X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/desynchronisation-controle.git/blobdiff_plain/bc18d50297e1b6798015a28bc91eee4f7f3752ca..fe438aa764b5d1ad7ea61512cffbb3761eff99b7:/exp_controle_asynchrone/simulMWSN.py diff --git a/exp_controle_asynchrone/simulMWSN.py b/exp_controle_asynchrone/simulMWSN.py index bf17462..45326b6 100644 --- a/exp_controle_asynchrone/simulMWSN.py +++ b/exp_controle_asynchrone/simulMWSN.py @@ -7,9 +7,12 @@ from itertools import * from scipy import optimize as opt from copy import deepcopy import sys as sy +import cv as cv +import cv2 as cv2 +import datetime as dt - -error = 0.1 +errorq = 0.1 +errorD = 1E-2 epsilon = 1E-10 vrate = 0.8 p = 0.7 @@ -32,6 +35,9 @@ lg = [(0, 1, 22.004323820359151), (0, 2, 28.750632705280324), (0, 3, 29.68069293 #lg= [(0,1,23),(1,0,15),(1,2,45)] sink = n-1 +def distance(d1,d2): + return mt.sqrt(sum([(d1[t]-d2[t])**2 for t in d1])) + def genereGraph(): test = False @@ -46,7 +52,54 @@ def genereGraph(): G.add_edge(io,ie,weight=dist) G.add_edge(ie,io,weight=dist) test = not(any([ not(nx.has_path(G,o,sink)) for o in G.nodes() if sink in G.nodes() and o != sink])) - return G + return (G,l) + + +def afficheGraph(G,l,tx,ty,sink): + r = 20 + img = cv.CreateImage ((tx, ty), 32, 3) + cv.Rectangle(img, (0,0),(tx,ty), cv.Scalar(255,255,255), thickness=-1) + def px((x,y)): + u = float(tx)/(coteCarre + 2*distanceEmmissionMax) + return(int(distanceEmmissionMax*u + x * u),int(distanceEmmissionMax*u + y * u)) + for i in set(range(len(l)))-set([sink]): + (x,y) = l[i] + pix,piy = px((x,y)) + demx = distanceEmmissionMax*tx/(coteCarre+2*distanceEmmissionMax) + cv.Circle(img, (pix,piy),demx, cv.Scalar(125,125,125)) + + for i in set(range(len(l)))-set([sink]): + (x,y) = l[i] + pix,piy = px((x,y)) + cv.Circle(img, (pix,piy),r, cv.Scalar(125,125,125),thickness=-1) + + #sink + (x,y) = l[sink] + pix,piy = px((x,y)) + + cv.Rectangle(img, (pix-r/2,piy-r/2),(pix+r/2,piy+r/2), cv.Scalar(125,125,125), thickness=-1) + + for i in range(len(l)): + for j in range(len(l)): + + if np.linalg.norm(np.array(l[i])-np.array(l[j])) < distanceEmmissionMax : + (xi,yi) = l[i] + pixi,piyi = px((xi,yi)) + (xj,yj) = l[j] + pixj,piyj = px((xj,yj)) + cv.Line(img, (pixi,piyi), (pixj,piyj), cv.Scalar(125,125,125)) + + + """ + for i in range(len(l)): + (x,y) = l[i] + pix,piy = px((x,y)) + print i + textColor = (0, 0, 255) # red + font = cv2.FONT_HERSHEY_SIMPLEX + imgp = + cv2.putText(img, str(i), (pix-r/4,piy-r/2),font, 3.0, textColor)#,thickn """ + cv.SaveImage("SensorNetwork.png",img) G = nx.DiGraph() G.add_weighted_edges_from(lg) @@ -56,6 +109,19 @@ G.add_weighted_edges_from(lg) #nx.draw(G) #pb.show() +#(G,l) = genereGraph() +N = G.nodes() +#V = list(set(sample(N,int(len(N)*vrate)))-set([sink])) +V = list(set(N)-set([sink])) +source = V +print "source",source + + +#afficheGraph(G,l,500,500,sink) +#nx.draw(G) +#pb.show() + + @@ -64,16 +130,9 @@ G.add_weighted_edges_from(lg) #print G.edges(data=True) #TODO afficher le graphe et etre sur qu'il est connexe -N = G.nodes() - -#V = list(set(sample(N,int(len(N)*vrate)))-set([sink])) -V = list(set(N)-set([sink])) -source = V -print "source",source - L = range(len(G.edges())) d = [di['weight'] for (_,_,di) in G.edges(data=True)] @@ -107,7 +166,6 @@ alpha = 0.5 beta = 1.3E-8 gamma = 55.54 delta = 0.2 -zeta = 0.1 amplifieur = 1 sigma2 = 3500 Bi = 5 @@ -146,8 +204,6 @@ def aminp(l): -def distance(d1,d2): - return mt.sqrt(sum([(d1[t]-d2[t])**2 for t in d1])) def AfficheVariation (up,vp,lap,wp,thetap,etap,qp,Psp,Rhp,xp,valeurFonctionDualep): @@ -187,19 +243,11 @@ def armin(f,xini,xr,args): #xpos = lambda x : x if x > 0 else -1 r = 0 if xr == POS : - #print "strictement pos" - #print "parametres passes a cobyla",xini,xpos,args,"consargs=(),rhoend=1E-5,iprint=0,maxfun=1000" - r= opt.fmin_cobyla(f,xini,cons=[xpos],args=args,consargs=(),rhoend=1E-3,iprint=0,maxfun=nbiter) - #print "le min str pos est",r - #print "le min pos est", r,"avec xini",xini + r= opt.fmin_cobyla(f,xini,cons=[xpos],args=args,consargs=(),rhoend=errorD/100,iprint=0,maxfun=nbiter) elif xr == POS_NUL : - #print "pos ou nul" - r = opt.fmin_cobyla(f,xini,[xposounul],args,consargs=(),rhoend=1E-3,iprint=0,maxfun=nbiter) - # print "le min pos est", r - #print "le min pos null est", r,"avec xini",xini + r = opt.fmin_cobyla(f,xini,[xposounul],args,consargs=(),rhoend=errorD/100,iprint=0,maxfun=nbiter) elif xr == POSINF1: - r = opt.fmin_cobyla(f,xini,[entre0et1],args,consargs=(),rhoend=1E-3,iprint=0,maxfun=nbiter) - #print "le min pos inf 1 est", r,"avec xini",xini + r = opt.fmin_cobyla(f,xini,[entre0et1],args,consargs=(),rhoend=errorD/100,iprint=0,maxfun=nbiter) return r @@ -220,7 +268,7 @@ def maj_theta(k): -def maj(k,maj_theta,mxg,idxexp): +def maj(k,maj_theta,mxg,idxexp,comppsh=False): # initialisation des operateurs lagrangiens global u, v, la, w, theta , q, Ps, Rh, eta, x, valeurFonctionDuale @@ -290,39 +338,59 @@ def maj(k,maj_theta,mxg,idxexp): qp={} - def f_q(qi,i): + def f_q(qi,*args): fa = sum([a[i][l]*w[l] for l in L]) - la[i]*Bi return qi*qi + qi*fa + tq,tqa = 0,0 for i in N: if not ASYNC or random() < taux_succes: + n1 = dt.datetime.now() c = -float(sum([a[i][l]*w[l] for l in L]) - la[i]*Bi)/(2*amplifieur) + n2 = dt.datetime.now() + #cp = armin(f_q,q[i],POS,tuple([i])) + n3 = dt.datetime.now() + tq += (n2-n1).microseconds + tqa += (n3-n2).microseconds rep = epsilon if c <= 0 else c qp[i] = rep else : qp[i] = q[i] - - + tp,tpa = 0,0 Psp={} - #print "maj des des Psh" def f_Ps(psh,h): - #print "ds f_ps",psh, v[h]* mt.log(float(sigma2)/D)/(gamma*((psh**2)**(float(2)/3))) +la[h]*psh - return v[h]* mt.log(float(sigma2)/D)/(gamma*mt.pow(float(2)/3)) +la[h]*psh - for h in V: - if not ASYNC or random() < taux_succes: - """ - lah = 0.05 if la[h] == 0 else la[h] - rep = mt.pow(float(2*v[h]*mt.log(float(sigma2)/D))/(3*gamma*lah),float(3)/5) - Psp[h] = epsilon if rep <= 0 else rep - """ - t= float(-3*la[h]+mt.sqrt(9*(la[h]**2)+64*zeta*v[h]*mt.log(float(sigma2)/D)/gamma))/(16*zeta) - #print t - rep = mt.pow(t,float(3)/5) - Psp[h]=rep - else : + try: + #print psh,(gamma*mt.pow(psh,float(2)/3)) + res= v[h]* mt.log(float(sigma2)/D)/(gamma*mt.pow(psh,float(2)/3)) +la[h]*psh + delta*mt.pow(psh,float(8)/3) + return res + except : + return 0 + for h in V: + if not ASYNC or random() < taux_succes: + n1 = dt.datetime.now() + #calcul nouvelle solution + if comppsh : + if la[h] != 0 : + t= float(2*v[h]*mt.log(float(sigma2)/D))/(3*gamma*la[h]) + rep = mt.pow(t,float(3)/5) + else : + rep = 10 + else : + t= float(-3*la[h]+mt.sqrt(9*(la[h]**2)+64*delta*v[h]*mt.log(float(sigma2)/D)/gamma))/(16*delta) + rep = mt.pow(t,float(3)/5) + + + + n2 = dt.datetime.now() + #cp = armin(f_Ps,Ps[h],POS,tuple([h])) + n3 = dt.datetime.now() + tp += (n2-n1).microseconds + tpa += (n3-n2).microseconds + Psp[h]=rep + else : Psp[h] = Ps[h] @@ -336,26 +404,43 @@ def maj(k,maj_theta,mxg,idxexp): Rhp={} + tr,tra=0,0 def f_Rh(rh,h): return delta*rh*rh-v[h]*rh-sum([u[(h,i)]*eta[(h,i)] for i in N]) - for h in V: if not ASYNC or random() < taux_succes: rep = float(v[h])/(2*delta) + n1 = dt.datetime.now() + rep = float(v[h])/(2*delta) + n2 = dt.datetime.now() + #cp = armin(f_Rh,Rh[h],POS_NUL,tuple([h])) + n3 = dt.datetime.now() + tr += (n2-n1).microseconds + tra += (n3-n2).microseconds Rhp[h] = 0 if rep < 0 else rep else : Rhp[h] = Rh[h] + + + xp={} + tx,txa=0,0 def f_x(xhl,h,l): r = delta*xhl*xhl + xhl*(cs[l]*sum([la[i]*aplus[i][l] for i in N]) +cr*sum([la[i]*amoins[i][l] for i in N])+sum([u[(h,i)]*a[i][l] for i in N])) return r - for h in V: for l in L: if not ASYNC or random() < taux_succes: + n1 = dt.datetime.now() rep = -float(cs[l]*sum([la[i]*aplus[i][l] for i in N]) +cr*sum([la[i]*amoins[i][l] for i in N])+sum([u[(h,i)]*a[i][l] for i in N]))/(2*delta) + n2 = dt.datetime.now() + #cp = armin(f_x,x[(h,l)],POS_NUL,tuple([h,l])) + n3 = dt.datetime.now() + tx += (n2-n1).microseconds + txa += (n3-n2).microseconds + xp[(h,l)] = 0 if rep < 0 else rep else : xp[(h,l)] = x[(h,l)] @@ -376,9 +461,9 @@ def maj(k,maj_theta,mxg,idxexp): #AfficheVariation(up,vp,lap,wp,thetap,etap,qp,Psp,Rhp,xp,valeurFonctionDualep) - arret = abs(valeurFonctionDuale-valeurFonctionDualep) < error + arret = abs(valeurFonctionDuale-valeurFonctionDualep) - return (up,vp,lap,wp,thetap,etap,qp,Psp,Rhp,xp,valeurFonctionDualep,arret,mxg,smax) + return (up,vp,lap,wp,thetap,etap,qp,Psp,Rhp,xp,valeurFonctionDualep,arret,mxg,smax,tq+tp+tr+tx,tqa+tpa+tra+txa) @@ -468,15 +553,15 @@ def initialisation_(): def __evalue_maj_theta__(nbexp,out=False): global u, v, la, w, theta , q, Ps, Rh, eta, x, valeurFonctionDuale - nbexp = 10 res = {} m = [] itermax = 100000 - + def __maj_theta(k): mem = [] om = omega/(mt.pow(k,0.75)) return om + liste_arret=[] for idxexp in range(nbexp): mxg = 0 if not(out): @@ -487,24 +572,34 @@ def __evalue_maj_theta__(nbexp,out=False): k = 1 arret = False sm = 0 + err, ar = 0,0 + dur,dura=0,0 while k < itermax and not arret : - (u,v,la,w,theta,eta,q,Ps,Rh,x,valeurFonctionDuale,ar,mxg,smax)=maj(k,__maj_theta,mxg,idxexp) - errorq = (max(q.values()) - min(q.values()))/min(q.values()) - arret = errorq < error + print "ici" + (u,v,la,w,theta,eta,q,Ps,Rh,x,valeurFonctionDuale,ar,mxg,smax,ct,cta)=maj(k,__maj_theta,mxg,idxexp) + err = (max(q.values()) - min(q.values()))/min(q.values()) + dur += ct + dura += cta + arret = err < errorq or ar < errorD k+=1 variation = "+" if smax > sm else "-" - print variation, - if k%100 ==0 : - print "k:",k,"erreur sur q", errorq, "et q:",q - print "maxg=", mxg + if out : print variation, + if k%10 ==0 : + print "k:", k, + "erreur sur q", + errorq, "erreur sur F", ar, "et q:", q + + if out : print "maxg=", mxg mem = [deepcopy(q),deepcopy(Ps),deepcopy(Rh),deepcopy(eta), deepcopy(x),deepcopy(u),deepcopy(v),deepcopy(la),deepcopy(w)] + """ if k%4500 == 0 : - print "#########\n",mem,"\#########\n" + + #print "#########\n",mem,"\#########\n" if k%4600 == 0 : - print "#########\n",mem,"\#########\n" - - + #print "#########m\n",mem,"\#########\n" + """ + if smax - sm > 500: print "variation trop grande" @@ -512,20 +607,24 @@ def __evalue_maj_theta__(nbexp,out=False): print init exit sm = smax - + if k == itermax: print "nbre d'iteration trop grand" print "init" print init sy.exit(1) + else : + liste_arret += [(err, ar,k,errorD)] print "###############" print k print "###############" m += [k] - - print (min(m),max(m),float(sum(m))/nbexp,m),m - + + #print liste_arret + #print (min(m),max(m),float(sum(m))/nbexp,m),m + #return (liste_arret,dur,dura) + return (liste_arret,dur) def __une_seule_exp__(fichier_donees): @@ -586,8 +685,186 @@ def __une_seule_exp__(fichier_donees): ASYNC = False -__une_seule_exp__("config_initiale.py") -#__evalue_maj_theta__() +#__une_seule_exp__("config_initiale.py") +#pour evaluerle test d'arret +""" +listederes=[] +for k in list(np.logspace(-6, -3, num=15)): + errorD = k + listederes += __evalue_maj_theta__(5) + +print listederes +""" + + + + +def __comparePsh_et_Psh_avec8_3__(nbexp,out=False): + print "tttttttttttt" + global u, v, la, w, theta , q, Ps, Rh, eta, x, valeurFonctionDuale + res = {} + m,mp = [],[] + itermax = 100000 + + def __maj_theta(k): + mem = [] + om = omega/(mt.pow(k,0.75)) + return om + liste_arret=[] + for idxexp in range(nbexp): + mxg = 0 + if not(out): + initialisation() + else : + initialisation_() + + k = 1 + arret = False + sm = 0 + err, ar = 0,0 + dur,dura=0,0 + + # duppliquee + while k < itermax and not arret : + (u,v,la,w,theta,eta,q,Ps,Rh,x,valeurFonctionDuale,ar,mxg,smax,ct,cta)=maj(k,__maj_theta,mxg,idxexp) + err = (max(q.values()) - min(q.values()))/min(q.values()) + dur += ct + dura += cta + arret = err < errorq or ar < errorD + k+=1 + variation = "+" if smax > sm else "-" + if out : print variation, + if k%100 ==0 : + print "k:", k, + "erreur sur q", + errorq, "erreur sur F", ar, "et q:", q + + if out : print "maxg=", mxg + mem = [deepcopy(q),deepcopy(Ps),deepcopy(Rh),deepcopy(eta), + deepcopy(x),deepcopy(u),deepcopy(v),deepcopy(la),deepcopy(w)] + """ + if k%4500 == 0 : + + #print "#########\n",mem,"\#########\n" + if k%4600 == 0 : + #print "#########m\n",mem,"\#########\n" + """ + + + if smax - sm > 500: + print "variation trop grande" + print "init" + print init + exit + sm = smax + + if k == itermax: + print "nbre d'iteration trop grand" + print "init" + print init + sy.exit(1) + else : + liste_arret += [(err, ar,k,errorD)] + + print "###############" + print k," avec optym" + print "###############" + m += [k] + ##fin de duplication + + + + #remise a zero + q,Ps,Rh,eta,x,u,v,la,w=init[0],init[1],init[2],init[3],init[4],init[5],init[6],init[7],init[8] + k = 1 + arret = False + sm = 0 + err, ar = 0,0 + dur,dura=0,0 + # duppliquee + while k < itermax and not arret : + (u,v,la,w,theta,eta,q,Ps,Rh,x,valeurFonctionDuale,ar,mxg,smax,ct,cta)=maj(k,__maj_theta,mxg,idxexp,True) + err = (max(q.values()) - min(q.values()))/min(q.values()) + dur += ct + dura += cta + arret = err < errorq or ar < errorD + k+=1 + variation = "+" if smax > sm else "-" + if out : print variation, + if k%100 ==0 : + print "k:", k, + "erreur sur q", + errorq, "erreur sur F", ar, "et q:", q + + if out : print "maxg=", mxg + mem = [deepcopy(q),deepcopy(Ps),deepcopy(Rh),deepcopy(eta), + deepcopy(x),deepcopy(u),deepcopy(v),deepcopy(la),deepcopy(w)] + """ + if k%4500 == 0 : + + #print "#########\n",mem,"\#########\n" + if k%4600 == 0 : + #print "#########m\n",mem,"\#########\n" + """ + + + if smax - sm > 500: + print "variation trop grande" + print "init" + print init + exit + sm = smax + + if k == itermax: + print "nbre d'iteration trop grand" + print "init" + print init + sy.exit(1) + else : + liste_arret += [(err, ar,k,errorD)] + + print "###############" + print k,"sans optym" + print "###############" + mp += [k] + + + #print liste_arret + #print (min(m),max(m),float(sum(m))/nbexp,m),m + #return (liste_arret,dur,dura) + return (m,mp) + + + +# pour evaluer argmin +""" +listederes=[] +k=0 +for k in list(np.logspace(-5, -3, num=10)): + print "k",k + errorD = k + listederes += [(k,__evalue_maj_theta__(3))] + k+=1 +print listederes +""" + +# pour evaluer psh +listederes=[] +for k in list(np.logspace(-5, -3, num=10)): + print "Precision",k + errorD = k + listederes += [(k,__comparePsh_et_Psh_avec8_3__(10))] + k+=1 +print listederes + + + + + + +print"#######################" + + #ASYNC = True #taux_succes = 0.9 #__evalue_maj_theta__() @@ -595,7 +872,7 @@ __une_seule_exp__("config_initiale.py") - +""" print "u",u print "v",v print "lambda",la @@ -608,7 +885,7 @@ print "Rh",Rh print "x",x print "L",valeurFonctionDuale - +""" # relation ente les variables primaires et secondaires ?