if os.path.isdir(args.data):
Xtr, Ytr, _, _, Xte, Yte, Yscaler = esnet.load_from_dir(args.data)
elif dataType=='SantaFe' or dataType=='Sunspots' or dataType=='Hongik' \
or dataType=='GEFC' or dataType=='Mackey' or dataType=='SP500' \
or dataType=='Rainfall' or dataType=='Temperature' \
or dataType == 'MinTempMel' or dataType == 'SunSpotsZu'\
or dataType == 'TempAlbuquerque' or dataType == 'TempDenver' or dataType == 'TempLasVegas' \
or dataType == 'TempLosAngeles' or dataType == 'TempPhoenix' or dataType == 'TempPortland' \
or dataType == 'TempSanDiego' or dataType == 'TempSanFrancisco' or dataType == 'TempSeattle' \
or dataType == 'TempVancouver' \
or dataType == 'eleGB2015_7_12' or dataType == 'eleDE2015_7_12' or dataType == 'eleFR2015_7_12'\
or dataType == 'Electric':
#Xtr, Ytr, _, _, Xte, Yte, Yscaler = esnet.generate_datasets_santafe(args.data)
X, Y = esnet.load_from_text(args.data)
# Construct training/test sets
Xtr, Ytr, _, _, Xte, Yte, Yscaler = esnet.generate_datasets(X, Y)
Xtr, Xte = esnet.reconstruct_input_1d([Xtr, Xte], reconstructconfig)
Ytr, Yte = esnet.reconstruct_output_1d([Ytr, Yte], reconstructconfig)
elif dataType == 'GEFC_temp' or dataType == 'HenonMap':
X, Y = esnet.load_from_text(args.data)
# Construct training/test sets
Xtr, Ytr, _, _, Xte, Yte, Yscaler = esnet.generate_datasets(X, Y)
# Reconstruct
Xtr, Xte = esnet.reconstruct_input_2d([Xtr, Xte], reconstructconfig)
def main():
averages = []
predictions_error = []
predictions = []
reals = []
startPoint = args.count
#For the first few predictions, use the last error as prediction
dataPath = '/home/minh/PycharmProjects/Ensemble/PythonESN/data/edgar_historical'
count = 0
"""with open(dataPath,'r') as f:
for line in f:
if count>0:
data = line.split(',')
total = 0
for i in range(5):
total += float(data[i])
averages.append(total/5)
reals.append(float(data[6]))
count+=1"""
"""count = 0
with open(dataPath,'r') as f:
for line in f:
if count>0:
if count>(startPoint+2):
break
else:
data = line.split(',')
total = 0
for i in range(5):
total += float(data[i])
predictions.append(float(data[6])-total/5)
count+=1
print('predictions:',predictions)"""
# Run in parallel and store result in a numpy array
X, Y = esnet.load_from_text(args.data)
Xtr, Ytr, _, _, Xte, Yte, Yscaler = esnet.generate_datasets(X[:startPoint], Y[:startPoint])
Yhat, error = esnet.run_from_config(Xtr, Ytr, Xte, Yte, config, Yscaler)
Yhat = np.ceil(Yscaler.inverse_transform(Yhat))
predictions_error.append(Yhat[len(Yhat) - 1][0])
"""for i in range(startPoint,len(X)):
Xtr, Ytr, _, _, Xte, Yte, Yscaler = esnet.generate_datasets(X[:i], Y[:i])
if i<800:
config['n_drop'] = int(i/8)
else:
config['n_drop'] = 100
Yhat, error = esnet.run_from_config(Xtr, Ytr, Xte, Yte, config)
Yhat = np.ceil(Yscaler.inverse_transform(Yhat))
#print('predictions:',Yhat)
#print('error:',error)
predictions_error.append(Yhat[len(Yhat)-1][0])
count+=1
if count%100==0:
print('predictions made:',count)"""
curPath = os.getcwd().split('/')
writePath = ''
for i in range(len(curPath)):
writePath += curPath[i] + '/'
configs = args.esnconfig.split('/')
writePath += 'predictions/predictions_' + configs[-1] + '_' + str(args.times)
"""with open(writePath, 'a') as f:
f.write(str(np.ceil(Yhat[len(Yhat) - 1][0]))+'\n')"""
with open (writePath, 'w') as f:
for i in range(len(Yhat)):
f.writelines(str(np.ceil(Yhat[i][0])) + '\n')