def funcNbofAttr(P, G):
endDir = 'G_%.3f_P_%.5f.npy' %(G,P)
try:
pattsx = data2array(dir_prix + '/patterns_' + endDir, mmap_mode="r+")
pattsA = data2array(dir_priA + '/patterns_' + endDir, mmap_mode="r+")
return len(pattsx)
except:
try:
pattsx = data2array(dir_prix + '/allPatt_' + endDir)
pattsA = data2array(dir_priA + '/allPatt_' + endDir)
except:
N = data2array(dco).shape[0]
pattsx = zeros((mmax,N))
pattsA = zeros((mmax,N))
conn = {'connAd': dco,
'normType': '1'}
noise = {'colors': None}
model = {'model': 'HopfieldBasedStatic',
'threshold': 'local',
'tauT': 0,
'P': P,
'G': G}
out = []
other = {'init': 'rand',
'dens': rand(),} #p2,!!!!!!!!!!!!!! RAND
for d in range(mmax):
eva = main.evaCure(evaCon=conn, evaNoi=noise, evaMod=model, out=out, **other)
eva.toEquilibrium()
pattsx[d] = eva.evaMod.x.copy()
pattsA[d] = eva.evaMod.A.copy()
array2data(pattsx, dir_prix + '/allPatt_' + endDir)
array2data(pattsA, dir_priA + '/allPatt_' + endDir)
patts = pattsx # !!!!!!!!!
S = sortBy(patts.mean(1) - patts.mean(), inverse=1)[0]
C1, freq = preClustering(patts[S], sim_coef=sim_coef, sim_func=similarity_Euclidean)
C2, freq = preClustering(patts[S][C1], freq=freq, sim_coef=sim_coef, sim_func=fPearsonCorrelation)
SC, freq = sortBy(freq, inverse=1)
array2data(pattsx[S][C1][C2][SC], dir_prix + '/patterns_' + endDir)
array2data(pattsA[S][C1][C2][SC], dir_priA + '/patterns_' + endDir)
array2data(freq, dir_priT + '/tendances_' + endDir)
os.system('rm ' + dir_prix + '/allPatt_' + endDir)
os.system('rm ' + dir_priA + '/allPatt_' + endDir)
return len(pattsx[S][C1][C2][SC])
T0 = 20000 # 0.1 ms
TF = 80000 # 0.1 ms
sx = 0.2
sT = 0.2
x, A = [], []
############################################################################################'
# **Loop**
tic()
for iii in range(100):
# Init
other.update({'dens': rand()})
eva = main.evaCure(evaCon=conn,
evaNoi=noise,
evaMod=model,
out=out,
**other)
#Run
for i in range(T0):
eva.update()
eva.evaNoi.updateNoise(stdD_x=sx, stdD_T=sT)
for i in range(TF):
eva.update()
#Save
x.append(array(eva.out['x']))
A.append(array(eva.out['A']).mean(1))
del eva
