graphkit-learn/notebooks/run_spkernel.ipynb

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      "\n",
      "Acyclic\n",
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "\n",
      "I. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      " None edge weight specified. Set all weight to 1.\n",
      "\n",
      "getting sp graphs: 100%|██████████| 183/183 [00:00<00:00, 2750.49it/s]\n",
      "calculating kernels: 100%|█████████▉| 16808/16836.0 [00:11<00:00, 607.39it/s] \n",
      " --- shortest path kernel matrix of size 183 built in 11.701499700546265 seconds ---\n",
      "calculating kernels: 100%|██████████| 16836/16836.0 [00:11<00:00, 1447.26it/s]\n",
      "\n",
      "the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}} is: \n",
      "[[1.         0.47140452 0.33333333 ... 0.30151134 0.30512858 0.27852425]\n",
      " [0.47140452 1.         0.         ... 0.14213381 0.11986583 0.17232809]\n",
      " [0.33333333 0.         1.         ... 0.36851387 0.37293493 0.34815531]\n",
      " ...\n",
      " [0.30151134 0.14213381 0.36851387 ... 1.         0.96429344 0.95175317]\n",
      " [0.30512858 0.11986583 0.37293493 ... 0.96429344 1.         0.96671243]\n",
      " [0.27852425 0.17232809 0.34815531 ... 0.95175317 0.96671243 1.        ]]\n"
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