#!/usr/bin/python3
import numpy as np
import os
import sys
import importlib
sys.path.append(os.environ['UASR_HOME'] + '-py')
import ipl
import icfg
import idnn
if len(sys.argv) < 2: raise ValueError("Usage: " + sys.argv[0] + " CFG {PAR}")
icfg.Cfg(sys.argv[1])
dlog = icfg.getdir('log')
dn = os.path.join(dlog, 'dnnrnd')
res = []
for fn in os.listdir(dn):
if fn[-8:] != '_arg.npy': continue
fna = os.path.join(dn, fn)
res.append({'fna': fna})
fns = fna[:-8] + '_stat.npy'
if os.path.exists(fns): res[-1]['fns'] = fns
fnx = fna[:-8] + '_stat_allsen.npy'
if os.path.exists(fnx): res[-1]['fnx'] = fnx
res[-1]['fnp'] = fna[:-8] + '_par.npy'
print(kwargs)
r=dnntest(**kwargs)
if r is None: break
xpar=idnn._netstat(r,r['idim'],prt=False)
if np.array(r['stat'])[:,2].max()<0.5: print("MAX-ACC<0.5"); break
stat.append(np.array(r['stat']))
print('/dnnrnd_'+str(int(time.time())))
np.save(dlog+'/dnnrnd/dnnrnd_'+str(int(time.time()))+'_arg.npy',kwargs)
np.save(dlog+'/dnnrnd/dnnrnd_'+str(int(time.time()))+'_par.npy',xpar)
if len(stat)==3: np.save(dlog+'/dnnrnd/dnnrnd_'+str(int(time.time()))+'_stat.npy',stat)
def dnnrndloop():
while not os.path.exists('stop'): dnnrnd()
if len(sys.argv)<2: raise ValueError("Usage: "+sys.argv[0]+" CFG [-n]")
icfg.Cfg(*sys.argv[1:])
ftrn=icfg.readflst('train')
ftst=icfg.readflst('test')
dmod=icfg.getdir('model')
dlog=icfg.getdir('log')
sen=getsensors()
regression=icfg.get('trn.regression')==True
senuse=sen
feause=['pfa','sfa','sig']
clsuse=['hmm','svm','cnnb','cnn','dnn']
if not icfg.get('senuse') is None: senuse=icfg.get('senuse').split(',')
if not icfg.get('feause') is None: feause=icfg.get('feause').split(',')
if not icfg.get('clsuse') is None: clsuse=icfg.get('clsuse').split(',')