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from Train_clique import * |
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from heap_n_clique import * |
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from nnet import * |
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from TestPool_Unit_clique import * |
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from sentences import * |
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bz2_input_folder = '../NewData/skt_dcs_DS.bz2_4K_bigram_mir_10K/' |
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loaded_SKT = pickle.load(open('../Simultaneous_CompatSKT_10K.p', 'rb'), encoding=u'utf-8') |
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loaded_DCS = pickle.load(open('../Simultaneous_DCS_10K.p', 'rb'), encoding=u'utf-8') |
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dsbz2_name = '4442.ds.bz2' |
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(nodelist_correct, conflicts_Dict_correct, featVMat_correct, nodelist_to_correct_mapping,\ |
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nodelist, conflicts_Dict, featVMat) = open_dsbz2(bz2_input_folder + dsbz2_name) |
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sentenceObj = loaded_SKT['4442.p2'] |
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WScalarMat_correct = Get_W_Scalar_Matrix_from_FeatVect_Matrix(featVMat_correct, nodelist_correct,\ |
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conflicts_Dict_correct, self.neuralnet) |
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source = 0 |
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(min_st_gold_ndict, min_st_adj_gold_small, _) =MST(nodelist_correct, WScalarMat_correct, conflicts_Dict_correct, source) |
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energy_gold_max_ST = np.sum(WScalarMat_correct[min_st_adj_gold_small]) |
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print(min_st_gold_ndict) |
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