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18 lines
1.0 KiB
18 lines
1.0 KiB
import numpy as np |
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import scipy.sparse as sp |
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def load_yam_feature(dataset): |
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dp = np.loadtxt('../../data/RawData/Yamanishi/{}_admat_dgc.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float).T |
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dd = np.loadtxt('../../data/RawData/Yamanishi/{}_simmat_dc.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float) |
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pp = np.loadtxt('../../data/RawData/Yamanishi/{}_simmat_dg.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float) |
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feature = np.vstack((np.hstack((dd, dp)), np.hstack((dp.T, pp)))) |
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return sp.lil_matrix(feature) |
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def load_luo_feature(dataset): |
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dp = np.loadtxt('../../data/RawData/luo/mat_drug_protein.txt'.format(dataset), dtype=float) |
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dd = np.loadtxt('../../data/RawData/luo/Similarity_Matrix_Drugs.txt'.format(dataset), dtype=float) |
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pp = np.loadtxt('../../data/RawData/luo/Similarity_Matrix_Proteins.txt'.format(dataset), dtype=float) / 100 |
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feature = np.vstack((np.hstack((dd, dp)), np.hstack((dp.T, pp)))) |
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return sp.lil_matrix(feature)
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