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