def possible(word):
temp = pattern.match(word)
try:
copy = temp[:]
for i, letter in enumerate(word):
if letter.islower() == True:
for j in temp:
if j[i] == letter:
continue
else:
try:
copy.remove(j)
except ValueError:
pass
return copy
except TypeError:
# print "TypeError"
return [word]
import extract
import context
makectx = context.ctx
currctx = context.curr
from util import Const, Var
class Magic(object):
"""
Give query more magics
"""
def __getattr__(self, name):
return Var(name)
def __getitem__(self, value):
return Q + Const(value)
def __call__(self, func, *args, **kwargs):
return lambda value: func(value, *args, **kwargs)
I = Magic()
query.Query.__div__ = lambda self, pat: self + pattern.match(pat)
query.Query.__and__ = lambda self, pats: self + pattern.All(*pats)
query.Query.__or__ = lambda self, pats: self + pattern.Choice(*pats)
query.Query.__floordiv__ = lambda self, pat: self + dset.Filter(pat)
query.Query.__rshift__ = lambda self, pat: self + dset.Map(pat)
import pattern
import strados2
import stratego
import tidy
games = pd.read_csv("../data/classic.csv")
setups = tidy.setups(games.copy())
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
np.set_printoptions(linewidth=100)
dfR = df.query('side_X == "L" & side_9 == "L"')
matches = pattern.match(df, """
BB......BB
..........
..........
..........
""")[0]
setups = [s.pieces for s in matches['setup_str']]
pieces = stratego.StrategoSetup.pieces
num_setups = len(setups)
placements = {p: sum(s == p for s in setups) for p in pieces}
print(num_setups)
print("Placement by piece and square")
for p, n in placements.items():
print(p)
print(np.round(n / num_setups, 3))
print("")
"""
Created on Tue Nov 7 17:23:15 2017
@author: Xie Yang
"""
import corpusProcess
import prepareVector
import pattern
import svm
import configparser
config = configparser.ConfigParser()
config.read('config.conf', encoding="utf8")
fileName = config['input']['fileName']
fn = fileName.split('.')[0]
filePath = config['input']['filePath']
mode = config['mode']['mode']
if mode == 'svm':
corpusProcess.process(fileName, filePath)
prepareVector.toVector(fileName, filePath)
svm.predict("model/svmclf.clf", filePath, fileName)
elif mode == 'pattern':
pattern.match(fileName, filePath)
elif mode == 'rf':
pass
elif mode == 'ensemble':
pass
else:
print("请输入正确的模式！")
