{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HypTrails\n",
    "> This tutorial is still in development!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "from pytrails.hyptrails import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-51.7608370813\n"
     ]
    }
   ],
   "source": [
    "transition_counts = csr_matrix(\n",
    "    [[1, 2, 3], \n",
    "     [4, 5, 6], \n",
    "     [7, 8, 9]],\n",
    "    dtype=np.float64)\n",
    "transition_probabilities = normalize(transition_counts, \"l1\", axis=1)\n",
    "pseudo_counts = transition_probabilities * 5\n",
    "ml = MarkovChain.marginal_likelihood(transition_counts, pseudo_counts)\n",
    "print(ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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