import json
import random
import numpy as np
from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets.classification import ClassificationDataSet
from pybrain.supervised.trainers import BackpropTrainer
from pybrain.structure.modules import SigmoidLayer
import src.dataloaders as d
from src.utils2 import c
D = d.testset()
a = range(D.shape[0])
random.shuffle(a)
num_train_rows = 10000
num_test_rows = 5000
tr_rows = a[:num_train_rows]
ts_rows = a[num_train_rows : (num_train_rows + num_test_rows)]
features = ["V11", "sdE5", "E9"]
X = D[tr_rows, c(*features)]
Y = D[tr_rows, c("IsAlert")]
Xt = D[ts_rows, c(*features)]
Yt = D[ts_rows, c("IsAlert")]
from __future__ import division
import numpy as np
from src.dataloaders import testset
from src.utils2 import c
T = testset()
length = T.shape[0]
fails_e9 = np.abs(T[:,c('E9')]-T[:,c('IsAlert')]).sum()
fails_v5 = np.abs(T[:,c('V5')]-T[:,c('IsAlert')]).sum()
print "Percent classified by E9: %.2f" % ((length - fails_e9)/length,)
print "Percent classified by V5: %.2f" % ((length - fails_v5)/length,)
import numpy as np
import src.dataloaders as d
from src.utils2 import create_extended_dataset_window
TrnD_ex = create_extended_dataset_window(d.trainingset())
TstD_ex = create_extended_dataset_window(d.testset())
np.save('data/trainingset_extended_window_30.npy', TrnD_ex)
np.save('data/testset_extended_window_30.npy', TstD_ex)
