http://www.pythonchallenge.com/pc/def/equality.html
One small letter, surrounded by EXACTLY three big bodyguards on each of its sides.
'''
import os, re
reg = re.compile('[a-z][A-Z]{3}[a-z][A-Z]{3}[a-z]')
reg = re.compile('[^A-Z][A-Z]{3}[a-z][A-Z]{3}[^A-Z]')
latters = ''
with open (os.path.join(os.getcwd(), '0003re.html'), 'r') as html:
for line in html:
a = reg.findall(line)
if a:
for l in a:
latters += l[4]
#print(a)
print(latters)
from tools import writeData
writeData(latters)
#import urllib.request as ur, re
#reg = re.compile('[^A-Z][A-Z]{3}[a-z][A-Z]{3}[^A-Z]')
#url = 'http://www.pythonchallenge.com/pc/def/equality.html'
#data = ur.urlopen(url).read().decode('utf-8')
#a = reg.findall(data)
#latters = ''
#for i in a:
#latters += i[4]
##print(a)
#print(latters)
return data, errors
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-l", "--lfile", dest="learnFile",
help="Learning data (CSV file name)")
parser.add_option("-t", "--tfile", dest="testFile",
help="Testing data (CSV file name)")
parser.add_option("-o", "--ofile", dest="outFile", help="Output file name to store testing data classification")
(options, args) = parser.parse_args()
learnFile = options.learnFile # e. g. vertebral_learn.csv
testFile = options.testFile # e. g. vertebral_test.csv
outFile = options.outFile # e. g. res
learnData = readData(learnFile)
c45= C45(learnData)
tree = c45.constructTree()
tree = c45.pruneTree(tree)
printTree(tree)
testData = readData(testFile)
classifiedLearnData, learnErrors = classifyData(tree, learnData)
classifiedTestData, testErrors = classifyData(tree, testData)
writeData(classifiedTestData, outFile)
print "Learning data error: %d/%d (%f)" % (learnErrors, len(learnData), float(learnErrors)/len(learnData))
print "Testing data error: %d/%d (%f)" % (testErrors, len(testData), float(testErrors)/len(testData))
self.result = {}
#self.find = 'yutileaq'
self.alph = ''
def handle_comment(self, data):
# len = 41:find rare characters in the mess below:
if len(data) != 41:
self.result = {}
for i in data:
if i in self.result:
self.result[i] += 1
else:
self.result[i] = 1
self.alph += i
return self.result, self.alph
parser = exampleHTMLParser()
with open (os.path.join(os.getcwd(), '0002ocr.html'), 'r') as html:
parser.feed(html.read())
result, alph = parser.result, parser.alph
print(result)
print(alph)
name = []
for i in alph:
if result[i] == 1:
print(i, end = '')
name.append(i)
from tools import writeData
writeData(''.join(name))
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
http://www.pythonchallenge.com/pc/ring/bell.html
repeat
switch
'''
from PIL import Image
im = Image.open("0028bell.png")
#print(im)
r, g, b = im.split()
#r.show()
#g.show() #ring ring ring
#b.show()
data = list(g.getdata())
#print(data)
outstand = [abs(data[i] - data[i + 1]) for i in range(0, len(data), 2) if abs(data[i] - data[i + 1]) != 42]
# print(outstand)
s = []
for i in outstand:
s.append(chr(i))
#print(''.join(s))
import tools
tools.writeData(''.join(s))
dest="testFile",
help="Testing data (CSV file name)")
parser.add_option(
"-o",
"--ofile",
dest="outFile",
help="Output file name to store testing data classification")
(options, args) = parser.parse_args()
learnFile = options.learnFile # e. g. vertebral_learn.csv
testFile = options.testFile # e. g. vertebral_test.csv
outFile = options.outFile # e. g. res
learnData = readData(learnFile)
c45 = C45(learnData)
tree = c45.constructTree()
tree = c45.pruneTree(tree)
printTree(tree)
testData = readData(testFile)
classifiedLearnData, learnErrors = classifyData(tree, learnData)
classifiedTestData, testErrors = classifyData(tree, testData)
writeData(classifiedTestData, outFile)
print "Learning data error: %d/%d (%f)" % (
learnErrors, len(learnData), float(learnErrors) / len(learnData))
print "Testing data error: %d/%d (%f)" % (
testErrors, len(testData), float(testErrors) / len(testData))
