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!")