notebooks/signaling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Complex Signals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from causalinfo import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You only need this if you want to draw pretty pictures of the Networks\n",
    "from nxpd import draw, nxpdParams\n",
    "nxpdParams['show'] = 'ipynb'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "c1, c2, s1, s2, s3, s4, a1 = make_variables('c1 c2 s1 s2 s3 s4 a1', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "eq1 = Equation('SAME', [c1], [s1], equations.same_)\n",
    "eq2 = Equation('SAMEB', [c2], [s2, s3], equations.branch_same_)\n",
    "eq3 = Equation('AND', [s1, s2], [s4], equations.and_)\n",
    "eq4 = Equation('OR', [s3, s4], [a1], equations.or_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "net = CausalGraph([eq1, eq2, eq3, eq4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw(net.full_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Pr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>a1</th>\n",
       "      <th>c1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.25</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<causalinfo.probability.TreeDistribution at 0x117591910>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_observe = net.generate_joint(UniformDist(c1, c2))\n",
    "j_observe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "1.0\n",
      "1.0\n",
      "0.31127812445913294\n"
     ]
    }
   ],
   "source": [
    "print(j_observe.mutual_info(s1, a1))\n",
    "print(j_observe.mutual_info(s2, a1))\n",
    "print(j_observe.mutual_info(s3, a1))\n",
    "print(j_observe.mutual_info(s4, a1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04879494069539869\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    <tr>\n",
       "      <th>c2</th>\n",
       "      <th>s1</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.125</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<causalinfo.probability.TreeDistribution at 0x118bec590>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_do_s2 = net.generate_joint(UniformDist(c1, c2), UniformDist(s2))\n",
    "print(j_do_s2.mutual_info(s2, a1))\n",
    "j_do_s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04879494069539869\n",
      "0.04879494069539869\n",
      "0.5487949406953987\n",
      "0.20443400292496494\n"
     ]
    }
   ],
   "source": [
    "print(j_do_s2.mutual_info(s1, a1))\n",
    "print(j_do_s2.mutual_info(s2, a1))\n",
    "print(j_do_s2.mutual_info(s3, a1))\n",
    "print(j_do_s2.mutual_info(s4, a1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "name": "stdout",
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_do_s3 = net.generate_joint(UniformDist(c1, c2), UniformDist(s3))\n",
    "print(j_do_s3.mutual_info(s3, a1))\n",
    "j_do_s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04879494069539869\n",
      "0.04879494069539869\n",
      "0.5487949406953987\n",
      "0.20443400292496494\n"
     ]
    }
   ],
   "source": [
    "print(j_do_s3.mutual_info(s1, a1))\n",
    "print(j_do_s3.mutual_info(s2, a1))\n",
    "print(j_do_s3.mutual_info(s3, a1))\n",
    "print(j_do_s3.mutual_info(s4, a1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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   "outputs": [
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      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_do_s1 = net.generate_joint(UniformDist(c1, c2), UniformDist(s1))\n",
    "j_do_s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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      "0.0\n",
      "1.0\n",
      "1.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(j_do_s1.mutual_info(s1, a1))\n",
    "print(j_do_s1.mutual_info(s2, a1))\n",
    "print(j_do_s1.mutual_info(s3, a1))\n",
    "print(j_do_s1.mutual_info(s4, a1, c2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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    "jupyter": {
     "outputs_hidden": false
    }
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   "outputs": [
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   "execution_count": 20,
   "metadata": {
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   },
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