graphkit-learn/notebooks/run_spkernel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Acyclic\n",
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      "getting sp graphs: 100%|██████████| 183/183 [00:00<00:00, 4451.98it/s]\n",
      "calculating kernels: 100%|█████████▉| 16811/16836.0 [00:24<00:00, 254.60it/s]--- shortest path kernel matrix of size 183 built in 24.209428787231445 seconds ---\n",
      "calculating kernels: 100%|██████████| 16836/16836.0 [00:24<00:00, 696.65it/s]\n",
      "[[ 1.          0.56568542  0.2        ...,  0.22823241  0.23199267\n",
      "   0.21342821]\n",
      " [ 0.56568542  1.          0.         ...,  0.10758979  0.09113533\n",
      "   0.13205197]\n",
      " [ 0.2         0.          1.         ...,  0.32966903  0.33510052\n",
      "   0.32014232]\n",
      " ..., \n",
      " [ 0.22823241  0.10758979  0.32966903 ...,  1.          0.95437578\n",
      "   0.94039745]\n",
      " [ 0.23199267  0.09113533  0.33510052 ...,  0.95437578  1.          0.95932949]\n",
      " [ 0.21342821  0.13205197  0.32014232 ...,  0.94039745  0.95932949  1.        ]]\n"
     ]
    },
    {
     "data": {