.kscience.kmath.nd.*
|
||||
import space.kscience.kmath.operations.invoke
|
||||
import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
|
||||
import space.kscience.kmath.tensors.core.internal.LMSettings
|
||||
import space.kscience.kmath.testutils.assertBufferEquals
|
||||
import kotlin.math.roundToInt
|
||||
@ -290,5 +291,6 @@ internal class TestDoubleTensorAlgebra {
|
||||
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)
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user