def main():
inputs = ReadCSV('./data/input.csv')
outputs = ReadCSV('./data/output.csv')
test_set = test.keys()
train_set = []
for k in inputs.keys():
if k not in test_set:
train_set.append(k)
print "Number of training samples", len(train_set)
print "Number of testing samples", len(test_set)
net = buildNetwork(178, 6, 5)
ds=SupervisedDataSet(178,5)
for id in train_set:
ds.addSample(inputs[id],outputs[id])
trainer = BackpropTrainer(net, ds, learningrate=0.001, momentum = 0.001)
trainer.trainUntilConvergence(maxEpochs=1000, validationProportion = 0.5)
for id in test_set:
predicted = net.activate(inputs[id])
actual = outputs[id]
print '-----------------------------'
print test[id]
print '-----------------------------'
print 'Trait\t\tPredicted\tActual\tError'
for i in range(0,5):
error = abs(predicted[i] - actual[i])*100/4.0
print traits[i], '\t', predicted[i], '\t', actual[i], '\t', error,"%"
from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets import SupervisedDataSet
from pybrain.structure import TanhLayer
from pybrain.supervised.trainers import BackpropTrainer
import pickle
import sys
sys.path.append('./lib')
from ReadCSV import ReadCSV
new_input = ReadCSV("newinput.csv")
net = pickle.load(open("neuralNet.sl", "r"))
input = new_input[new_input.keys()[0]]
traits = net.activate(input)
text = ""
for trait in traits:
text = text + str(trait) + " "
print text
from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets import SupervisedDataSet
from pybrain.structure import TanhLayer
from pybrain.supervised.trainers import BackpropTrainer
import pickle
import sys
sys.path.append('./lib')
from ReadCSV import ReadCSV
new_input = ReadCSV("newinput.csv")
net = pickle.load(open("neuralNet.sl", "r"))
input = new_input[new_input.keys()[0]]
traits = net.activate(input)
text = ""
for trait in traits:
text = text + str(trait) + " "
print text
