import loadGroupData
import optGroupData
import disGroupData
from lmfit import report_fit
traindays = 20
begin = 0
end = 135621
mode = 0 #[0, 1, 2, 3, 4, 5] for [multi, single, SIRd, SIR, SI, SpikeM]
name = ['multi', 'single', 'SIRd', 'SIR', 'SI']
fw = open('../../' + name[mode] + '_res/params.csv', 'w')
for i in range(begin, end):
rawdata = loadGroupData.load_data(i)
result = optGroupData.optimize(rawdata, mode)
report_fit(result.params)
fw.write(str(i) + ',')
fw.write(str(result.params))
fw.write('\n')
path = '../../' + name[mode] + '/'
#disGroupData.display_bi(rawdata,result,result_test,path+str(i)+'.png')
p = disGroupData.display(rawdata, result, path + str(i) + '.png', mode)
if peaks.has_key(p):
peaks[p] += 1
else:
peaks[p] = 1
print 'No.' + str(i) + ' items finished.(' + name[i] + ')'
fw.close()
print peaks
import loadGroupData
import optGroupData
import disGroupData
from lmfit import report_fit
traindays = 20
begin = 0
end = 135621
mode = 0 #[0, 1, 2, 3, 4, 5] for [multi, single, SIRd, SIR, SI, SpikeM]
name = ['multi', 'single', 'SIRd', 'SIR', 'SI']
fw=open('../../' + name[mode] + '_res/params.csv','w')
for i in range(begin, end):
rawdata=loadGroupData.load_data(i)
result=optGroupData.optimize(rawdata, mode)
report_fit(result.params)
fw.write(str(i)+',')
fw.write(str(result.params))
fw.write('\n')
path='../../'+name[mode]+'/'
#disGroupData.display_bi(rawdata,result,result_test,path+str(i)+'.png')
p = disGroupData.display(rawdata,result,path+str(i)+'.png',mode)
if peaks.has_key(p):
peaks[p] += 1
else:
peaks[p] = 1
print 'No.' + str(i) + ' items finished.('+name[i]+')'
fw.close()
print peaks
data.append(line)
csvfile.close()
csvfile = file('../../badID.csv', 'rb')
reader = csv.reader(csvfile)
baddata = list()
for line in reader:
baddata.append(line)
csvfile.close()
fw = open('../multi_predict/params.csv','w')
for line in data:
flag = False
for per in baddata:
if line[0] == per[0]:
flag = True
break
if flag:
continue
index = int(line[0])
rawdata = loadGroupData.load_data(index)
front = list()
for i in range(4):
front.append(rawdata[i][:-10])
result=optGroupData.optimize(front)
report_fit(result.params)
fw.write(str(result.params))
fw.write('\n')
path='../multi_predict/'
p = disGroupData.display(rawdata,result,path+line[0]+'.png')
fw.close()
print 'Finished.'
data.append(line)
csvfile.close()
csvfile = file('../../badID.csv', 'rb')
reader = csv.reader(csvfile)
baddata = list()
for line in reader:
baddata.append(line)
csvfile.close()
fw = open('../multi_predict/params.csv', 'w')
for line in data:
flag = False
for per in baddata:
if line[0] == per[0]:
flag = True
break
if flag:
continue
index = int(line[0])
rawdata = loadGroupData.load_data(index)
front = list()
for i in range(4):
front.append(rawdata[i][:-10])
result = optGroupData.optimize(front)
report_fit(result.params)
fw.write(str(result.params))
fw.write('\n')
path = '../multi_predict/'
p = disGroupData.display(rawdata, result, path + line[0] + '.png')
fw.close()
print 'Finished.'