6 from itertools import *
7 from scipy import optimize as opt
8 from copy import deepcopy
19 distanceEmmissionMax = 30
25 fichier_init="config_initiale_default.txt"
29 # construction du graphe
31 lg = [(0, 1, 22.004323820359151), (0, 2, 28.750632705280324), (0, 3, 29.68069293796183), (0, 4, 8.547146256271331), (0, 5, 28.079672647730469), (0, 7, 23.017867703525138), (0, 8, 6.1268526078857208), (0, 9, 24.573433868296771), (1, 0, 22.004323820359151), (1, 2, 18.807277287689722), (1, 3, 18.982897767602783), (1, 4, 16.848855991756174), (1, 5, 17.042671653231526), (1, 6, 16.410544777532913), (1, 7, 25.598808236367063), (1, 8, 20.175759189503321), (1, 9, 12.843763853990932), (2, 0, 28.750632705280324), (2, 1, 18.807277287689722), (2, 3, 1.0957062702237066), (2, 4, 29.159997765424084), (2, 5, 1.8557839901886808), (2, 6, 23.122260476726876), (2, 9, 6.052562826627808), (3, 0, 29.68069293796183), (3, 1, 18.982897767602783), (3, 2, 1.0957062702237066), (3, 4, 29.884008054261855), (3, 5, 1.9922790489539697), (3, 6, 22.479228556182363), (3, 9, 6.4359869969688059), (4, 0, 8.547146256271331), (4, 1, 16.848855991756174), (4, 2, 29.159997765424084), (4, 3, 29.884008054261855), (4, 5, 28.006189408396626), (4, 7, 15.774839848636024), (4, 8, 3.6206480052249144), (4, 9, 23.804744370383144), (5, 0, 28.079672647730469), (5, 1, 17.042671653231526), (5, 2, 1.8557839901886808), (5, 3, 1.9922790489539697), (5, 4, 28.006189408396626), (5, 6, 21.492976178079076), (5, 8, 29.977996181215822), (5, 9, 4.4452006140146185), (6, 1, 16.410544777532913), (6, 2, 23.122260476726876), (6, 3, 22.479228556182363), (6, 5, 21.492976178079076), (6, 9, 20.04488615603487), (7, 0, 23.017867703525138), (7, 1, 25.598808236367063), (7, 4, 15.774839848636024), (7, 8, 16.915923579829141), (8, 0, 6.1268526078857208), (8, 1, 20.175759189503321), (8, 4, 3.6206480052249144), (8, 5, 29.977996181215822), (8, 7, 16.915923579829141), (8, 9, 25.962918470558208), (9, 0, 24.573433868296771), (9, 1, 12.843763853990932), (9, 2, 6.052562826627808), (9, 3, 6.4359869969688059), (9, 4, 23.804744370383144), (9, 5, 4.4452006140146185), (9, 6, 20.04488615603487), (9, 8, 25.962918470558208)]
34 #lg= [(0,1,23),(1,0,15),(1,2,45)]
38 return mt.sqrt(sum([(d1[t]-d2[t])**2 for t in d1]))
45 l = [(random()*coteCarre, random()*coteCarre) for _ in range(n)]
46 for io in range(len(l)) :
47 for ie in range(len(l)) :
49 dist = mt.sqrt((l[io][0]-l[ie][0])**2 + (l[io][1]-l[ie][1])**2)
50 if dist <= distanceEmmissionMax :
51 G.add_edge(io,ie,weight=dist)
52 G.add_edge(ie,io,weight=dist)
53 test = not(any([ not(nx.has_path(G,o,sink)) for o in G.nodes() if sink in G.nodes() and o != sink]))
57 def afficheGraph(G,l,tx,ty,sink):
59 img = cv.CreateImage ((tx, ty), 32, 3)
60 cv.Rectangle(img, (0,0),(tx,ty), cv.Scalar(255,255,255), thickness=-1)
62 return(int(tx*x/coteCarre),ty-int(ty*y/coteCarre))
63 for i in set(range(len(l)))-set([sink]):
66 demx = distanceEmmissionMax*tx/coteCarre
67 cv.Circle(img, (pix,piy),demx, cv.Scalar(125,125,125))
69 for i in set(range(len(l)))-set([sink]):
72 cv.Circle(img, (pix,piy),r, cv.Scalar(125,125,125),thickness=-1)
78 cv.Rectangle(img, (pix-r/2,piy-r/2),(pix+r/2,piy+r/2), cv.Scalar(125,125,125), thickness=-1)
80 for i in range(len(l)):
81 for j in range(len(l)):
83 if np.linalg.norm(np.array(l[i])-np.array(l[j])) < distanceEmmissionMax :
85 pixi,piyi = px((xi,yi))
87 pixj,piyj = px((xj,yj))
88 cv.Line(img, (pixi,piyi), (pixj,piyj), cv.Scalar(125,125,125))
92 for i in range(len(l)):
96 textColor = (0, 0, 255) # red
97 font = cv2.FONT_HERSHEY_SIMPLEX
99 cv2.putText(img, str(i), (pix-r/4,piy-r/2),font, 3.0, textColor)#,thickn """
100 cv.SaveImage("SensorNetwork.png",img)
103 G.add_weighted_edges_from(lg)
104 #print nx.is_strongly_connected(G)
110 #(G,l) = genereGraph()
112 #V = list(set(sample(N,int(len(N)*vrate)))-set([sink]))
113 V = list(set(N)-set([sink]))
115 print "source",source
116 #afficheGraph(G,l,500,500,sink)
127 #print G.edges(data=True)
128 #TODO afficher le graphe et etre sur qu'il est connexe
134 L = range(len(G.edges()))
135 d = [di['weight'] for (_,_,di) in G.edges(data=True)]
142 assert l in L, " pb de dimennsion de l: "+str(l)+ " "+ str(L)
154 a = [[ail(i,l) for l in L ] for i in xrange(n)]
155 aplus = [[1 if ail(i,l) > 0 else 0 for l in L ] for i in xrange(n)]
156 amoins = [[1 if ail(i,l) < 0 else 0 for l in L ] for i in xrange(n)]
174 cs = [alpha + beta*(di**path_loss_exp) for di in d]
197 return cmp(x1[1],x2[1])
207 def AfficheVariation (up,vp,lap,wp,thetap,etap,qp,Psp,Rhp,xp,valeurFonctionDualep):
208 global u, v, la, w, theta , q, Ps, Rh, eta, x,valeurFonctionDuale
210 print "du=",distance(u,up),
211 print "dv=",distance(v,vp),
212 print "dlambda=",distance(la,lap),
213 print "dw=",distance(w,wp),
214 print "dtheta=",abs(theta-thetap),
215 print "deta=",distance(eta,etap),
216 print "dq=",distance(q,qp),
217 print "dPs=",distance(Ps,Psp),
218 print "dRh=",distance(Rh,Rhp),
219 print "dx=",distance(x,xp),
220 print "dL=",abs(valeurFonctionDuale-valeurFonctionDualep),"\n"
223 valeurFonctionDuale = 0
226 r = x if (x >0 and x <= 1) else -1
227 #print "ds entre0et1 x et r", x,r
232 r = x if x >0 else -1
233 #print "ds xpos x et r", x,r
237 return x if x >= 0 else -1
240 def armin(f,xini,xr,args):
241 #xpos = lambda x : x if x > 0 else -1
244 #print "strictement pos"
245 #print "parametres passes a cobyla",xini,xpos,args,"consargs=(),rhoend=1E-5,iprint=0,maxfun=1000"
246 r= opt.fmin_cobyla(f,xini,cons=[xpos],args=args,consargs=(),rhoend=1E-3,iprint=0,maxfun=nbiter)
247 #print "le min str pos est",r
248 #print "le min pos est", r,"avec xini",xini
251 r = opt.fmin_cobyla(f,xini,[xposounul],args,consargs=(),rhoend=1E-3,iprint=0,maxfun=nbiter)
252 # print "le min pos est", r
253 #print "le min pos null est", r,"avec xini",xini
255 r = opt.fmin_cobyla(f,xini,[entre0et1],args,consargs=(),rhoend=1E-3,iprint=0,maxfun=nbiter)
256 #print "le min pos inf 1 est", r,"avec xini",xini
267 return omega/(mt.pow(k,0.5))
272 return omega/mt.sqrt(k)
277 def maj(k,maj_theta,mxg,idxexp):
278 # initialisation des operateurs lagrangiens
279 global u, v, la, w, theta , q, Ps, Rh, eta, x, valeurFonctionDuale
287 if not ASYNC or random() < taux_succes:
288 s = eta[(h,i)]-sum([a[i][l]*x[(h,l)] for l in L])
290 print "ds calcul u",abs(s),idxexp
292 smax = max(smax,abs(s))
293 up[(h,i)] = u[(h,i)]-theta*s
301 if not ASYNC or random() < taux_succes:
302 s = Rh[h]- mt.log(float(sigma2)/D)/(gamma*mt.pow(Ps[h],float(2)/3))
304 print "ds calcul v",abs(s),idxexp
306 smax = max(smax,abs(s))
307 vp[h] = max(0,v[h]-theta*s)
315 if not ASYNC or random() < taux_succes:
316 s = q[i]*Bi -sum([aplus[i][l]*cs[l]*sum([x[(h,l)] for h in V]) for l in L])-cr*sum([amoins[i][l]*sum([x[(h,l)] for h in V]) for l in L])-psi(Ps,i)
318 print "ds calcul la",abs(s),idxexp,i
320 smax = max(smax,abs(s))
321 resla = la[i]-theta*s
322 lap[i] = max(0,resla)
331 if not ASYNC or random() < taux_succes:
332 s = sum([a[i][l]*q[i] for i in N])
334 print "ds calcul w",abs(s),idxexp
336 smax = max(smax,abs(s))
337 wp[l] = w[l] + theta*s
343 thetap = maj_theta(k)
348 fa = sum([a[i][l]*w[l] for l in L]) - la[i]*Bi
352 if not ASYNC or random() < taux_succes:
353 c = -float(sum([a[i][l]*w[l] for l in L]) - la[i]*Bi)/(2*amplifieur)
354 rep = epsilon if c <= 0 else c
364 #print "maj des des Psh"
366 #print "ds f_ps",psh, v[h]* mt.log(float(sigma2)/D)/(gamma*((psh**2)**(float(2)/3))) +la[h]*psh
367 return v[h]* mt.log(float(sigma2)/D)/(gamma*mt.pow(float(2)/3)) +la[h]*psh
369 if not ASYNC or random() < taux_succes:
371 lah = 0.05 if la[h] == 0 else la[h]
372 rep = mt.pow(float(2*v[h]*mt.log(float(sigma2)/D))/(3*gamma*lah),float(3)/5)
373 Psp[h] = epsilon if rep <= 0 else rep
375 t= float(-3*la[h]+mt.sqrt(9*(la[h]**2)+64*zeta*v[h]*mt.log(float(sigma2)/D)/gamma))/(16*zeta)
377 rep = mt.pow(t,float(3)/5)
388 etap[(h,i)] = etahi(h,i,Rh)
394 return delta*rh*rh-v[h]*rh-sum([u[(h,i)]*eta[(h,i)] for i in N])
397 if not ASYNC or random() < taux_succes:
398 rep = float(v[h])/(2*delta)
399 Rhp[h] = 0 if rep < 0 else rep
405 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]))
411 if not ASYNC or random() < taux_succes:
412 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)
413 xp[(h,l)] = 0 if rep < 0 else rep
421 valeurFonctionDualep = 0
422 valeurFonctionDualep += sum([amplifieur*q[i]*q[i] for i in N])
423 valeurFonctionDualep += sum([sum([delta*(x[(h,l)]**2) for l in L]) for h in V])
424 valeurFonctionDualep += sum([delta*(Rh[h]**2) for h in V])
425 valeurFonctionDualep += sum([sum([u[(h,i)]*(sum([ a[i][l]*x[(h,l)] for l in L])- eta[(h,i)]) for i in N]) for h in V])
426 valeurFonctionDualep += sum([v[h]*(mt.log(float(sigma2)/D)/(gamma*mt.pow(Ps[h],float(2)/3)) - Rh[h]) for h in V])
427 valeurFonctionDualep += sum([la[i]*(psi(Ps,i) +sum([aplus[i][l]*cs[l]*sum([x[(h,l)] for h in V]) for l in L])+ cr*sum([ amoins[i][l]*sum([x[(h,l)] for h in V]) for l in L]) -q[i]*Bi) for i in N ])
428 valeurFonctionDualep += sum([w[l]*sum([a[i][l]*q[i] for i in N]) for l in L])
431 #AfficheVariation(up,vp,lap,wp,thetap,etap,qp,Psp,Rhp,xp,valeurFonctionDualep)
433 arret = abs(valeurFonctionDuale-valeurFonctionDualep)
435 return (up,vp,lap,wp,thetap,etap,qp,Psp,Rhp,xp,valeurFonctionDualep,arret,mxg,smax)
456 def initialisation():
457 global u, v, la, w, theta , q, Ps, Rh, eta, x,init
463 q[i] = 0.15 + random()*0.05
468 Ps[h] = 0.2+random()*0.3
472 Rh[vi] = 0.1 + random()*0.1
477 eta[(h,i)]= etahi(h,i,Rh)
487 # initialisation des operateurs lagrangiens
505 init = [deepcopy(q),deepcopy(Ps),deepcopy(Rh),deepcopy(eta),
506 deepcopy(x),deepcopy(u),deepcopy(v),deepcopy(la),deepcopy(w)]
510 def initialisation_():
511 global u, v, la, w, theta , q, Ps, Rh, eta, x,init
512 fd = open(fichier_init,"r")
517 (q,Ps,Rh,eta,x,u,v,la,w) = tuple([deepcopy(x) for x in init_p])
518 init = [deepcopy(q),deepcopy(Ps),deepcopy(Rh),deepcopy(eta),
519 deepcopy(x),deepcopy(u),deepcopy(v),deepcopy(la),deepcopy(w)]
523 def __evalue_maj_theta__(nbexp,out=False):
524 global u, v, la, w, theta , q, Ps, Rh, eta, x, valeurFonctionDuale
531 om = omega/(mt.pow(k,0.75))
534 for idxexp in range(nbexp):
545 while k < itermax and not arret :
546 (u,v,la,w,theta,eta,q,Ps,Rh,x,valeurFonctionDuale,ar,mxg,smax)=maj(k,__maj_theta,mxg,idxexp)
547 err = (max(q.values()) - min(q.values()))/min(q.values())
549 arret = err < errorq or ar < errorD
551 variation = "+" if smax > sm else "-"
552 if out : print variation,
556 errorq, "erreur sur F", ar, "et q:", q
558 if out : print "maxg=", mxg
559 mem = [deepcopy(q),deepcopy(Ps),deepcopy(Rh),deepcopy(eta),
560 deepcopy(x),deepcopy(u),deepcopy(v),deepcopy(la),deepcopy(w)]
564 #print "#########\n",mem,"\#########\n"
566 #print "#########\n",mem,"\#########\n"
571 print "variation trop grande"
578 print "nbre d'iteration trop grand"
583 liste_arret += [(err, ar,k,errorD)]
585 print "###############"
587 print "###############"
591 #print (min(m),max(m),float(sum(m))/nbexp,m),m
595 def __une_seule_exp__(fichier_donees):
596 global u, v, la, w, theta , q, Ps, Rh, eta, x, valeurFonctionDuale
600 fichier = open(fichier_donees, "r")
604 instructions[l[0]] = eval(l[1])
605 u, v, la, w, q, Ps, Rh, eta, x, = instructions['u'], instructions['v'], instructions['la'], instructions['w'], instructions['q'], instructions['Ps'], instructions['Rh'], instructions['eta'], instructions['x']
612 om = omega/(mt.pow(k,0.75))
614 for idxexp in range(nbexp):
619 while k < itermax and not arret :
620 (u,v,la,w,theta,eta,q,Ps,Rh,x,valeurFonctionDuale,ar,mxg,smax)=maj(k,__maj_theta,mxg,idxexp)
621 errorq = (max(q.values()) - min(q.values()))/min(q.values())
622 arret = errorq < error
624 variation = "+" if smax > sm else "-"
627 print "k:",k,"erreur sur q", errorq, "et q:",q
630 print "variation trop grande"
637 print "nbre d'iteration trop grand"
642 print "###############"
644 print "###############"
647 print (min(m),max(m),float(sum(m))/nbexp,m),m
653 #__une_seule_exp__("config_initiale.py")
655 for k in list(np.logspace(-6, -3, num=15)):
657 listederes += __evalue_maj_theta__(5)
662 #__evalue_maj_theta__()
678 print "L",valeurFonctionDuale
682 # relation ente les variables primaires et secondaires ?