def repeat(second=1.0):
global current, is_seoul_bus, route_name, routeID
response = parse(is_seoul_bus, get_response(is_seoul_bus, routeID))
if current == {}:
compare(route_name, response, current)
current = response
else:
compare(route_name, response, current)
current = response
#print('Repeating...')
threading.Timer(second, repeat, [second]).start()
def get_url_info(saved_id):
conn = MySQLdb.connect(host=mysql_info['host'],
user=mysql_info['user'],
passwd=mysql_info['passwd'],
db=mysql_info['db'],
port=int(mysql_info['port']),
charset='utf8')
cur = conn.cursor()
if str(saved_id) != '':
sql1 = 'SELECT count(1) FROM `t_zzyq_news` where id>%s order by id asc' % saved_id
sql2 = 'SELECT id,content,url,title FROM `t_zzyq_news` where id>%s order by id asc' % saved_id
else:
sql1 = 'SELECT count(1) FROM `t_zzyq_news` where id order by id asc'
sql2 = 'SELECT id,content,url,title FROM `t_zzyq_news` where id order by id asc'
cur.execute(sql1)
count = cur.fetchone()
print(count[0])
cur.execute(sql2)
result = cur.fetchall()
conn.cursor().close()
conn.close()
for i in range(0, count[0]):
id = result[i][0]
content = result[i][1]
url = result[i][2]
title = result[i][3]
# save_url(url)
split_to_word(content, id, db_word_path)
html_content = ''
n = 0
while html_content == '':
html_content = get_clean_content(url)
if html_content == '':
time.sleep(2)
print('%s的html_content为空，稍后重试' % str(id))
n += 1
if n == 3:
break
split_to_word(html_content, id, html_word_path)
first_path = db_word_path + '\\' + str(id) + '.txt'
second_path = html_word_path + '\\' + str(id) + '.txt'
compare(id, title, first_path, second_path)
path = dirs + '\\id.txt'
a = open(path, 'w')
a.truncate()
a.write(str(id))
a.close()
def detect(a):
#a = "Counter-Strike is a first-person shooter in which players join either the terrorist team, the counter-terrorist team, or become spectators. Each team attempts to complete their mission objective and/or eliminate the opposing team. Each round starts with the two teams spawning simultaneously.A player can choose to play as one of eight different default character models (four for each side, although Counter-Strike: Condition Zero added two extra models, bringing the total to ten). Players are generally given a few seconds before the round begins (known as freeze time) to prepare and buy equipment, during which they cannot attack or move (one notable exception is that a player may receive damage during freeze time. This happens when a map is changed to spawn players at a certain height above the ground, thus causing fall damage to the player"
#comp = "Counter-Strike is a first-person shooter in which players join either the terrorist team, the counter-terrorist team, or become spectators. Each team attempts to complete their mission objective and/or eliminate the opposing team. Each round starts with the two teams spawning simultaneously.A player can choose to play as one of eight different default character models (four for each side, although Counter-Strike: Condition Zero added two extra models, bringing the total to ten). Players are generally given a few seconds before the round begins (known as freeze time) to prepare and buy equipment, during which they cannot attack or move (one notable exception is that a player may receive damage during freeze time. This happens when a map is changed to spawn players at a certain height above the ground, thus causing fall damage to the player."
a = remove_punctuation(a)
comp = a
a = chunks(a, 250)
#comp = remove_punctuation(comp)
comp = remv_extra(comp)
#print a
#print comp
#print len(comp)
sites, site_list = begin(a)
lev_list = []
i = 0
#print len(sites)
#print len(site_list)
for item in site_list:
item = remove_punctuation(item)
item = remv_extra(item)
#print item
lev = compare(item, comp)
lev_list.append(lev)
#print sites[i]
#print lev_list[i]
print sites[i], lev_list[i]
i = i + 1
return sites, lev_list
def detect(a):
#a = "Counter-Strike is a first-person shooter in which players join either the terrorist team, the counter-terrorist team, or become spectators. Each team attempts to complete their mission objective and/or eliminate the opposing team. Each round starts with the two teams spawning simultaneously.A player can choose to play as one of eight different default character models (four for each side, although Counter-Strike: Condition Zero added two extra models, bringing the total to ten). Players are generally given a few seconds before the round begins (known as freeze time) to prepare and buy equipment, during which they cannot attack or move (one notable exception is that a player may receive damage during freeze time. This happens when a map is changed to spawn players at a certain height above the ground, thus causing fall damage to the player"
#comp = "Counter-Strike is a first-person shooter in which players join either the terrorist team, the counter-terrorist team, or become spectators. Each team attempts to complete their mission objective and/or eliminate the opposing team. Each round starts with the two teams spawning simultaneously.A player can choose to play as one of eight different default character models (four for each side, although Counter-Strike: Condition Zero added two extra models, bringing the total to ten). Players are generally given a few seconds before the round begins (known as freeze time) to prepare and buy equipment, during which they cannot attack or move (one notable exception is that a player may receive damage during freeze time. This happens when a map is changed to spawn players at a certain height above the ground, thus causing fall damage to the player."
a = remove_punctuation(a)
comp = a
a = chunks(a,250)
#comp = remove_punctuation(comp)
comp= remv_extra(comp)
#print a
#print comp
#print len(comp)
sites,site_list = begin(a)
lev_list = []
i=0
#print len(sites)
#print len(site_list)
for item in site_list:
item = remove_punctuation(item)
item = remv_extra(item)
#print item
lev = compare(item, comp)
lev_list.append(lev)
#print sites[i]
#print lev_list[i]
print sites[i], lev_list[i]
i = i+1
return sites,lev_list
def compute(self):
for itr in range(len(self.clusters)-self.target_size):
score = compare(self.clusters)
self.clusters[score[0]].add(self.clusters.pop(score[1]))
self.text.insert('1.0', " classified {0} time(s) now there are {1} clusters\n".format(itr+1,len(self.clusters)))
self.root.update()
self.text.insert('1.0', " classification finished\n")
def histComic(comicURL):
webObject = fetchWeb(comicURL,False)
cache = Cache()
cacheObject = cache.fetchCache(comicURL,None)
if not compare(webObject, cacheObject):
cache.storeCache(webObject)
Scheduler.histComicNotification += 1
notification("Hist: " + comicURL)
return None
def resolve(theorem, outer, clause, loc):
if loc == 1:
opp = 2
else:
opp = 1
for i in clause[loc]:
find = i[:i.find('(')]
for j in theorem[outer][opp]:
new = j[:j.find('(')]
curr = theorem[outer][opp].index(j)
if find == new:
compare(clause, theorem[outer])
subst = unify(parse_term(i), parse_term(j), {})
if subst is not None:
found = substitute(theorem, outer, subst, i, clause, loc,
opp, curr)
return found
return False
def newComic(comicURLs):
lastChange = None
for url in comicURLs:
cache = Cache()
webObject = fetchWeb(url,False)
cacheObject = cache.fetchCache(url,None)
if not compare(webObject, cacheObject):
cache.storeCache(webObject)
lastChange = url
if lastChange:
Scheduler.newComicNotification += 1
notification("New: " + lastChange)
return None
def main():
best=10000000
bestmatch = ""
sample, sampleDuration = loadmusic(argv[1])
'''plt.title("Sample Peaks")
plt.plot(*zip(*sample))
plt.show()'''
d = {}
for m in music.find(): # for each music in the collection
k, s = compare(sample, m["peaks"]); # gets the correspondency value
l = [abs(i-j) for i,j in s.items()]
std = numpy.std(l) # calculates standard deviation between differences of time instants
print m["music"], k, numpy.std(l)
d[std] = s
if std < best:
best = std
bestmatch = m["music"]
c = correlation(d[min(d)], False)
#print "correlation: ", c
print bestmatch
srtfile = "/home/michel/data/db/" + bestmatch.split('.')[0] + ".srt"
readlyrics(srtfile, argv[2])
bmatch = music.find_one({"music": bestmatch})
maxpeak = max([i for i,j in bmatch["peaks"] ])
print "maxpeak: ", maxpeak
duration = bmatch["duration"] * 1000
print "duration: ", duration
x = (c * duration) / maxpeak + sampleDuration
print x
'''
def _compare(name,segs,refs):
compare(refs,ic100_ref,name,segs)
_compare('mean_t',mean_t,refs)
_compare('rc_t',rc_t,refs)
#_compare('watershed:direct',water_direct,refs)
#_compare('watershed:gradient',water_gradient,refs)
#_compare('watershed:direct_rc',water_direct_rc,refs)
#_compare('watershed:gradient_rc',water_gradient_rc,refs)
_compare('watershed:direct_mean',water_direct_mean,refs)
_compare('watershed:gradient_mean',water_gradient_mean,refs)
#_compare('watershed:direct_raw',water_direct_raw,refs)
#_compare('watershed:gradient_raw',water_gradient_raw,refs)
#_compare('watershed:direct:full',water_direct,refs_full)
#_compare('watershed:gradient:full',water_gradient,refs_full)
#_compare('watershed:direct_raw:full',water_direct_raw,refs_full)
#_compare('watershed:gradient_raw:full',water_gradient_raw,refs_full)
#_compare('active_masks',active_masks,refs)
_compare('active_masks:filtered',active_masks_filtered,refs)
#_compare('active_masks2',active_masks2,refs)
#_compare('roysam',roysams,refs)
_compare('roysam_mean',roysams_mean,refs)
#_compare('roysams_mean_filter',roysams_mean_filtered,refs)
_compare('roysams_mean_filter_no_AS',roysams_mean_filtered[5:],refs[5:])
aabid_refs = ic100_ref[:5] + gnf_ref[:5]
aabids = [Task(load_aabid,'ic100',i) for i in xrange(5)] +\
[Task(load_aabid,'gnf',i) for i in xrange(5)]
compare(aabid_refs,ic100_ref[:5],'AS',aabids)
# vim: set ts=4 sts=4 sw=4 expandtab smartindent:
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from base import *
from compare import *
infile = open("C:\Users\wearable\Trevor.txt", "r")
#infile2 = open("C:\Users\wearable\IT.txt", "r")
oxy = []
oxy_1 = []
test1 = base(infile)
test1.clear()
oxy = test1.start()
oxy_1 = test1.filter(0.15, 150.0, 1000.0, 0.0)
test2 = compare(oxy, oxy_1, 14)
test2.xavier()
test2.clear()
plt.plot(oxy, 'r')
plt.plot(oxy_1, 'b')
plt.show()
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from base import *
from compare import *
infile = open("C:\Users\wearable\it2.txt", "r")
#infile2 = open("C:\Users\wearable\IT.txt", "r")
oxy = []
oxy_1 = []
test1 = base(infile)
test1.clear()
oxy = test1.start()
oxy_1 = test1.filter(0.15, 150.0, 1000.0, 0.0)
test2 = compare(oxy, oxy_1, 14)
test2.xavier()
test2.clear()
plt.plot(oxy, 'r')
plt.plot(oxy_1, 'b')
plt.show()
# -*- coding: utf-8 -*- """ Created on Wed Feb 11 19:06:27 2015 @author: user """ from BCD_GEM import * from compare import * from generate_gaussians import * N = 10000 P = 20 K = 5 X, Y, phi1, rho1, pi1 = generate_gaussian_mix(N, P, K) phi2, rho2, pi2 = BCD_GEM(X, Y, K, myLambda) print phi2 print rho2 print pi2 compare(phi1, phi2, pi1, pi2, rho1, rho2)
def test_compare_short(): nose.tools.assert_equal([8, 12], compare(triangle[1],triangle[2])) nose.tools.assert_equal([6], compare(triangle[0],triangle[1]))
import PIL
from PIL import Image
import barcodeReader
import read_text
import compare
img = Image.open("IMG_9000.JPG")
bgr = (8, 70, 208)
barcode_values = barcodeReader(img, bgr)
print(barcode_values)
text_values = read_text(path, full_text)
print(text_values)
validation = compare(text_values, barcode_values)
def _compare(name,segs,refs):
compare(refs,ic100_ref,name,segs)
#_compare('otsu',otsu,refs)
#_compare('mean_t',mean_t,refs)
#_compare('rc_t',rc_t,refs)
#_compare('watershed:direct',water_direct,refs)
#_compare('watershed:gradient',water_gradient,refs)
#_compare('watershed:direct_rc',water_direct_rc,refs)
#_compare('watershed:gradient_rc',water_gradient_rc,refs)
#_compare('watershed:direct_mean',water_direct_mean,refs)
#_compare('watershed:gradient_mean',water_gradient_mean,refs)
#_compare('watershed:direct_raw',water_direct_raw,refs)
#_compare('watershed:gradient_raw',water_gradient_raw,refs)
#_compare('watershed:direct:full',water_direct,refs_full)
#_compare('watershed:gradient:full',water_gradient,refs_full)
#_compare('watershed:direct_raw:full',water_direct_raw,refs_full)
#_compare('watershed:gradient_raw:full',water_gradient_raw,refs_full)
#_compare('active_masks',active_masks,refs)
#_compare('active_masks:filtered',active_masks_filtered,refs)
#_compare('active_masks2',active_masks2,refs)
#_compare('roysam',roysams,refs)
#_compare('roysam_mean',roysams_mean,refs)
#_compare('roysams_mean_filter',roysams_mean_filtered,refs)
#_compare('roysams_mean_filter_no_AS',roysams_mean_filtered[5:],refs[5:])
aabid_refs = ic100_ref[:5] + gnf_ref[:5]
aabids = [Task(load_aabid,'ic100',i) for i in xrange(5)] +\
[Task(load_aabid,'gnf',i) for i in xrange(5)]
compare(aabid_refs,ic100_ref[:5],'AS',aabids)
from PIL import Image
(from *) import compare
im = Image.open("chemin de l'image")
# isolation des couches de couleur de l'image
im1 = im.split()[0] # couche rouge
im2 = im.split()[1] # couche verte
im3 = im.split()[2] # couche bleu
im4 = im.convert("L") # conversion RGBA -> niveaux de gris
compare(im1, im2, im3, im4)
from compare import *
my_language = language("patterns/patterns_en.txt", "entities/entities_en.txt")
print(
compare(input("> "), [["Hello", "Hi", "Hey"], "bot"],
entities=my_language.entities))
