X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/desynchronisation-controle.git/blobdiff_plain/90e98a89726a12ad0e1d68d39c993aa73b3c4230..b41ef425e5e9f8ab1e567e38b17f250bf4a75c75:/exp_controle_asynchrone/simulMWSN.py

diff --git a/exp_controle_asynchrone/simulMWSN.py b/exp_controle_asynchrone/simulMWSN.py
index 4844593..41e82c6 100644
--- a/exp_controle_asynchrone/simulMWSN.py
+++ b/exp_controle_asynchrone/simulMWSN.py
@@ -7,14 +7,14 @@ 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 
 error  = 0.1
 epsilon = 1E-10
 vrate = 0.8
 p = 0.7
 coteCarre = 50
-distanceEmmissionMax = 30
+distanceEmmissionMax = 20
 nbiter = 1000
 POS = 1
 POS_NUL = 2
@@ -32,6 +32,9 @@ 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 +49,53 @@ 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)): 
+        return(int(tx*x/coteCarre),ty-int(ty*y/coteCarre))
+    for i in set(range(len(l)))-set([sink]):
+        (x,y) = l[i]
+        pix,piy = px((x,y))
+        demx = distanceEmmissionMax*tx/coteCarre
+        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 +105,18 @@ 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()
+
+    
+
 
 
 
@@ -64,15 +125,8 @@ 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()))
@@ -146,8 +200,6 @@ 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):
@@ -311,7 +363,7 @@ def maj(k,maj_theta,mxg,idxexp):
     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:
+    for h in V:
          if not ASYNC or  random() < taux_succes:
              """
              lah = 0.05 if la[h] == 0 else  la[h]
@@ -468,6 +520,7 @@ 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
@@ -492,7 +545,6 @@ def __evalue_maj_theta__(nbexp,out=False):
             arret = errorq <  error
             k+=1
             variation = "+" if smax > sm else "-"
-            sm = smax
             print variation,
             if k%100 ==0 :
                 print "k:",k,"erreur sur q", errorq, "et q:",q
@@ -510,7 +562,9 @@ def __evalue_maj_theta__(nbexp,out=False):
                 print "variation trop grande"
                 print "init"
                 print init
-                sy.exit(0)
+                exit 
+            sm = smax
+
         if k == itermax:
             print "nbre d'iteration trop grand"
             print "init"
@@ -526,49 +580,71 @@ def __evalue_maj_theta__(nbexp,out=False):
 
 
 
+def __une_seule_exp__(fichier_donees):
+    global u, v, la, w, theta , q,  Ps, Rh,  eta, x, valeurFonctionDuale 
+    initialisation()
+        
 
+    fichier = open(fichier_donees, "r")
+    instructions ={}
+    for line in fichier:    
+         l = line.split("=")
+         instructions[l[0]] = eval(l[1])
+    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']
+    nbexp = 1
+    res = {}
+    m = []
+    itermax = 100000
+ 
+    def __maj_theta(k):
+        om = omega/(mt.pow(k,0.75))
+        return om
+    for idxexp in range(nbexp):
+        mxg = 0
+        k = 1
+        arret = False
+        sm = 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
+            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 smax - sm  > 500:
+                print "variation trop grande"
+                print "init"
+                print init
+                exit 
+            sm = smax
 
-ASYNC = False
-__evalue_maj_theta__(1,True)
-
+        if k == itermax:
+            print "nbre d'iteration trop grand"
+            print "init"
+            print init
+            exit 
 
+        print "###############"
+        print k
+        print "###############"
+        m += [k]
 
-#ASYNC = True
-#taux_succes = 0.9
-#__evalue_maj_theta__()
+    print (min(m),max(m),float(sum(m))/nbexp,m),m
 
 
-"""
-initialisation()
-k = 1
-arret = False
-while k < 10000  and not arret : 
-    (u,v,la,w,theta,eta,q,Ps,Rh,x,valeurFonctionDuale,ar)=maj(k,maj_theta)
-    arret = ar
-    k+=1
-    errorq =  abs(min(q.values())-max(q.values()))
-    print "errorq",errorq
-    arret = errorq <  error
-"""
 
-"""
-    print "u",u
-    print "w",w
-    print "theta",theta
-    print "eta", eta
-    print "q",q
-    print "v",v
-    print "lambda",la
-    print "Ps",Ps
-    print "Rh",Rh
-    print "x",x
-"""
 
+ASYNC = False
+__une_seule_exp__("config_initiale.py")
+#__evalue_maj_theta__()
+#ASYNC = True
+#taux_succes = 0.9
+#__evalue_maj_theta__()
 
-"""
-    k +=1
 
-"""