def show(): path =r"C:\Users\inesmeya\Desktop\out\TestTest.pck" dbs = pload(path) #a = ProblemSetSolution(agent, roomset) for db in dbs: print db
def show(): path = r"C:\Users\inesmeya\Desktop\out\TestTest.pck" dbs = pload(path) #a = ProblemSetSolution(agent, roomset) for db in dbs: print db
def test(): ag = TestAgent(AnytimeBestFirstGraphSearch(), heuristics.PowerHeuristic2()) rs = RoomSet("d") a = ProblemSetSolution(ag, rs) # path =r"C:\Users\inesmeya\Desktop\out\Test\p.p" path =r"C:\Users\inesmeya\Desktop\out\Test\e.txt" a = pload(path) print "F" print a
def test(): ag = TestAgent(AnytimeBestFirstGraphSearch(), heuristics.PowerHeuristic2()) rs = RoomSet("d") a = ProblemSetSolution(ag, rs) # path =r"C:\Users\inesmeya\Desktop\out\Test\p.p" path = r"C:\Users\inesmeya\Desktop\out\Test\e.txt" a = pload(path) print "F" print a
def try_load_room_set(name, count, seed): ''' return roomset if file exists None otherwise ''' path = roomset_filepath(name, count, seed) if not os.path.exists(path): return None print "Room set loaded:", path return pload(path)
def load(self, path): self.dbs = utils.pload(path)
from traditional import idf from preprocessing import corenlp as nlp import logging from utils import pload, pdump training_path_base = "../../data/training/" logging.basicConfig( level=logging.INFO, format= "%(levelname)s: %(asctime)s %(filename)s[line:%(lineno)d] %(message)s") # Obtain training data training_matrix = pload(training_path_base + "training_data.pickle") sentence_pair_matrix = [] label_matrix = [] # Using Stanford NLP to parsing the sentence pair lemma_matrix = [] parse_tree_matrix = [] # for syntactic parse features corpus = [[], []] # for idf, bow, word embedding parse_result_matrix = [] # for alignment features dep_matrix = [[], []] def parse_dependency(dep_triple): return " ".join([ '|'.join(