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- #!/usr/bin/env python3
- # -*- coding: utf-8 -*-
- """
- Created on Sun Dec 23 16:40:52 2018
-
- @author: ljia
- """
- import sys
- import numpy as np
- import networkx as nx
-
- sys.path.insert(0, "../")
- from gklearn.utils.graphfiles import loadDataset
- from gklearn.utils.model_selection_precomputed import compute_gram_matrices
- from gklearn.kernels.spKernel import spkernel
- from sklearn.model_selection import ParameterGrid
-
- from libs import *
- import multiprocessing
- import functools
- from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
-
-
- if __name__ == "__main__":
- # load dataset.
- print('getting dataset and computing kernel distance matrix first...')
- ds_name = 'SYNTHETICnew'
- gkernel = 'spkernel'
- dataset = '../datasets/SYNTHETICnew/SYNTHETICnew_A.txt'
- Gn, y_all = loadDataset(dataset)
-
- for G in Gn:
- G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl'
-
- # compute/read Gram matrix and pair distances.
- mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
- Kmatrix = np.empty((len(Gn), len(Gn)))
- Kmatrix, run_time, idx = spkernel(Gn, node_label=None, node_kernels=
- {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel},
- n_jobs=multiprocessing.cpu_count(), verbose=True)
-
- # normalization
- Kmatrix_diag = Kmatrix.diagonal().copy()
- for i in range(len(Kmatrix)):
- for j in range(i, len(Kmatrix)):
- Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])
- Kmatrix[j][i] = Kmatrix[i][j]
-
- np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
- Kmatrix=Kmatrix, run_time=run_time)
-
- print('complete!')
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