import json
import random
import numpy as np
from src.utils2 import c_ex as c, get_path
import src.dataloaders as d
from src.logistic import fit_logistic_regression
path = get_path(__file__) + '/..'
D = d.trainingset_extended()
Dt = d.testset_extended()
cols = c('sde5', 'v11', 'e9')
a = range(D.shape[0])
random.shuffle(a)
X = D[:, cols]
X = X[a[:320000], :]
y = D[a[:320000], c('isalert')]
y = y.astype(int)^1
Xt = D[:, cols]
Xt = Xt[a[320000:], :]
yt = D[a[320000:], c('isalert')]
yt = yt.astype(int)^1
num_tests = 1
learningrate = 0.04
max_epochs = 40
continue_epochs = 6
training_size = 100000
feature_set = 'forward'
if feature_set == 'winner':
features = ['sde5', 'v11', 'e9']
elif feature_set == 'forward':
features = ['sde1', 'v11', 'e9']
else:
raise Exception('Unknown feature set')
if training_size == 'all':
D = d.trainingset_extended()
training_size = int(D.shape[0])
else:
D = get_random_subset(d.trainingset_extended(), training_size)
T = d.testset_extended()
cols = c(*features)
Xt = T[:, cols]
yt = T[:, c.isalert]
bins = get_bins(T.shape[0], num_bins)
neural_network = train_network(D[:, cols], D[:, c.isalert],
hidden_units=hidden_units, learningrate=learningrate,
