tutorials/minibn/Simulations with minibn.ipynb

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    "## Simple simulations with minibn\n",
    "\n",
    "The module `minibn` implements basic methods for computing simulation runs with usual synchronous, fully-asynchronous, and (general) asynchronous update modes. For the two latter, the `seed` argument ensures the reproducibility of traces.\n",
    "The implementation also enables defining custom update modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from colomoto import minibn\n",
    "import pandas as pd # for displaying traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bn = minibn.BooleanNetwork({\n",
    "    \"a\": \"c\",\n",
    "    \"b\": \"!a\",\n",
    "    \"c\": \"!b\"\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run synchronous simulation from zero state, for at most 10 iterations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>1</td>\n",
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       "    a  b  c\n",
       "0   0  0  0\n",
       "1   0  1  1\n",
       "2   1  1  0\n",
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       "4   0  1  1\n",
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       "6   0  0  0\n",
       "7   0  1  1\n",
       "8   1  1  0\n",
       "9   0  0  0\n",
       "10  0  1  1"
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "pd.DataFrame(minibn.SyncRun(bn, bn.zero(), 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run fully-aynchronous simulation from zero state, for at most 10 iterations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "   a  b  c\n",
       "0  0  0  0\n",
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   "source": [
    "pd.DataFrame(minibn.FAsyncRun(bn, bn.zero(), 10, seed=23892830))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run (general) aynchronous simulation from zero state, for at most 10 iterations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "   a  b  c\n",
       "0  0  0  0\n",
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   "source": [
    "pd.DataFrame(minibn.GAsyncRun(bn, bn.zero(), 10, seed=12323892))"
   ]
  },
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   "source": [
    "We define a custom update mode by extending from the general asynchronous mode.\n",
    "The method `select_for_update` is called to select which nodes to update from the given list of nodes which can change of value in the current state.\n",
    "\n",
    "In this example, we implement a simple priority mecanisms which will select the nodes with the highest priority, and then use the general asynchronous mode, i.e., picking randomly a subset of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PrioGAsyncRun(minibn.GAsyncRun):\n",
    "    def __init__(self, *args, priorities=[], **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.priorities = priorities\n",
    "    def select_for_update(self, nodes):\n",
    "        # select nodes by priority order\n",
    "        for m in self.priorities:\n",
    "            mn = set(m).intersection(nodes)\n",
    "            if mn:\n",
    "                nodes = list(mn)\n",
    "                break\n",
    "        # use parent class to select the nodes to update\n",
    "        return super().select_for_update(nodes)"
   ]
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       "   a  b  c\n",
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    "priorities = [{\"c\"},{\"b\"},{\"a\"}]\n",
    "pd.DataFrame(PrioGAsyncRun(bn, bn.zero(), 10, priorities=priorities, seed=12323892))"
   ]
  }
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