def load(city):
TicToc = tic_toc.TicTocGenerator()
tic_toc.tic(TicToc)
junctions = []
subjun = []
for line in enumerate(codecs.open("{}_net.txt".format(city), "r",
"utf-8")):
junctions.append(Junction(line[1]))
subjun.append(len(junctions[-1].subjunctions))
city_array = np.array(subjun)
tic_toc.toc(TicToc)
return junctions, city_array
def maneuver(city_array, city):
step = 1
checked = 0
y_array = []
x_array = []
while checked < city_array.size:
TicToc = tic_toc.TicTocGenerator()
tic_toc.tic(TicToc)
x_array.append(step)
y_array.append(0)
y_array[-1] += np.extract(city_array == step, city_array).size
checked += y_array[-1]
tic_toc.toc(TicToc)
print(step, checked, city_array.size)
step += 1
pickle.dump(x_array, open('histogram_a/x_array_{}.p'.format(city), 'wb'))
pickle.dump(y_array, open('histogram_a/y_array_{}.p'.format(city), 'wb'))
#!/usr/bin/python
# -*- coding:utf-8 -*-
import requests
from bs4 import BeautifulSoup
import pandas as pd
from collections import defaultdict
from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver.common.keys import Keys
import time
from tic_toc import tic, toc
tic()
trainfle = './data/train3.csv'
f1 = open(trainfle, 'w')
train_urls = ['https://music.douban.com/top250']
#
#
#
sel = webdriver.Chrome()
loginurl = 'https://music.douban.com/top250'
#open the login in page
sel.get(loginurl)
s = requests.session()
s.keep_alive = False
head = {
'User-Agent':
X_train, X_test, y_train, y_test = train_test_split(df.drop(['Survived'],
axis=1),
df['Survived'],
test_size=.1)
#%% iris binary
df = pd.read_csv('data/iris_binary_prepd.csv')
#%% bike buyer
df = pd.read_csv('data/bike_buyer_prepd.csv')
df = df.set_index('ID')
#%%
X_train, X_test, y_train, y_test = train_test_split(df.drop(['label'], axis=1),
df['label'],
test_size=.1)
#%% test the training module
tic_toc.tic()
size = len(X_train)
# titanic does well with norm 1
# if validation is an integer cross validation is performed and perc_cluster is set to 0
best_part = gen_part.train(df.drop(['Survived'], axis=1),
df['Survived'],
500,
15,
prob_mutate=.05,
mutate_strength=.3,
survival_rate=.1,
alien_rate=.1,
min_cubes=20,
min_rows_in_cube=20,
metric='acc',
validation=5,