From f1fd257592d4fd5c4f7dfb6f816f52a8f59bc267 Mon Sep 17 00:00:00 2001
From: "Nazim@misirlou" <nazim@internet.gestell>
Date: Mon, 7 Oct 2024 00:07:56 +0200
Subject: [PATCH] nzm
---
c-dichteG/a-dichteG.py | 58 ++++++++++++++++++++++++------------------
1 file changed, 33 insertions(+), 25 deletions(-)
diff --git a/c-dichteG/a-dichteG.py b/c-dichteG/a-dichteG.py
index 2ca1c53..fcefd1e 100644
--- a/c-dichteG/a-dichteG.py
+++ b/c-dichteG/a-dichteG.py
@@ -4,6 +4,16 @@ OCTO = 8
N=5
E=2**N
+T=1000 # number of iterations to reach the stationary distrib. on pi
+epsilon=0.05 #step for moving with the gradient
+
+# ---- iteration on phi -------
+NSTEPS_ITER=20
+LAMBDA=0.4 # coefficient for weighting the derivative
+
+# starting point
+phiFuks = [0.,1/3,1/3,2/3,1/3,2/3,2/3,1.]
+
#Randomisation Function which return a vector
def randomization(n):
"""
@@ -58,23 +68,23 @@ def tindex(config,pos):
def Ptrans(phiT,config1,config2):
assert(len(config1)==len(config2))
L=len(config1)
- w12=1.
+ p12=1.
for pos in range(L):
+ assert( config2[pos]==0 or config2[pos]==1 )
itr=tindex(config1,pos)
if config2[pos]==0 :
- w12 *= 1. - phiT[itr]
- elif config2[pos]==1 :
- w12 *= phiT[itr]
- else:
- raise ValueError("binary inputs only")
- m=phiT[itr] if config2[pos]==1 else 1-phiT[itr]
+ p12 *= 1. - phiT[itr] # product
+ else :
+ p12 *= phiT[itr] # product
+ #m=phiT[itr] if config2[pos]==1 else 1-phiT[itr]
#print("pos:%d indx:%d pres:%f m1:%f"%(pos,itr,w12,m))
- return w12
+ return p12
# uses Ptrans
+# can be optimised with shifts
def calculateTransitionMatrix(phiT):
"""
- from phi to transition matrix
+ from phi to transition matrix, 2^n transitions to perform
"""
tMatrix= np.zeros((E, E))
for j in range(E):
@@ -83,7 +93,7 @@ def calculateTransitionMatrix(phiT):
return tMatrix
-# prints for all the configurations pi and calculates Q
+# prints pi for all the configurations and calculates Q
def printDistribStats(PIst):
"""
prints the distribution of pi
@@ -136,20 +146,23 @@ def iterateDistrib(PI,k,transMatrix):
p=0.
for i in range(1,E): # remove 0 because it is zero !
p+= transMatrix[k,i]*PI[i]
- delta=p-PI[k]
+ #delta=p-PI[k]
#print (" %s p=%f p'=%f delta=%f"%(configT[k],PI[k],p,delta))
PI[k]=p
+# procedure for calculating the stationary distrtibution by iterating
+# T times ; fully asynchronous updating
def iterateOnPi(PI, transMatrix):
"""
T iterations, changes PI
"""
for t in range(T):
- # draw between 1 and E-2 (included)
- k=np.random.randint(1,E-1)
- #print("updated:::",k)
+ # draws RANDOMLY A CONFIGURATION between 1 and E-2 (included)
+ k= np.random.randint(1,E-1)
+ # print("updated:::",k)
iterateDistrib(PI,k,transMatrix)
+# stationary ditrib of the proba to go to a 1
def findStationaryDistrib(phi):
"""
finds the stationary distribution from a random init distribution
@@ -190,14 +203,10 @@ def evaluateGradient(phiZ):
############
print("----- starting test ------")
-T=5000 # number of iterations to reach the stationary distrib. on pi
-epsilon=0.05
-phiFuks = [0,1/3,1/3,2/3,1/3,2/3,2/3,1.]
#phiG=randomization(OCTO)
#print("PHI:%s"%phiG)
-
#fills the config array
configT=[]
for i in range(E):
@@ -205,22 +214,21 @@ for i in range(E):
# ---- for next phi -------
-def fadd(x,y):
- coef= 1;#np.random.random() # [0,1] random weight for each coordinate
+def fWeightedAddition(x,y):
+ coef= np.random.random() # [0,1] random weight for each coordinate
val= x + coef*LAMBDA*y
if val < 0.: return 0.
if val > 1.: return 1.
return val
-# ---- iteration on phi -------
-NSTEPS_ITER=20
-LAMBDA=0.4 # coefficient for weighting the derivative
+
phiIter = phiFuks.copy() # deep copy
+#phiIter = randomization(8)
for stepG in range(NSTEPS_ITER):
print("----- step %d -----"%stepG)
- grad= evaluateGradient(phiIter)
- phiNew= list( map(fadd,phiIter,grad) )
+ grad= evaluateGradient(phiIter) #vector with eight dimensions
+ phiNew= list( map(fWeightedAddition,phiIter,grad) ) # "moving" according to grad
phiIter= phiNew.copy()
#T=+2000
LAMBDA*= 0.98
--
GitLab