from provider import Provider
from feature_selection import Selector
import numpy as np
import neurolab as nl
from sklearn.metrics import mean_squared_error
from math import sqrt
import matplotlib.pyplot as plt
if __name__ == '__main__':
provider = Selector()
net = nl.net.newff(
provider.getDataRanges(),
[provider.getInputCount() * 2,
provider.getInputCount() * 2, 1])
# net.trainf = nl.train.train_gd
input = []
target = []
mult = provider.provider.multiplier
for d in provider.getLearnData():
input.append(d[0])
target.append([d[1][0] / mult])
err = net.train(input, target, epochs=1000, show=10,
goal=0.001) #, lr=0.000001)
results = net.sim(input)
w = open('output1.txt', 'w')
for i, line in enumerate(results):
w.write('%f;%f\n' % (target[i][0] * mult, line[0] * mult))
w.close()
input = []
from provider import Provider
from feature_selection import Selector
import numpy as np
import neurolab as nl
from sklearn.metrics import mean_squared_error
from math import sqrt
if __name__ == '__main__':
provider = Selector()
net = nl.net.newff(provider.getDataRanges(), [provider.getInputCount()*2, provider.getInputCount()*2, 1])
# net.trainf = nl.train.train_gd
input = []
target = []
mult = provider.provider.multiplier
for d in provider.getLearnData():
input.append(d[0])
target.append([d[1][0]/mult])
err = net.train(input, target, epochs=1000, show=10, goal=0.001)#, lr=0.000001)
results = net.sim(input)
w = open('output1.txt', 'w')
for i, line in enumerate(results):
w.write('%f;%f\n' % (target[i][0]*mult, line[0]*mult))
w.close()
input = []
target = []
for d in provider.getTestData():
input.append(d[0])
target.append([d[1][0]/mult])
results = net.sim(input)