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)