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Dorchies David authoredcdf55000
import { BoolIdentity, Debug } from "./base";
import { Nub } from "./nub";
import { ParamDefinition } from "./param/param-definition";
import { ParamDomain, ParamDomainValue } from "./param/param-domain";
import { Interval } from "./util/interval";
import { Message, MessageCode } from "./util/message";
import { Result } from "./util/result";
class SearchInterval extends Interval {
private _step: number;
private _dicho: Dichotomie;
private _targets: Interval;
constructor(d: Dichotomie, min: number, max: number, s: number) {
super(min, max);
if (s === 0) {
const e = new Message(MessageCode.ERROR_DICHO_NULL_STEP);
throw e;
}
this._step = s;
this._dicho = d;
}
public reverse() {
this._step = -this._step;
}
public growStep(k: number) {
if (k === 0) {
const e = new Message(MessageCode.ERROR_DICHO_INVALID_STEP_GROWTH);
throw e;
}
this._step *= k;
}
get step() {
return this._step;
}
get targets() {
if (this._targets === undefined) {
// @TODO just set _targets to undefined / null ?
this._targets = new Interval(undefined, undefined);
}
return this._targets;
}
/**
* get target value for lower bound
*/
get targetLower() {
this.updateTargets();
// @TODO val1 is not guaranteed to be the lower bound; use .min ?
return this.targets.val1;
}
/**
* get target value for upper bound
*/
get targetUpper() {
this.updateTargets();
// @TODO val2 is not guaranteed to be the upper bound; use .max ?
return this.targets.val2;
}
public next() {
if (this._step > 0) {
this.val1 = this.val2;
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this.val2 += this._step;
this.targets.setValues(this.targets.val2, undefined);
} else {
this.val2 = this.val1;
this.val1 += this._step;
this.targets.setValues(undefined, this.targets.val1);
}
}
public hasTargetValue(targ: number) {
this.updateTargets();
return this.targets.intervalHasValue(targ);
}
public toString(): string {
return super.toString() + " step=" + this._step;
}
public updateTargets() {
let t1 = this.targets.val1;
if (t1 === undefined || isNaN(t1)) {
t1 = this._dicho.CalculX(this.val1);
}
let t2 = this.targets.val2;
if (t2 === undefined || isNaN(t2)) {
t2 = this._dicho.CalculX(this.val2);
}
this.targets.setValues(t1, t2);
}
}
/**
* calcul par dichotomie
* principe : y=f(x1,x2,...), on connait une valeur de y,
* on cherche par ex le x2 correspondant, les autres xi étant fixés.
* la méthode Equation() calcule analytiquement y=f(xi)
*/
// tslint:disable-next-line:max-classes-per-file
export class Dichotomie extends Debug {
/**
* définition de la variable de la fonction
*/
private _paramX: ParamDefinition;
/**
* nombre d'étapes de recherche de l'intervalle de départ
*/
private _startIntervalMaxSteps = 60;
/**
* nombre d'itérations maxi pour la recherche dichotomique
*/
private _maxIterations: number = 100;
private _currentIterations: number;
/**
* nom du paramètre calculé analytiquement par Equation()
*/
private analyticalSymbol: string;
/**
* Targetted value calculated by the function
*/
private _target: number;
private _func: () => number;
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/**
* Construction de la classe.
* @param nub Noeud de calcul contenant la méthode de calcul Equation
* @param sVarCalc Nom de la variable à calculer
* @param targetSymbol nom du paramètre calculé analytiquement par Equation()
*/
constructor(private nub: Nub, private sVarCalc: string, dbg: boolean = false, func?: () => number) {
super(dbg);
this._paramX = this.nub.getParameter(this.sVarCalc);
if (func !== undefined) {
this._func = func;
} else {
this._func = this.nub.EquationFirstAnalyticalParameter;
this.analyticalSymbol = this.nub.firstAnalyticalPrmSymbol;
}
}
public set maxIterations(n: number) {
this._maxIterations = Math.max(n, 1);
}
/**
* Valeur inconnue à rechercher
*/
private get vX(): number {
return this._paramX.v;
}
private set vX(vCalc: number) {
this._paramX.v = vCalc;
}
/**
* étapes de recherche de l'intervalle de départ
*/
get startIntervalMaxSteps() {
return this._startIntervalMaxSteps;
}
set startIntervalMaxSteps(n: number) {
this._startIntervalMaxSteps = n;
}
public CalculX(x: number): number {
this.vX = x;
return this.Calcul();
}
/**
* trouve la valeur x pour y=f(x) avec un y donné
* @param rTarget valeur de y
* @param rTol tolérance pour l'arrêt de la recherche
* @param rInit valeur initiale approximative de x
*/
public Dichotomie(rTarget: number, rTol: number, rInit: number): Result {
this._target = rTarget;
// console.log("-----");
// for (let x = 0; x <= 1; x += 0.1)
// console.log(this.CalculX(x).vCalc);
// console.log("-----");
// recherche de l'intervalle de départ
this.debug(
"Dichotomie : valeur cible de " + this.analyticalSymbol + "=" + rTarget
+ ", valeur initiale de " + this.sVarCalc + "=" + rInit
);
const r = this.getStartInterval(rTarget, rInit);
if (r.ok) {
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const inter: SearchInterval = r.intSearch;
this._currentIterations = 0;
// Recherche du zero par la méthode de Brent
const s: number = this.uniroot(
this.CalcZero,
this,
inter.min,
inter.max,
rTol
);
if (s === undefined) {
this.debug(
"Dichotomie : Non convergence"
);
return new Result(new Message(MessageCode.ERROR_DICHO_CONVERGE));
} else {
this.debug(
`Dichotomie : ${this.analyticalSymbol}(${this.sVarCalc}=${s}) = ${rTarget}`
);
return new Result(s);
}
// Dichotomie
// this.dichoSearch(
// rTarget,
// rTol,
// inter.min,
// inter.max,
// inter.targets.getVal(inter.minIndex),
// inter.targets.getVal(inter.maxIndex)
// );
} else {
return new Result(r.res);
}
}
/**
* Calcul de l'équation analytique.
* @note Wrapper vers this.nub.Equation pour simplifier le code.
* On utilise la première variable du tableau des variables pouvant être calculée analytiquement
* Il faudra s'assurer que cette première variable correspond à la méthode de calcul la plus rapide
*/
private Calcul(): number {
// return this.nub.Equation(this.analyticalSymbol).vCalc;
return this._func.call(this.nub);
}
/**
* Détermine si une fonction est croissante ou décroissante.
* @param x point auquel on calcul la dérivée
* @param dom domaine de définition de la variable
*/
private isIncreasingFunction(x: number, dom: Interval): boolean {
const epsilon = 1e-8;
const bounds = new Interval(x - epsilon, x + epsilon);
bounds.setInterval(bounds.intersect(dom)); // au cas où l'on sorte du domaine de la variable de la fonction
const y1 = this.CalculX(bounds.min);
const y2 = this.CalculX(bounds.max);
return y2 > y1;
}
/**
* recherche l'intervalle contenant la valeur cible
* @param rTarget valeur cible
* @param intSearch intervalle de départ
* @param intMax intervalle maximum (domaine de définition de la variable)
*/
private searchTarget(rTarget: number, intSearch: SearchInterval, intMax: Interval) {
// tslint:disable-next-line:max-line-length
this.debug(`searchTarget debut: Target ${this.analyticalSymbol}=${rTarget} dans ${this.sVarCalc}=${intSearch.toString()}`);
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let n = 0;
let ok: boolean = false;
do {
intSearch.setInterval(intSearch.intersect(intMax));
if (intSearch.length === 0) {
break;
}
if (intSearch.hasTargetValue(rTarget)) {
ok = true;
break;
}
intSearch.growStep(2);
intSearch.next();
} while (n++ < this._startIntervalMaxSteps);
// tslint:disable-next-line:max-line-length
this.debug(`searchTarget fin: Target ${this.analyticalSymbol}=${rTarget} dans ${this.sVarCalc}=${intSearch.toString()}`);
return { ok, intSearch };
}
/**
* Détermine l'intervalle de recherche initial
* @param rTarget valeur cible de la fonction
* @param rInit valeur initiale de la variable
*/
private getStartInterval(rTarget: number, rInit: number): any {
this.debug("intervalle de départ");
const prmDom: ParamDomain = this._paramX.domain;
const step = 0.001;
// const step = this.nub.prms.Pr.v;
const min: number = prmDom.minValue;
const max: number = prmDom.maxValue;
if (prmDom.domain === ParamDomainValue.NOT_NULL) {
if (rInit === 0) {
rInit = 1e-8;
}
}
const intMax: Interval = new Interval(min, max);
try {
intMax.checkValue(rInit);
} catch (m) {
return { ok: false, res: m };
}
// initialisation de l'intervalle de recherche
const intSearch1: SearchInterval = new SearchInterval(this, rInit - step / 2, rInit + step / 2, step);
// sens de variation de la fonction
const inc = this.isIncreasingFunction(rInit, intMax);
if (BoolIdentity(this.CalculX(rInit) > rTarget, inc)) {
intSearch1.reverse();
// par ex, la fonction est croissante et la valeur initiale
// de la variable a une image par la fonction > valeur cible
}
// on cherche dans une première direction
let a = this.searchTarget(rTarget, intSearch1, intMax);
if (a.ok) {
return a;
}
// il se peut que la fonction ne soit pas monotone et qu'il faille chercher dans l'autre direction
const oldStepSign = intSearch1.step > 0 ? 1 : -1;
const intSearch2 = new SearchInterval(this, rInit + step / 2, rInit + step, step * -oldStepSign);
a = this.searchTarget(rTarget, intSearch2, intMax);
if (a.ok) {
return a;
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}
// gestion de l'erreur
// la valeur cible de la fonction est elle trouvable ?
let res: Message;
let errDomain = false;
switch (prmDom.domain) {
case ParamDomainValue.INTERVAL:
const si: SearchInterval = new SearchInterval(this, intMax.min, intMax.max, 1);
errDomain = !si.hasTargetValue(rTarget);
break;
case ParamDomainValue.POS:
case ParamDomainValue.POS_NULL:
const y = this.CalculX(1e-8);
errDomain = inc && (rTarget < y);
break;
case ParamDomainValue.ANY:
break;
default:
throw new Error("unsupported parameter domain value");
}
if (errDomain) {
res = new Message(MessageCode.ERROR_DICHO_INIT_DOMAIN);
res.extraVar.targetSymbol = this.analyticalSymbol; // symbole de la variable calculée par la fonction
res.extraVar.targetValue = rTarget; // valeur cible pour la fonction
// intervalle de valeurs pour la variable d'entrée de la fonction
res.extraVar.variableInterval = intMax.toString();
res.extraVar.variableSymbol = this._paramX.symbol; // symbole de la variable d'entrée de la fonction
} else {
// Fusion des infos des deux intervalles explorés
const intFus: number[] = []; // Signification des indices : 0,1 : minmax target; 2, 3 : variables associées
if (isNaN(intSearch1.targets.max) || isNaN(intSearch2.targets.max)) {
// bug targets en NaN en prod mais pas en debug pas à pas en explorant les variables
intSearch1.updateTargets();
intSearch2.updateTargets();
}
if (intSearch1.targets.max > intSearch2.targets.max) {
intFus[1] = intSearch1.targets.max;
intFus[3] = intSearch1.getVal(intSearch1.targets.maxIndex);
} else {
intFus[1] = intSearch2.targets.max;
intFus[3] = intSearch2.getVal(intSearch2.targets.maxIndex);
}
if (intSearch1.targets.min < intSearch2.targets.min) {
intFus[0] = intSearch1.targets.min;
intFus[2] = intSearch1.getVal(intSearch1.targets.minIndex);
} else {
intFus[0] = intSearch2.targets.min;
intFus[2] = intSearch2.getVal(intSearch2.targets.minIndex);
}
if (intFus[1] < rTarget) {
res = new Message(MessageCode.ERROR_DICHO_TARGET_TOO_HIGH);
res.extraVar.extremeTarget = intFus[1];
res.extraVar.variableExtremeValue = intFus[3];
} else {
res = new Message(MessageCode.ERROR_DICHO_TARGET_TOO_LOW);
res.extraVar.extremeTarget = intFus[0];
res.extraVar.variableExtremeValue = intFus[2];
}
res.extraVar.variableSymbol = this._paramX.symbol; // symbole de la variable de la fonction
res.extraVar.targetSymbol = this.analyticalSymbol; // symbole de la variable calculée par la fonction
res.extraVar.targetValue = rTarget; // valeur cible pour la fonction
}
return { ok: false, res };
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}
/**
* effectue récursivement la recherche dichotomique
* @param Xmin valeur min de l'intervalle
* @param Xmax valeur max de l'intervalle
* @param Y valeur cible de la fonction
* @param Ymin valeur de la fonction pour Xmin
* @param Ymax valeur de la fonction pour Xmax
*/
// tslint:disable-next-line:variable-name
private dichoSearch(Ytarg: number, rTol: number, Xmin: number, Xmax: number, Ymin: number, Ymax: number): Result {
this._currentIterations++;
if (this._currentIterations > this._maxIterations) {
const r = new Result();
r.globalLog.add(new Message(MessageCode.ERROR_DICHO_CONVERGE));
return r;
}
// tslint:disable-next-line:max-line-length
this.debug(`dichoSearch : yTarget ${this.analyticalSymbol}= ${Ytarg} ${this.sVarCalc}=[${Xmin}; ${Xmax}] tol ${rTol}`);
// tslint:disable-next-line:variable-name
const Xmid = (Xmin + Xmax) / 2;
if (Math.abs(Xmin - Xmax) <= rTol) {
return new Result(Xmid);
}
// tslint:disable-next-line:variable-name
const Ymid = this.CalculX(Xmid);
if (Ymin < Ymax) {
// fonction croissante
if (Ytarg > Ymid) {
return this.dichoSearch(Ytarg, rTol, Xmid, Xmax, Ymid, Ymax);
} // intervalle supérieur
return this.dichoSearch(Ytarg, rTol, Xmin, Xmid, Ymin, Ymid); // intervalle inférieur
} else {
// fonction décroissante
if (Ytarg > Ymid) {
return this.dichoSearch(Ytarg, rTol, Xmin, Xmid, Ymin, Ymid);
} // intervalle inférieur
return this.dichoSearch(Ytarg, rTol, Xmid, Xmax, Ymid, Ymax); // intervalle supérieur
}
}
private CalcZero(x: number): number {
this.vX = x;
return this.Calcul() - this._target;
}
/**
* Searches the interval from <tt>lowerLimit</tt> to <tt>upperLimit</tt>
* for a root (i.e., zero) of the function <tt>func</tt> with respect to
* its first argument using Brent's method root-finding algorithm.
*
* Translated from zeroin.c in http://www.netlib.org/c/brent.shar.
*
* Copyright (c) 2012 Borgar Thorsteinsson <borgar@borgar.net>
* MIT License, http://www.opensource.org/licenses/mit-license.php
*
* @param {function} func function for which the root is sought.
* @param {number} lowerlimit the lower point of the interval to be searched.
* @param {number} upperlimit the upper point of the interval to be searched.
* @param {number} errorTol the desired accuracy (convergence tolerance).
* @param {number} maxIter the maximum number of iterations.
* @returns an estimate for the root within accuracy.
*
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*/
private uniroot<T>(func: (param: number) => number, thisArg: T, lowerLimit: number, upperLimit: number,
errorTol: number = 0, maxIter: number = 1000
): number {
let a = lowerLimit;
let b = upperLimit;
let c = a;
let fa = func.call(thisArg, a);
let fb = func.call(thisArg, b);
let fc = fa;
let tolAct; // Actual tolerance
let newStep; // Step at this iteration
let prevStep; // Distance from the last but one to the last approximation
let p; // Interpolation step is calculated in the form p/q; division is delayed until the last moment
let q;
while (maxIter-- > 0) {
prevStep = b - a;
if (Math.abs(fc) < Math.abs(fb)) {
// Swap data for b to be the best approximation
a = b, b = c, c = a;
fa = fb, fb = fc, fc = fa;
}
tolAct = 1e-15 * Math.abs(b) + errorTol / 2;
newStep = (c - b) / 2;
if (
Math.abs(newStep) <= tolAct
&& (
Math.abs(fb) < errorTol
|| Math.abs(fb - fc) < errorTol * 1E5
)
) {
return b; // Acceptable approx. is found
}
// Decide if the interpolation can be tried
if (Math.abs(prevStep) >= tolAct && Math.abs(fa) > Math.abs(fb)) {
// If prev_step was large enough and was in true direction, Interpolatiom may be tried
let t1;
let cb;
let t2;
cb = c - b;
if (a === c) { // If we have only two distinct points linear interpolation can only be applied
t1 = fb / fa;
p = cb * t1;
q = 1.0 - t1;
} else { // Quadric inverse interpolation
q = fa / fc, t1 = fb / fc, t2 = fb / fa;
p = t2 * (cb * q * (q - t1) - (b - a) * (t1 - 1));
q = (q - 1) * (t1 - 1) * (t2 - 1);
}
if (p > 0) {
q = -q; // p was calculated with the opposite sign; make p positive
} else {
p = -p; // and assign possible minus to q
}
if (p < (0.75 * cb * q - Math.abs(tolAct * q) / 2) &&
p < Math.abs(prevStep * q / 2)) {
// If (b + p / q) falls in [b,c] and isn't too large it is accepted
newStep = p / q;
}
// If p/q is too large then the bissection procedure can reduce [b,c] range to more extent
}
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if (Math.abs(newStep) < tolAct) { // Adjust the step to be not less than tolerance
newStep = (newStep > 0) ? tolAct : -tolAct;
}
a = b, fa = fb; // Save the previous approx.
b += newStep, fb = func.call(thisArg, b); // Do step to a new approxim.
if ((fb > 0 && fc > 0) || (fb < 0 && fc < 0)) {
c = a, fc = fa; // Adjust c for it to have a sign opposite to that of b
}
}
return undefined;
}
}