diff --git a/docs/getting_started/die.html b/docs/getting_started/die.html index 7243e1d..99877ca 100644 --- a/docs/getting_started/die.html +++ b/docs/getting_started/die.html @@ -1,6 +1,6 @@ + > \ No newline at end of file diff --git a/docs/getting_started/die.ipynb b/docs/getting_started/die.ipynb index 5ce68a6..949908a 100644 --- a/docs/getting_started/die.ipynb +++ b/docs/getting_started/die.ipynb @@ -1,13167 +1,13190 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A simple die" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import stormvogel.model\n", - "import stormvogel.visualization\n", - "import stormvogel.show\n", - "import stormvogel.mapping\n", - "import stormpy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will show off the API by creating a simple six-sided die." - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A simple die" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import stormvogel.model\n", + "import stormvogel.visualization\n", + "import stormvogel.show\n", + "import stormvogel.mapping\n", + "import stormpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will show off the API by creating a simple six-sided die." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "\n", + " " ], - "source": [ - "# Create a new model with the name \"Die\"\n", - "dtmc = stormvogel.model.new_dtmc(\"Die\")\n", - "\n", - "init = dtmc.get_initial_state()\n", - "\n", - "# From the initial state, add the transition to 6 new states with probability 1/6th.\n", - "init.set_transitions(\n", - " [(1 / 6, dtmc.new_state(f\"rolled{i}\", {\"rolled\": i})) for i in range(1, 7)]\n", - ")\n", - "dtmc.add_self_loops()\n", - "import ipywidgets as widgets\n", - "import IPython.display as ipd\n", - "import logging\n", - "parent_logger = logging.getLogger(\"parent\")\n", - "parent_logger.setLevel(logging.DEBUG)\n", - "debug = widgets.Output()\n", - "\n", - "vis = stormvogel.show.show(dtmc, debug_output=debug, save_and_embed=True, name=\"die\")" + "text/plain": [ + "" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a new model with the name \"Die\"\n", + "dtmc = stormvogel.model.new_dtmc(\"Die\")\n", + "\n", + "init = dtmc.get_initial_state()\n", + "\n", + "# From the initial state, add the transition to 6 new states with probability 1/6th.\n", + "init.set_transitions(\n", + " [(1 / 6, dtmc.new_state(f\"rolled{i}\", {\"rolled\": i})) for i in range(1, 7)]\n", + ")\n", + "dtmc.add_self_loops()\n", + "import ipywidgets as widgets\n", + "import IPython.display as ipd\n", + "import logging\n", + "parent_logger = logging.getLogger(\"parent\")\n", + "parent_logger.setLevel(logging.DEBUG)\n", + "debug = widgets.Output()\n", + "\n", + "vis = stormvogel.show.show(dtmc, debug_output=debug, save_and_embed=True, name=\"die\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "stormpy_dtmc = stormvogel.mapping.stormvogel_to_stormpy(dtmc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can perform DTMC evolution, although it is not very interesting for this particular model..." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "stormpy_dtmc = stormvogel.mapping.stormvogel_to_stormpy(dtmc)" + "data": { + "image/png": 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wgX72s5+d93xUVJRqa2u7PRQA4OJ1Keh2u10NDQ3nPV9ZWalBgwZ1eygAwMXrUtD//u//Xs8995zOnj0rSbLZbPrss880Z84cTZo0ya8DAgA6p0tBX7p0qTwejwYPHqyvv/5ad955p4YOHSqHw6Hnn3/e3zO2kZycrKysLN/jmJgYFRYWdmvNvLw830cZAEBv1aV3uYSEhGjr1q0qLS1VRUWFPB6P4uPjlZqa6u/5LPHGG29owYIFOnz4sM6ePavY2Fj9y7/8i376059aPRoAXFCXgr527VpNmTJFSUlJSkpK8j3f3Nys1157TVOnTu3SMM3NzQoMDOzSa/0pLCxMOTk5Gj58uAIDA/XWW28pPT1dgwcP1oQJE6weDwC+V5duuaSnp6u+vv6850+fPq309PROr5OcnKyMjAxlZWUpPDxcEyZM0Pbt2zVmzBjZ7XY5nU7NnTtX586d6/Sap06d0rRp0zRo0CAFBwdr/PjxqqioaLPPokWLFBERIYfDIbfbrcbGxvPmuv/++zVixAjdeOONyszM1MiRI7Vz585OzwEAl1qXP23RZjv/F9qPHTumkJCQi1przZo1CgwMVGlpqfLy8jRx4kSNHj1aFRUVWrFihVauXKmCgoJOrzd58mTV1dVp8+bN2rdvn+Lj45WSkuL7bdZ169YpLy9PCxYsUFlZmZxOp5YvX97uuW7btk2HDh3SuHHjLrhfU1OTGhoa2mwAcCld1C2X2267TTabTTabTSkpKQoI+P+Xt7S06MiRI/rRj350UQPExsZq8eLFkr65lRMdHa1ly5bJZrNp+PDhOn78uObMmaNnn31Wffq0/++fnTt3au/evaqrq5PdbpckLVmyRBs3btT69ev1xBNPqLCwUG63W263W5JUUFCgd95557yr9Pr6ekVFRampqUl9+/bV8uXL9cMf/vCCx164cKHy8/Mv6twBwJ8uKuhpaWmSpPLyck2YMEFBQUG+7wUGBiomJuai37aYkJDg+/rAgQNyuVxtrv6TkpLk8Xh07NgxXXfdde2u9e0PaAcOHNjm+a+//lpVVVW+Yzz55JNtvu9yufTuu++2ec7hcKi8vFwej0fbtm1Tdna2brjhBiUnJ3/vsefNm6fs7Gzf44aGBkVHR7c7LwD400UFPTc3V9I3bxWcMmVKm1/776oBAwZ0e41veTweOZ1OlZSUnPe90NDQi1qrT58+vo8FHjVqlA4cOKCFCxdeMOh2u933XwUAYIUuvcvlkUce8fcckqQRI0Zow4YNbe7Rl5aWyuFwaMiQIR2+Pj4+XrW1tQoICFBMTMwFj7Fnz54278TZvXt3h2u3traqqampcycCABbo0g9FW1patGTJEo0ZM0aRkZEKCwtrs3XVjBkzVFNTo1mzZungwYPatGmTcnNzlZ2d3eH9c0lKTU2Vy+VSWlqaiouLVV1drV27diknJ0dlZWWSpMzMTK1atUqrV69WZWWlcnNztX///jbrLFy4UFu3btWnn36qAwcOaOnSpXrllVf0k5/8pMvnBgA9rUtBz8/P13/8x39oypQpqq+vV3Z2th544AH16dNHeXl5XR4mKipKRUVF2rt3r+Li4vTkk0/K7XZr/vz5nXq9zWZTUVGRxo0bp/T0dN1000168MEHdfToUd+fxpsyZYqeeeYZzZ49WwkJCTp69KimT5/eZp0zZ85oxowZ+tu//VslJSVpw4YN+t3vfqdp06Z1+dwAoKd16S8W3XjjjfrlL3+pe+65x/fDw2+f2717t1599dWemLVX4S8WAfCHHv+LRbW1tbr11lslSUFBQb5fMvq7v/s7vf32211ZEgDQTV0K+pAhQ/T5559L+uZqvbi4WNI3n5POOz0AwBpdCvr999+vbdu2SZJmzZqlZ555RrGxsZo6daoee+wxvw4IAOicLt1D/2u7d+/Wrl27FBsbq3vvvdcfc/V63EMH4A8Xcw+9S+9D37Fjh+644w7fr/7ffvvtuv3223Xu3Dnt2LGj3c88AQD0jC7dcrnrrrt8H3b1XfX19brrrru6PRQA4OL59dMW//d//9evv8oPAOi8i7rl8sADD0j65hd4Hn300TbvaGlpadHHH3+sO+64w78TAgA65aKC/u1nnXu9XjkcDvXv39/3vcDAQN1+++16/PHH/TshAKBTLiroq1evliQNGjRIeXl5uvrqqyVJ1dXV2rhxo0aMGKHw8HD/TwkA6FCX7qF/9NFHWrt2raRv/uTb7bffrqVLlyotLU0rVqzw64AAgM7pctB/8IMfSJLWr1+viIgIHT16VGvXrtUvf/lLvw4IAOicLgX9q6++ksPhkCQVFxf7Pmnx9ttv19GjR/06IACgc7oU9KFDh2rjxo2qqanRli1bdPfdd0uS6urqOvxNJgBAz+hS0J999ln9/Oc/V0xMjMaOHSuXyyXpm6v12267za8DAgA6p8uf5VJbW6vPP/9ccXFxvr8mtHfvXgUHB2v48OF+HbI34rNcAPhDj3+WiyRFRkYqMjKyzXNjxozp6nIAgG7q0i0XAMDlh6ADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCF6XdCTk5OVlZXlexwTE6PCwsJurZmXl6dRo0Z1aw0AsFqvC/ql9tprr8lmsyktLc3qUQCgXZdV0Jubm60eoY3q6mr9/Oc/1w9+8AOrRwGADlka9OTkZGVkZCgrK0vh4eGaMGGCtm/frjFjxshut8vpdGru3Lk6d+5cp9c8deqUpk2bpkGDBik4OFjjx49XRUVFm30WLVqkiIgIORwOud1uNTY2nrdOS0uLHn74YeXn5+uGG27o9rkCQE+z/Ap9zZo1CgwMVGlpqfLy8jRx4kSNHj1aFRUVWrFihVauXKmCgoJOrzd58mTV1dVp8+bN2rdvn+Lj45WSkqKTJ09KktatW6e8vDwtWLBAZWVlcjqdWr58+XnrPPfccxo8eLDcbnenjtvU1KSGhoY2GwBcSgFWDxAbG6vFixdLktauXavo6GgtW7ZMNptNw4cP1/HjxzVnzhw9++yz6tOn/X//7Ny5U3v37lVdXZ3sdrskacmSJdq4caPWr1+vJ554QoWFhXK73b5QFxQU6J133mlzlb5z506tXLlS5eXlnT6PhQsXKj8//yLPHgD8x/Ir9ISEBN/XBw4ckMvlks1m8z2XlJQkj8ejY8eOdbhWRUWFPB6PBg4cqKCgIN925MgRVVVV+Y4xduzYNq9zuVy+r0+fPq2f/vSneumllxQeHt7p85g3b57q6+t9W01NTadfCwD+YPkV+oABA/y2lsfjkdPpVElJyXnfCw0N7dQaVVVVqq6u1r333ut7rrW1VZIUEBCgQ4cO6cYbbzzvdXa73fdfBQBgBcuD/l0jRozQhg0b5PV6fVfppaWlcjgcGjJkSIevj4+PV21trQICAhQTE3PBY+zZs0dTp071Pbd7927f18OHD9cnn3zS5jXz58/X6dOn9cILLyg6OroLZwYAPe+yCvqMGTNUWFioWbNmKSMjQ4cOHVJubq6ys7M7vH8uSampqXK5XEpLS9PixYt100036fjx43r77bd1//33KzExUZmZmXr00UeVmJiopKQk/f73v9f+/ft972Tp16+fbrnlljbrfnt1/9fPA8Dl5LIKelRUlIqKivT0008rLi5OYWFhcrvdmj9/fqdeb7PZVFRUpJycHKWnp+vEiROKjIzUuHHjFBERIUmaMmWKqqqqNHv2bDU2NmrSpEmaPn26tmzZ0pOnBgA9zub1er1WD2GihoYGhYSEqL8kW4d79z5PWT0AcIVokvQLSfX19QoODm53X8vf5QIA8A+CDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYAiCDgCGIOgAYIheF/Tk5GRlZWX5HsfExKiwsLBba+bl5WnUqFHdWgMArNbrgn4p/Pa3v5XNZmuz9evXz+qxAKBdAVYP8F3Nzc0KDAy0egxJUnBwsA4dOuR7bLPZLJwGADpm6RV6cnKyMjIylJWVpfDwcE2YMEHbt2/XmDFjZLfb5XQ6NXfuXJ07d67Ta546dUrTpk3ToEGDFBwcrPHjx6uioqLNPosWLVJERIQcDofcbrcaGxvPW8dmsykyMtK3RUREdPt8AaAnWX7LZc2aNQoMDFRpaany8vI0ceJEjR49WhUVFVqxYoVWrlypgoKCTq83efJk1dXVafPmzdq3b5/i4+OVkpKikydPSpLWrVunvLw8LViwQGVlZXI6nVq+fPl563g8Hl1//fWKjo7Wfffdp/3797d73KamJjU0NLTZAOBSsjzosbGxWrx4sYYNG6bi4mJFR0dr2bJlGj58uNLS0pSfn6+lS5eqtbW1w7V27typvXv36vXXX1diYqJiY2O1ZMkShYaGav369ZKkwsJCud1uud1uDRs2TAUFBbr55pvbrDNs2DCtWrVKmzZt0u9+9zu1trbqjjvu0LFjxy547IULFyokJMS3RUdHd+8fDABcJMuDnpCQ4Pv6wIEDcrlcbe5XJyUlyePxtBvTb1VUVMjj8WjgwIEKCgrybUeOHFFVVZXvGGPHjm3zOpfLdd7jqVOnatSoUbrzzjv1xhtvaNCgQXrxxRcveOx58+apvr7et9XU1HTq/AHAXyz/oeiAAQP8tpbH45HT6VRJScl53wsNDe3yuldddZVuu+02HT58+IL72O122e32Lh8DALrL8iv07xoxYoTef/99eb1e33OlpaVyOBwaMmRIh6+Pj49XbW2tAgICNHTo0DZbeHi47xh79uxp87rdu3e3u25LS4s++eQTOZ3OLpwVAFwal1XQZ8yYoZqaGs2aNUsHDx7Upk2blJubq+zsbPXp0/GoqampcrlcSktLU3Fxsaqrq7Vr1y7l5OSorKxMkpSZmalVq1Zp9erVqqysVG5u7nk/8HzuuedUXFysTz/9VB9++KF+8pOf6OjRo5o2bVqPnDcA+IPlt1y+KyoqSkVFRXr66acVFxensLAwud1uzZ8/v1Ovt9lsKioqUk5OjtLT03XixAlFRkZq3LhxvrcdTpkyRVVVVZo9e7YaGxs1adIkTZ8+XVu2bPGt8+WXX+rxxx9XbW2trrnmGiUkJGjXrl3n/fAUAC4nNu9372/AbxoaGhQSEqL+kkz8laSnrB4AuEI0SfqFpPr6egUHB7e772V1ywUA0HUEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBC9LujJycnKysryPY6JiVFhYWG31szLy9OoUaO6tQYAWK3XBf1SOXXqlGbOnCmn0ym73a6bbrpJRUVFVo8FABcUYPUA39Xc3KzAwECrx1Bzc7N++MMfavDgwVq/fr2ioqJ09OhRhYaGWj0aAFyQpVfoycnJysjIUFZWlsLDwzVhwgRt375dY8aMkd1ul9Pp1Ny5c3Xu3LlOr3nq1ClNmzZNgwYNUnBwsMaPH6+Kioo2+yxatEgRERFyOBxyu91qbGxs8/1Vq1bp5MmT2rhxo5KSkhQTE6M777xTcXFxfjlvAOgJlt9yWbNmjQIDA1VaWqq8vDxNnDhRo0ePVkVFhVasWKGVK1eqoKCg0+tNnjxZdXV12rx5s/bt26f4+HilpKTo5MmTkqR169YpLy9PCxYsUFlZmZxOp5YvX95mjf/+7/+Wy+XSzJkzFRERoVtuuUULFixQS0vLBY/b1NSkhoaGNhsAXEqW33KJjY3V4sWLJUlr165VdHS0li1bJpvNpuHDh+v48eOaM2eOnn32WfXp0/6/f3bu3Km9e/eqrq5OdrtdkrRkyRJt3LhR69ev1xNPPKHCwkK53W653W5JUkFBgd555502V+mffvqp/vCHP+jhhx9WUVGRDh8+rBkzZujs2bPKzc393mMvXLhQ+fn5/vhHAgBdYvkVekJCgu/rAwcOyOVyyWaz+Z5LSkqSx+PRsWPHOlyroqJCHo9HAwcOVFBQkG87cuSIqqqqfMcYO3Zsm9e5XK42j1tbWzV48GD95je/UUJCgqZMmaKcnBz953/+5wWPPW/ePNXX1/u2mpqaTp0/APiL5VfoAwYM8NtaHo9HTqdTJSUl533vYn6g6XQ6ddVVV6lv376+50aMGKHa2toL/uDWbrf7/qsAAKxg+RX6d40YMULvv/++vF6v77nS0lI5HA4NGTKkw9fHx8ertrZWAQEBGjp0aJstPDzcd4w9e/a0ed3u3bvbPE5KStLhw4fV2trqe66yslJOp/OyeBcOAHyfyyroM2bMUE1NjWbNmqWDBw9q06ZNys3NVXZ2dof3zyUpNTVVLpdLaWlpKi4uVnV1tXbt2qWcnByVlZVJkjIzM7Vq1SqtXr1alZWVys3N1f79+9usM336dJ08eVKZmZmqrKzU22+/rQULFmjmzJk9ct4A4A+W33L5rqioKBUVFenpp59WXFycwsLC5Ha7NX/+/E693mazqaioSDk5OUpPT9eJEycUGRmpcePGKSIiQpI0ZcoUVVVVafbs2WpsbNSkSZM0ffp0bdmyxbdOdHS0tmzZon/+53/WyJEjFRUVpczMTM2ZM6dHzhsA/MHm/e79DfhNQ0ODQkJC1F+SrcO9e5+nrB4AuEI0SfqFpPr6egUHB7e772V1ywUA0HUEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMEWD1AKb69m9vm/oXuJusHgC4Qnz7/7Vvm9Iegt5DTp8+LUlqtHiOnvILqwcArjCnT59WSEhIu/vYvJ3JPi5aa2urjh8/LofDIZvN1uPHa2hoUHR0tGpqahQcHNzjx7uUOLfeiXPzD6/Xq9OnT+vaa69Vnz7t3yXnCr2H9OnTR0OGDLnkxw0ODjbu/zzf4tx6J86t+zq6Mv8WPxQFAEMQdAAwBEE3hN1uV25urux2u9Wj+B3n1jtxbpcePxQFAENwhQ4AhiDoAGAIgg4AhiDoAGAIgm6AX//614qJiVG/fv00duxY7d271+qR/GLHjh269957de2118pms2njxo1Wj+QXCxcu1OjRo+VwODR48GClpaXp0KFDVo/lFytWrNDIkSN9v3Djcrm0efNmq8fqEYsWLZLNZlNWVpbVo/gQ9F7uv/7rv5Sdna3c3Fx9+OGHiouL04QJE1RXV2f1aN125swZxcXF6de//rXVo/jV9u3bNXPmTO3evVtbt27V2bNndffdd+vMmTNWj9ZtQ4YM0aJFi7Rv3z6VlZVp/Pjxuu+++7R//36rR/OrDz74QC+++KJGjhxp9ShtedGrjRkzxjtz5kzf45aWFu+1117rXbhwoYVT+Z8k75tvvmn1GD2irq7OK8m7fft2q0fpEddcc4335ZdftnoMvzl9+rQ3NjbWu3XrVu+dd97pzczMtHokH67Qe7Hm5mbt27dPqampvuf69Omj1NRUvf/++xZOhotRX18vSQoLC7N4Ev9qaWnRa6+9pjNnzsjlclk9jt/MnDlT99xzT5v/310u+HCuXuyLL75QS0uLIiIi2jwfERGhgwcPWjQVLkZra6uysrKUlJSkW265xepx/OKTTz6Ry+VSY2OjgoKC9Oabb+rmm2+2eiy/eO211/Thhx/qgw8+sHqU70XQAQvNnDlT//M//6OdO3daPYrfDBs2TOXl5aqvr9f69ev1yCOPaPv27b0+6jU1NcrMzNTWrVvVr18/q8f5XgS9FwsPD1ffvn31l7/8pc3zf/nLXxQZGWnRVOisjIwMvfXWW9qxY4clH7XcUwIDAzV06FBJUkJCgj744AO98MILevHFFy2erHv27dunuro6xcfH+55raWnRjh07tGzZMjU1Nalv374WTsi7XHq1wMBAJSQkaNu2bb7nWltbtW3bNqPuWZrG6/UqIyNDb775pv7whz/ob/7mb6weqUe1traqqan3/9HClJQUffLJJyovL/dtiYmJevjhh1VeXm55zCWu0Hu97OxsPfLII0pMTNSYMWNUWFioM2fOKD093erRus3j8ejw4cO+x0eOHFF5ebnCwsJ03XXXWThZ98ycOVOvvvqqNm3aJIfDodraWknf/BGD/v37Wzxd98ybN08//vGPdd111+n06dN69dVXVVJSoi1btlg9Wrc5HI7zfs4xYMAADRw48PL5+YfVb7NB9/3qV7/yXnfddd7AwEDvmDFjvLt377Z6JL949913vfrm72y32R555BGrR+uW7zsnSd7Vq1dbPVq3PfbYY97rr7/eGxgY6B00aJA3JSXFW1xcbPVYPeZye9siH58LAIbgHjoAGIKgA4AhCDoAGIKgA4AhCDoAGIKgA4AhCDoAGIKgA4AhCDrQAx599FGlpaVZPQauMAQdAAxB0IFuWL9+vW699Vb1799fAwcOVGpqqp5++mmtWbNGmzZtks1mk81mU0lJiaRvPlP7H//xHxUaGqqwsDDdd999qq6u9q337ZV9fn6+Bg0apODgYD355JNqbm5u95gm/D1SdB+ftgh00eeff66HHnpIixcv1v3336/Tp0/rvffe09SpU/XZZ5+poaFBq1evlvTNn5c7e/asJkyYIJfLpffee08BAQEqKCjQj370I3388ccKDAyUJG3btk39+vVTSUmJqqurlZ6eroEDB+r555+/4DH5SCZI4tMWga7at2+fV5K3urr6vO898sgj3vvuu6/Nc6+88op32LBh3tbWVt9zTU1N3v79+3u3bNnie11YWJj3zJkzvn1WrFjhDQoK8ra0tLR7TIBbLkAXxcXFKSUlRbfeeqsmT56sl156SV9++eUF96+oqNDhw4flcDgUFBSkoKAghYWFqbGxUVVVVW3Wvfrqq32PXS6XPB6PampqLvqYuLIQdKCL+vbtq61bt2rz5s26+eab9atf/UrDhg3TkSNHvnd/j8ejhISENn/xpry8XJWVlfqnf/qnHjkmriwEHegGm82mpKQk5efn66OPPlJgYKDefPNNBQYGqqWlpc2+8fHx+tOf/qTBgwdr6NChbbaQkBDffhUVFfr66699j3fv3q2goCBFR0e3e0yAoANdtGfPHi1YsEBlZWX67LPP9MYbb+jEiRMaMWKEYmJi9PHHH+vQoUP64osvdPbsWT388MMKDw/Xfffdp/fee09HjhxRSUmJnnrqKR07dsy3bnNzs9xut/74xz+qqKhIubm5ysjIUJ8+fdo9JsC7XIAuCg4O1o4dO1RYWKiGhgZdf/31Wrp0qX784x8rMTFRJSUlSkxMlMfj0bvvvqvk5GTt2LFDc+bM0QMPPKDTp08rKipKKSkpCg4O9q2bkpKi2NhYjRs3Tk1NTXrooYeUl5fX4TEB/gQdcBl59NFHderUKW3cuNHqUdALccsFAAxB0AHAENxyAQBDcIUOAIYg6ABgCIIOAIYg6ABgCIIOAIYg6ABgCIIOAIYg6ABgCIIOAIb4P6V/byIgvpd9AAAAAElFTkSuQmCC", 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56, + "y": -12 + }, + "10000000003": { + "x": 40, + "y": -210 + }, + "10000000004": { + "x": 181, + "y": 3 }, - "6": { - "x": 24, - "y": -96 + "10000000005": { + "x": 387, + "y": -132 }, - "7": { - "x": 81, - "y": 69 + "10000000006": { + "x": 198, + "y": 88 }, - "8": { - "x": 126, - "y": 205 + "10000000007": { + "x": 438, + "y": 216 } }, - "width": 590, - "height": 406, + "width": 687, + "height": 519, "physics": true -} +} \ No newline at end of file diff --git a/docs/getting_started/lion.html b/docs/getting_started/lion.html index 23826ec..6384f7a 100644 --- a/docs/getting_started/lion.html +++ b/docs/getting_started/lion.html @@ -1,8 +1,8 @@ + > \ No newline at end of file diff --git a/docs/getting_started/mdp.ipynb b/docs/getting_started/mdp.ipynb index 098dc6c..a357f10 100644 --- a/docs/getting_started/mdp.ipynb +++ b/docs/getting_started/mdp.ipynb @@ -1,27440 +1,25741 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from stormvogel import visualization, mapping, result, show\n", - "import stormpy" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%prism leader\n", - "// asynchronous leader election\n", - "// 4 processes\n", - "// gxn/dxp 29/01/01\n", - "\n", - "mdp\n", - "\n", - "const int N = 3; // number of processes\n", - "\n", - "//----------------------------------------------------------------------------------------------------------------------------\n", - "module process1\n", - "\t\n", - "\t// COUNTER\n", - "\tc1 : [0..3-1];\n", - "\t\n", - "\t// STATES\n", - "\ts1 : [0..4];\n", - "\t// 0 make choice\n", - "\t// 1 have not received neighbours choice\n", - "\t// 2 active\n", - "\t// 3 inactive\n", - "\t// 4 leader\n", - "\t\n", - "\t// PREFERENCE\n", - "\tp1 : [0..1];\n", - "\t\n", - "\t// VARIABLES FOR SENDING AND RECEIVING\n", - "\treceive1 : [0..2];\n", - "\t// not received anything\n", - "\t// received choice\n", - "\t// received counter\n", - "\tsent1 : [0..2];\n", - "\t// not send anything\n", - "\t// sent choice\n", - "\t// sent counter\n", - "\t\n", - "\t// pick value\n", - "\t[pick] (s1=0) -> 0.5 : (s1'=1) & (p1'=0) + 0.5 : (s1'=1) & (p1'=1);\n", - "\n", - "\t// send preference\n", - "\t[p12] (s1=1) & (sent1=0) -> (sent1'=1);\n", - "\t// receive preference\n", - "\t// stay active\n", - "\t[p31] (s1=1) & (receive1=0) & !( (p1=0) & (p3=1) ) -> (s1'=2) & (receive1'=1);\n", - "\t// become inactive\n", - "\t[p31] (s1=1) & (receive1=0) & (p1=0) & (p3=1) -> (s1'=3) & (receive1'=1);\n", - "\t\n", - "\t// send preference (can now reset preference)\n", - "\t[p12] (s1=2) & (sent1=0) -> (sent1'=1) & (p1'=0);\n", - "\t// send counter (already sent preference)\n", - "\t// not received counter yet\n", - "\t[c12] (s1=2) & (sent1=1) & (receive1=1) -> (sent1'=2);\n", - "\t// received counter (pick again)\n", - "\t[c12] (s1=2) & (sent1=1) & (receive1=2) -> (s1'=0) & (p1'=0) & (c1'=0) & (sent1'=0) & (receive1'=0);\n", - "\t\n", - "\t// receive counter and not sent yet (note in this case do not pass it on as will send own counter)\n", - "\t[c31] (s1=2) & (receive1=1) & (sent1<2) -> (receive1'=2);\n", - "\t// receive counter and sent counter\n", - "\t// only active process (decide)\n", - "\t[c31] (s1=2) & (receive1=1) & (sent1=2) & (c3=N-1) -> (s1'=4) & (p1'=0) & (c1'=0) & (sent1'=0) & (receive1'=0);\n", - "\t// other active process (pick again)\n", - "\t[c31] (s1=2) & (receive1=1) & (sent1=2) & (c3 (s1'=0) & (p1'=0) & (c1'=0) & (sent1'=0) & (receive1'=0);\n", - "\t\n", - "\t// send preference (must have received preference) and can now reset\n", - "\t[p12] (s1=3) & (receive1>0) & (sent1=0) -> (sent1'=1) & (p1'=0);\n", - "\t// send counter (must have received counter first) and can now reset\n", - "\t[c12] (s1=3) & (receive1=2) & (sent1=1) -> (s1'=3) & (p1'=0) & (c1'=0) & (sent1'=0) & (receive1'=0);\n", - "\t\n", - "\t// receive preference\n", - "\t[p31] (s1=3) & (receive1=0) -> (p1'=p3) & (receive1'=1);\n", - "\t// receive counter\n", - "\t[c31] (s1=3) & (receive1=1) & (c3 (c1'=c3+1) & (receive1'=2);\n", - "\t\t\n", - "\t// done\n", - "\t[done] (s1=4) -> (s1'=s1);\n", - "\t// add loop for processes who are inactive\n", - "\t[done] (s1=3) -> (s1'=s1);\n", - "\n", - "endmodule\n", - "\n", - "//----------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "// construct further stations through renaming\n", - "module process2=process1[s1=s2,p1=p2,c1=c2,sent1=sent2,receive1=receive2,p12=p23,p31=p12,c12=c23,c31=c12,p3=p1,c3=c1] endmodule\n", - "module process3=process1[s1=s3,p1=p3,c1=c3,sent1=sent3,receive1=receive3,p12=p31,p31=p23,c12=c31,c31=c23,p3=p2,c3=c2] endmodule\n", - "\n", - "//----------------------------------------------------------------------------------------------------------------------------\n", - "rewards \"rounds\"\n", - " [c12] true : 1;\n", - "endrewards\n", - "\n", - "//----------------------------------------------------------------------------------------------------------------------------\n", - "formula leaders = (s1=4?1:0)+(s2=4?1:0)+(s3=4?1:0);\n", - "label \"elected\" = s1=4|s2=4|s3=4;\n" - ] - }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from stormvogel import visualization, mapping, result, show\n", + "import stormpy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "leader_model = stormpy.build_model(leader)\n", - "\n" + "data": { + "text/plain": [ + "" ] - }, + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%prism leader\n", + "// asynchronous leader election\n", + "// 4 processes\n", + "// gxn/dxp 29/01/01\n", + "\n", + "mdp\n", + "\n", + "const int N = 3; // number of processes\n", + "\n", + "//----------------------------------------------------------------------------------------------------------------------------\n", + "module process1\n", + "\t\n", + "\t// COUNTER\n", + "\tc1 : [0..3-1];\n", + "\t\n", + "\t// STATES\n", + "\ts1 : [0..4];\n", + "\t// 0 make choice\n", + "\t// 1 have not received neighbours choice\n", + "\t// 2 active\n", + "\t// 3 inactive\n", + "\t// 4 leader\n", + "\t\n", + "\t// PREFERENCE\n", + "\tp1 : [0..1];\n", + "\t\n", + "\t// VARIABLES FOR SENDING AND RECEIVING\n", + "\treceive1 : [0..2];\n", + "\t// not received anything\n", + "\t// received choice\n", + "\t// received counter\n", + "\tsent1 : [0..2];\n", + "\t// not send anything\n", + "\t// sent choice\n", + "\t// sent counter\n", + "\t\n", + "\t// pick value\n", + "\t[pick] (s1=0) -> 0.5 : (s1'=1) & (p1'=0) + 0.5 : (s1'=1) & (p1'=1);\n", + "\n", + "\t// send preference\n", + "\t[p12] (s1=1) & (sent1=0) -> (sent1'=1);\n", + "\t// receive preference\n", + "\t// stay active\n", + "\t[p31] (s1=1) & (receive1=0) & !( (p1=0) & (p3=1) ) -> (s1'=2) & (receive1'=1);\n", + "\t// become inactive\n", + "\t[p31] (s1=1) & (receive1=0) & (p1=0) & (p3=1) -> (s1'=3) & (receive1'=1);\n", + "\t\n", + "\t// send preference (can now reset preference)\n", + "\t[p12] (s1=2) & (sent1=0) -> (sent1'=1) & (p1'=0);\n", + "\t// send counter (already sent preference)\n", + "\t// not received counter yet\n", + "\t[c12] (s1=2) & (sent1=1) & (receive1=1) -> (sent1'=2);\n", + "\t// received counter (pick again)\n", + "\t[c12] (s1=2) & (sent1=1) & (receive1=2) -> (s1'=0) & (p1'=0) & (c1'=0) & (sent1'=0) & (receive1'=0);\n", + "\t\n", + "\t// receive counter and not sent yet (note in this case do not pass it on as will send own counter)\n", + "\t[c31] (s1=2) & (receive1=1) & (sent1<2) -> (receive1'=2);\n", + "\t// receive counter and sent counter\n", + "\t// only active process (decide)\n", + "\t[c31] (s1=2) & (receive1=1) & (sent1=2) & (c3=N-1) -> (s1'=4) & (p1'=0) & (c1'=0) & (sent1'=0) & (receive1'=0);\n", + "\t// other active process (pick again)\n", + "\t[c31] (s1=2) & (receive1=1) & (sent1=2) & (c3 (s1'=0) & (p1'=0) & (c1'=0) & (sent1'=0) & (receive1'=0);\n", + "\t\n", + "\t// send preference (must have received preference) and can now reset\n", + "\t[p12] (s1=3) & (receive1>0) & (sent1=0) -> (sent1'=1) & (p1'=0);\n", + "\t// send counter (must have received counter first) and can now reset\n", + "\t[c12] (s1=3) & (receive1=2) & (sent1=1) -> (s1'=3) & (p1'=0) & (c1'=0) & (sent1'=0) & (receive1'=0);\n", + "\t\n", + "\t// receive preference\n", + "\t[p31] (s1=3) & (receive1=0) -> (p1'=p3) & (receive1'=1);\n", + "\t// receive counter\n", + "\t[c31] (s1=3) & (receive1=1) & (c3 (c1'=c3+1) & (receive1'=2);\n", + "\t\t\n", + "\t// done\n", + "\t[done] (s1=4) -> (s1'=s1);\n", + "\t// add loop for processes who are inactive\n", + "\t[done] (s1=3) -> (s1'=s1);\n", + "\n", + "endmodule\n", + "\n", + "//----------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "// construct further stations through renaming\n", + "module process2=process1[s1=s2,p1=p2,c1=c2,sent1=sent2,receive1=receive2,p12=p23,p31=p12,c12=c23,c31=c12,p3=p1,c3=c1] endmodule\n", + "module process3=process1[s1=s3,p1=p3,c1=c3,sent1=sent3,receive1=receive3,p12=p31,p31=p23,c12=c31,c31=c23,p3=p2,c3=c2] endmodule\n", + "\n", + "//----------------------------------------------------------------------------------------------------------------------------\n", + "rewards \"rounds\"\n", + " [c12] true : 1;\n", + "endrewards\n", + "\n", + "//----------------------------------------------------------------------------------------------------------------------------\n", + "formula leaders = (s1=4?1:0)+(s2=4?1:0)+(s3=4?1:0);\n", + "label \"elected\" = s1=4|s2=4|s3=4;\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "leader_model = stormpy.build_model(leader)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-------------------------------------------------------------- \n", - "Model type: \tMDP (sparse)\n", - "States: \t190\n", - "Transitions: \t323\n", - "Choices: \t316\n", - "Reward Models: rounds\n", - "State Labels: \t3 labels\n", - " * deadlock -> 3 item(s)\n", - " * init -> 1 item(s)\n", - " * elected -> 0 item(s)\n", - "Choice Labels: \tnone\n", - "-------------------------------------------------------------- \n", - "\n", - "['__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '_as_sparse_ctmc', '_as_sparse_dtmc', '_as_sparse_exact_dtmc', '_as_sparse_exact_mdp', '_as_sparse_imdp', '_as_sparse_ma', '_as_sparse_mdp', '_as_sparse_pctmc', '_as_sparse_pdtmc', '_as_sparse_pma', '_as_sparse_pmdp', '_as_sparse_pomdp', '_as_sparse_ppomdp', '_as_symbolic_ctmc', '_as_symbolic_dtmc', '_as_symbolic_ma', '_as_symbolic_mdp', '_as_symbolic_pctmc', '_as_symbolic_pdtmc', '_as_symbolic_pma', '_as_symbolic_pmdp', 'add_reward_model', 'apply_scheduler', 'backward_transition_matrix', 'choice_labeling', 'choice_origins', 'get_choice_index', 'get_nr_available_actions', 'get_reward_model', 'has_choice_labeling', 'has_choice_origins', 'has_parameters', 'has_reward_model', 'has_state_valuations', 'initial_states', 'initial_states_as_bitvector', 'is_discrete_time_model', 'is_exact', 'is_nondeterministic_model', 'is_partially_observable', 'is_sink_state', 'is_sparse_model', 'is_symbolic_model', 'labeling', 'labels_state', 'model_type', 'nondeterministic_choice_indices', 'nr_choices', 'nr_states', 'nr_transitions', 'reduce_to_state_based_rewards', 'reward_models', 'set_initial_states', 'state_valuations', 'states', 'supports_parameters', 'supports_uncertainty', 'to_dot', 'transition_matrix']\n" - ] - } - ], - "source": [ - "print(leader_model)\n", - "\n", - "print(dir(leader_model))\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------------------------------------------------- \n", + "Model type: \tMDP (sparse)\n", + "States: \t190\n", + "Transitions: \t323\n", + "Choices: \t316\n", + "Reward Models: rounds\n", + "State Labels: \t3 labels\n", + " * deadlock -> 3 item(s)\n", + " * init -> 1 item(s)\n", + " * elected -> 0 item(s)\n", + "Choice Labels: \tnone\n", + "-------------------------------------------------------------- \n", + "\n", + "['__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '_as_sparse_ctmc', '_as_sparse_dtmc', '_as_sparse_exact_dtmc', '_as_sparse_exact_mdp', '_as_sparse_imdp', '_as_sparse_ma', '_as_sparse_mdp', '_as_sparse_pctmc', '_as_sparse_pdtmc', '_as_sparse_pma', '_as_sparse_pmdp', '_as_sparse_pomdp', '_as_sparse_ppomdp', '_as_sparse_smg', '_as_symbolic_ctmc', '_as_symbolic_dtmc', '_as_symbolic_ma', '_as_symbolic_mdp', '_as_symbolic_pctmc', '_as_symbolic_pdtmc', '_as_symbolic_pma', '_as_symbolic_pmdp', 'add_reward_model', 'apply_scheduler', 'backward_transition_matrix', 'choice_labeling', 'choice_origins', 'get_choice_index', 'get_nr_available_actions', 'get_reward_model', 'has_choice_labeling', 'has_choice_origins', 'has_parameters', 'has_reward_model', 'has_state_valuations', 'initial_states', 'initial_states_as_bitvector', 'is_discrete_time_model', 'is_exact', 'is_nondeterministic_model', 'is_partially_observable', 'is_sink_state', 'is_sparse_model', 'is_symbolic_model', 'labeling', 'labels_state', 'model_type', 'nondeterministic_choice_indices', 'nr_choices', 'nr_states', 'nr_transitions', 'reduce_to_state_based_rewards', 'reward_models', 'set_initial_states', 'state_valuations', 'states', 'supports_parameters', 'supports_uncertainty', 'to_dot', 'transition_matrix']\n" + ] + } + ], + "source": [ + "print(leader_model)\n", + "\n", + "print(dir(leader_model))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "prop = stormpy.parse_properties(\"Rmax=? [F \\\"elected\\\"]\", leader)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "stormpy_result = stormpy.model_checking(leader_model, prop[0], extract_scheduler=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "prop = stormpy.parse_properties(\"Rmax=? [F \\\"elected\\\"]\", leader)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "[inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]\n" + ] + } + ], + "source": [ + "print(stormpy_result.get_values())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "stormpy_result = stormpy.model_checking(leader_model, prop[0], extract_scheduler=True)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------------------------------------------------- \n", + "Model type: \tDTMC (sparse)\n", + "States: \t49\n", + "Transitions: \t56\n", + "Reward Models: rounds\n", + "State Labels: \t3 labels\n", + " * elected -> 0 item(s)\n", + " * init -> 1 item(s)\n", + " * deadlock -> 3 item(s)\n", + "Choice Labels: \tnone\n", + "-------------------------------------------------------------- \n", + "\n" + ] + } + ], + "source": [ + "scheduler = stormpy_result.scheduler\n", + "\n", + "print(leader_model.apply_scheduler(scheduler))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]\n" - ] - } - ], - "source": [ - "print(stormpy_result.get_values())" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0 -> 0, 1 -> 0, 2 -> 0, 3 -> 0, 4 -> 0, 5 -> 0, 6 -> 0, 7 -> 0, 8 -> 0, 9 -> 0, 10 -> 0, 11 -> 0, 12 -> 0, 13 -> 0, 14 -> 0, 15 -> 0, 16 -> 0, 17 -> 0, 18 -> 0, 19 -> 0, 20 -> 0, 21 -> 0, 22 -> 0, 23 -> 0, 24 -> 0, 25 -> 0, 26 -> 0, 27 -> 0, 28 -> 0, 29 -> 0, 30 -> 0, 31 -> 0, 32 -> 0, 33 -> 0, 34 -> 0, 35 -> 0, 36 -> 0, 37 -> 0, 38 -> 0, 39 -> 0, 40 -> 0, 41 -> 0, 42 -> 0, 43 -> 0, 44 -> 0, 45 -> 0, 46 -> 0, 47 -> 0, 48 -> 0, 49 -> 0, 50 -> 0, 51 -> 0, 52 -> 0, 53 -> 0, 54 -> 0, 55 -> 0, 56 -> 0, 57 -> 0, 58 -> 0, 59 -> 0, 60 -> 0, 61 -> 0, 62 -> 0, 63 -> 0, 64 -> 0, 65 -> 0, 66 -> 0, 67 -> 0, 68 -> 0, 69 -> 0, 70 -> 0, 71 -> 0, 72 -> 0, 73 -> 0, 74 -> 0, 75 -> 0, 76 -> 0, 77 -> 0, 78 -> 0, 79 -> 0, 80 -> 0, 81 -> 0, 82 -> 0, 83 -> 0, 84 -> 0, 85 -> 0, 86 -> 0, 87 -> 0, 88 -> 0, 89 -> 0, 90 -> 0, 91 -> 0, 92 -> 0, 93 -> 0, 94 -> 0, 95 -> 0, 96 -> 0, 97 -> 0, 98 -> 0, 99 -> 0, 100 -> 0, 101 -> 0, 102 -> 0, 103 -> 0, 104 -> 0, 105 -> 0, 106 -> 0, 107 -> 0, 108 -> 0, 109 -> 0, 110 -> 0, 111 -> 0, 112 -> 0, 113 -> 0, 114 -> 0, 115 -> 0, 116 -> 0, 117 -> 0, 118 -> 0, 119 -> 0, 120 -> 0, 121 -> 0, 122 -> 0, 123 -> 0, 124 -> 0, 125 -> 0, 126 -> 0, 127 -> 0, 128 -> 0, 129 -> 0, 130 -> 0, 131 -> 0, 132 -> 0, 133 -> 0, 134 -> 0, 135 -> 0, 136 -> 0, 137 -> 0, 138 -> 0, 139 -> 0, 140 -> 0, 141 -> 0, 142 -> 0, 143 -> 0, 144 -> 0, 145 -> 0, 146 -> 0, 147 -> 0, 148 -> 0, 149 -> 0, 150 -> 0, 151 -> 0, 152 -> 0, 153 -> 0, 154 -> 0, 155 -> 0, 156 -> 0, 157 -> 0, 158 -> 0, 159 -> 0, 160 -> 0, 161 -> 0, 162 -> 0, 163 -> 0, 164 -> 0, 165 -> 0, 166 -> 0, 167 -> 0, 168 -> 0, 169 -> 0, 170 -> 0, 171 -> 0, 172 -> 0, 173 -> 0, 174 -> 0, 175 -> 0, 176 -> 0, 177 -> 0, 178 -> 0, 179 -> 0, 180 -> 0, 181 -> 0, 182 -> 0, 183 -> 0, 184 -> 0, 185 -> 0, 186 -> 0, 187 -> 0, 188 -> 0, 189 -> 0\n" + ] + } + ], + "source": [ + "scheduler_str = []\n", + "for state in leader_model.states:\n", + " choice = scheduler.get_choice(state)\n", + " action = choice.get_deterministic_choice()\n", + " scheduler_str.append(f\"{state} -> {action}\")\n", + "print(\", \".join(scheduler_str))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "stormvogel_model = mapping.stormpy_to_stormvogel(leader_model)\n", + "\n", + "stormvogel_result = result.convert_model_checking_result(stormvogel_model, stormpy_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-------------------------------------------------------------- \n", - "Model type: \tDTMC (sparse)\n", - "States: \t49\n", - "Transitions: \t56\n", - "Reward Models: rounds\n", - "State Labels: \t3 labels\n", - " * elected -> 0 item(s)\n", - " * init -> 1 item(s)\n", - " * deadlock -> 3 item(s)\n", - "Choice Labels: \tnone\n", - "-------------------------------------------------------------- \n", - "\n" - ] - } - ], - "source": [ - "scheduler = stormpy_result.scheduler\n", - "\n", - "print(leader_model.apply_scheduler(scheduler))" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec0aa36b544d42a798016fcbe2415505", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import stormvogel.layout\n", + "\n", + "vis1 = show.show(stormvogel_model, stormvogel_result, layout=stormvogel.layout.EXPLORE(), show_editor=False)" + ] + }, + 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"left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/docs/getting_started/naive_value_iteration.ipynb b/docs/getting_started/naive_value_iteration.ipynb index 963ced2..5908418 100644 --- a/docs/getting_started/naive_value_iteration.ipynb +++ b/docs/getting_started/naive_value_iteration.ipynb @@ -1,1301 +1,1393 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "9bdef9ae-fb41-4b72-924b-93077f182178", - "metadata": {}, - "source": [ - "# Naive value iteration, using a hungry lion\n", - "We start by creating a model of a lion. Whenever it gets hungry, it will go hunting and hopefully catch a prey. However, if it fails, it might starve to death..." - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "9bdef9ae-fb41-4b72-924b-93077f182178", + "metadata": {}, + "source": [ + "# Naive value iteration, using a hungry lion\n", + "We start by creating a model of a lion. Whenever it gets hungry, it will go hunting and hopefully catch a prey. However, if it fails, it might starve to death..." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7f3692d1-ad64-449d-809c-01ceb84e6bb4", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "id": "7f3692d1-ad64-449d-809c-01ceb84e6bb4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/html": [ + "\n", + " " ], - "source": [ - "from stormvogel.model import Model, ModelType\n", - "\n", - "lion = Model(name=\"lion\", model_type=ModelType.DTMC)\n", - "init = lion.get_initial_state()\n", - "full = lion.new_state(\"full :D\")\n", - "satisfied = lion.new_state(\"satisfied :)\")\n", - "hungry = lion.new_state(\"hungry :(\")\n", - "starving = lion.new_state(\"starving :((\")\n", - "rawr = lion.new_state(\"rawr\")\n", - "hunt = lion.new_state(\"hunt >:D\")\n", - "desperate_hunt = lion.new_state(\"desparate hunt!\")\n", - "dead = lion.new_state(\"dead...\")\n", - "\n", - "init.set_transitions([(1, satisfied)])\n", - "satisfied.set_transitions([(1, hungry)])\n", - "hungry.set_transitions([(1, hunt)])\n", - "starving.set_transitions([(1, desperate_hunt)])\n", - "hunt.set_transitions([(0.5, full), (0.3, starving), (0.2, satisfied)])\n", - "full.set_transitions([(1, satisfied)])\n", - "desperate_hunt.set_transitions([(0.2, full), (0.3, starving), (0.1, dead), (0.4, satisfied)])\n", - "lion.add_self_loops()\n", - "\n", - "from stormvogel.show import show\n", - "from stormvogel.layout import Layout\n", - "vis = show(lion, layout=Layout(\"layouts/lion.json\"), name=\"lion\", save_and_embed=True)" + "text/plain": [ + "" ] - }, - { - "cell_type": "markdown", - "id": "1c09fb4a-1fe9-4f76-982a-1eacd0b46a87", - "metadata": {}, - "source": [ - "Here we provide an implementation of naive value iteration using the model API." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8807425f-1d69-4522-9077-b902c90bd0b2", - "metadata": {}, - "outputs": [], - "source": [ - "def naive_value_iteration(\n", - " model: stormvogel.model.Model, steps: int, starting_state: stormvogel.model.State\n", - ") -> list[list[float]]:\n", - " \"\"\"Run naive value iteration. The result is a 2D list where result[n][m] is the probability to be in state m at step n.\n", - "\n", - " Args:\n", - " model (stormvogel.model.Model): Target model.\n", - " steps (int): Amount of steps.\n", - " starting_state (stormvogel.model.State): Starting state.\n", - "\n", - " Returns:\n", - " list[list[float]]: The result is a 2D list where result[n][m] is the probability to be in state m at step n.\n", - " \"\"\"\n", - " if steps < 2:\n", - " RuntimeError(\"Need at least two steps\")\n", - " if model.type != stormvogel.model.ModelType.DTMC:\n", - " RuntimeError(\"Only works for DTMC\")\n", - "\n", - " # Create a matrix and set the value for the starting state to 1 on the first step.\n", - " matrix_steps_states = [[0.0 for s in model.states] for x in range(steps)]\n", - " matrix_steps_states[0][starting_state.id] = 1\n", - "\n", - " # Apply the updated values for each step.\n", - " for current_step in range(steps - 1):\n", - " next_step = current_step + 1\n", - " for s_id, s in model.get_states().items():\n", - " branch = model.get_branch(s)\n", - " for transition_prob, target in branch.branch:\n", - " current_prob = matrix_steps_states[current_step][s_id]\n", - " matrix_steps_states[next_step][target.id] += current_prob * float(\n", - " transition_prob\n", - " )\n", - "\n", - " return matrix_steps_states\n", - " " - ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from stormvogel.model import Model, ModelType, Branch, Transition\n", + "\n", + "lion = Model(name=\"lion\", model_type=ModelType.MDP)\n", + "init = lion.get_initial_state()\n", + "full = lion.new_state(\"full :D\")\n", + "satisfied = lion.new_state(\"satisfied :)\")\n", + "hungry = lion.new_state(\"hungry :(\")\n", + "starving = lion.new_state(\"starving :((\")\n", + "dead = lion.new_state(\"dead...\")\n", + "\n", + "hunt = lion.new_action(\"hunt >:D\")\n", + "rawr = lion.new_action(\"rawr\")\n", + "\n", + "init.set_transitions([(1, satisfied)])\n", + "\n", + "full.set_transitions(Transition({\n", + " hunt: Branch([\n", + " (0.5, satisfied),\n", + " (0.5, full),\n", + " ]),\n", + " rawr: Branch([\n", + " (0.9, full),\n", + " (0.1, satisfied)\n", + " ])\n", + "}))\n", + "\n", + "satisfied.set_transitions(Transition({\n", + " hunt: Branch([\n", + " (0.5, satisfied),\n", + " (0.3, full),\n", + " (0.2, hungry)\n", + " ]),\n", + " rawr: Branch([\n", + " (0.9, satisfied),\n", + " (0.1, hungry)\n", + " ])\n", + "}))\n", + "\n", + "hungry.set_transitions(Transition({\n", + " hunt: Branch([\n", + " (0.2, full),\n", + " (0.5, satisfied),\n", + " (0.1, hungry),\n", + " (0.2, starving)\n", + " ]),\n", + " rawr: Branch([\n", + " (0.9, hungry),\n", + " (0.1, starving)\n", + " ])\n", + "}))\n", + "\n", + "starving.set_transitions(Transition({\n", + " hunt: Branch([\n", + " (0.1, full),\n", + " (0.5, satisfied),\n", + " (0.2, hungry),\n", + " (0.2, dead)\n", + " ]),\n", + " rawr: Branch([\n", + " (0.9, starving),\n", + " (0.1, dead)\n", + " ])\n", + "}))\n", + "lion.add_self_loops()\n", + "\n", + "from stormvogel.show import show\n", + "from stormvogel.layout import Layout\n", + "vis = show(lion, layout=Layout(\"layouts/lion.json\"), name=\"lion\", save_and_embed=True)" + ] + }, + { + "cell_type": "markdown", + "id": "1c09fb4a-1fe9-4f76-982a-1eacd0b46a87", + "metadata": {}, + "source": [ + "Here we provide an implementation of naive value iteration using the model API." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8807425f-1d69-4522-9077-b902c90bd0b2", + "metadata": {}, + "outputs": [], + "source": [ + "import stormvogel.model\n", + "\n", + "def naive_value_iteration(\n", + " model: stormvogel.model.Model, epsilon: float, target_state: stormvogel.model.State\n", + ") -> list[list[float]]:\n", + " \"\"\"Run naive value iteration. The result is a 2D list where result[n][m] is the probability to be in state m at step n.\n", + "\n", + " Args:\n", + " model (stormvogel.model.Model): Target model.\n", + " steps (int): Amount of steps.\n", + " target_state (stormvogel.model.State): Target state of the model.\n", + "\n", + " Returns:\n", + " list[list[float]]: The result is a 2D list where result[n][m] is the value of state m at iteration n.\n", + " \"\"\"\n", + " if epsilon <= 0:\n", + " RuntimeError(\"The algorithm will not terminate if epsilon is zero.\")\n", + "\n", + " # Create a dynamic matrix (list of lists) to store the result.\n", + " values_matrix = [[0 for state in model.get_states()]]\n", + " values_matrix[0][target_state.id] = 1\n", + "\n", + " terminate = False\n", + " while not terminate:\n", + " old_values = values_matrix[len(values_matrix) - 1]\n", + " new_values = [None for state in model.get_states()]\n", + " for sid, state in model.get_states().items():\n", + " transitions = model.get_transitions(state)\n", + " # Now we have to take a decision for an action.\n", + " actions = transitions.transition.keys()\n", + " action_values = {}\n", + " for action, branch in transitions.transition.items():\n", + " branch_value = sum([prob * old_values[state.id] for (prob, state) in branch.branch])\n", + " action_values[action] = branch_value\n", + " # We take the action with the highest value.\n", + " highest_value = max(action_values.values())\n", + " new_values[sid] = highest_value\n", + " values_matrix.append(new_values)\n", + " terminate = sum([abs(x-y) for (x, y) in zip(new_values, old_values)]) < epsilon\n", + " return values_matrix\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "46c998da-b579-4fc8-b385-7b1b07582359", + "metadata": {}, + "source": [ + "We apply naive value iteration on our lion model with 20 steps. Then we display the result of the as a heatmap, using matplotlib." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a606dc3e-5d22-45a2-8234-30d3fe8b6bc5", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "46c998da-b579-4fc8-b385-7b1b07582359", - "metadata": {}, - "source": [ - "We apply naive value iteration on our lion model with 20 steps. Then we display the result of the as a heatmap, using matplotlib." + "data": { + "image/png": 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", + "text/plain": [ + "" ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a606dc3e-5d22-45a2-8234-30d3fe8b6bc5", - "metadata": {}, - "outputs": [ + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import stormvogel.lib\n", + "res = naive_value_iteration(lion, 0.003, full)\n", + "labels = [s.labels[0] for s in lion.get_states().values()]\n", + "stormvogel.lib.display_value_iteration_result(res, 10, labels)" + ] + }, + { + "cell_type": "markdown", + "id": "dcbef4f2-78ae-4d2c-9587-9c96becf26b1", + "metadata": {}, + "source": [ + "Note that naive_value_iteration is also available under stormvogel.lib, in case you would like to use it later. However, this implementation is very inefficient so we recommend using the value iteration algorithms from stormpy if you want good performance." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "12b57306-780c-4a40-898a-f3a95f7ff9eb", + "metadata": {}, + "outputs": [], + "source": [ + "import stormvogel.lib\n", + "res2 = stormvogel.lib.naive_value_iteration(lion, 0.003, full)\n", + "assert res == res2" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "0a4470793a704b388de44c9e5f08cab8": { + "model_module": "@jupyter-widgets/output", + 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "data": { + "text/html": "\n ", + "text/plain": "" + }, + "metadata": {}, + "output_type": "display_data" } - ], - "source": [ - "res = naive_value_iteration(lion, 20, starting_state=lion.get_initial_state())\n", - "labels = [s.labels[0] for s in lion.get_states().values()]\n", - "stormvogel.lib.display_value_iteration_result(res, 10, labels)" - ] - }, - { - "cell_type": "markdown", - "id": "dcbef4f2-78ae-4d2c-9587-9c96becf26b1", - "metadata": {}, - "source": [ - "Note that naive_value_iteration is also available under stormvogel.lib, in case you would like to use it later. However, this implementation is very inefficient so we recommend using the value iteration algorithms from stormpy if you want good performance." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "12b57306-780c-4a40-898a-f3a95f7ff9eb", - "metadata": {}, - "outputs": [], - "source": [ - "import stormvogel.lib\n", - "res2 = stormvogel.lib.naive_value_iteration(lion, 20, starting_state=lion.get_initial_state())\n", - "assert res == res2" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": { - "0a4470793a704b388de44c9e5f08cab8": { - "model_module": 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b/docs/getting_started/study.html index c5d78df..23d014c 100644 --- a/docs/getting_started/study.html +++ b/docs/getting_started/study.html @@ -1,6 +1,6 @@ + > \ No newline at end of file diff --git a/docs/getting_started/study.ipynb b/docs/getting_started/study.ipynb index 7964016..497bd21 100644 --- a/docs/getting_started/study.ipynb +++ b/docs/getting_started/study.ipynb @@ -1,13301 +1,13295 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "31fda09b", - "metadata": {}, - "source": [ - "# The studying dilemma" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e924f3a4", - "metadata": {}, - "outputs": [], - "source": [ - "import stormvogel.model\n", - "import stormvogel.visualization\n", - "from stormvogel.layout import Layout\n", - "from stormvogel.show import show" - ] - }, - { - "cell_type": "markdown", - "id": "0b09222b", - "metadata": {}, - "source": [ - "Let's create an MDP and add all states:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1f2120a8", - "metadata": {}, - "outputs": [], - "source": [ - "mdp = stormvogel.model.new_mdp(\"Monty Hall\")\n", - "\n", - "init = mdp.get_initial_state()\n", - "study = mdp.action(\"study\")\n", - "not_study = mdp.action(\"don't study\")\n", - "\n", - "studied = mdp.new_state(\"studied\")\n", - "not_studied = mdp.new_state(\"didn't study\")\n", - "pass_test = mdp.new_state(\"pass test\")\n", - "fail_test = mdp.new_state(\"fail test\")\n", - "end = mdp.new_state(\"end\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1143b6c0", - "metadata": {}, - "outputs": [], - "source": [ - "init.set_transitions([\n", - " (study, studied),\n", - " (not_study, not_studied)\n", - "])" - ] - }, - { - "cell_type": "markdown", - "id": "1db64312", - "metadata": {}, - "source": [ - "If you studied, then there is a 90% chance that you pass the test." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "64bfb789", - "metadata": {}, - "outputs": [], - "source": [ - "studied.set_transitions([\n", - " (9/10, pass_test),\n", - " (1/10, fail_test)\n", - "])" - ] - }, - { - "cell_type": "markdown", - "id": "600839f5", - "metadata": {}, - "source": [ - "If you did not study, then there is only a 40% chance that you pass the test." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "89d1d749", - "metadata": {}, - "outputs": [], - "source": [ - "not_studied.set_transitions([\n", - " (4/10, pass_test),\n", - " (6/10, fail_test)\n", - "])" - ] - }, - { - "cell_type": "markdown", - "id": "b0c4965f", - "metadata": {}, - "source": [ - "After the test, you are done" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2f8fb42e", - "metadata": {}, - "outputs": [], - "source": [ - "pass_test.set_transitions([(1, end)])\n", - "fail_test.set_transitions([(1, end)])" - ] - }, - { - "cell_type": "markdown", - "id": "14d7ab92", - "metadata": {}, - "source": [ - "We'll add some rewards for passing the test (nice!) and for not studying (had some free time)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bfcbb7fa", - "metadata": {}, - "outputs": [], - "source": [ - "reward_model = mdp.add_rewards(\"R\")\n", - "reward_model.set(pass_test, 100)\n", - "reward_model.set(fail_test, 0)\n", - "reward_model.set(not_studied, 15)" - ] - }, - { - "cell_type": "markdown", - "id": "797e09bb", - "metadata": {}, - "source": [ - "We'll decorate our model with a nice layout. Set show_editor to True to change how the model looks." - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "31fda09b", + "metadata": {}, + "source": [ + "# The studying dilemma" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e924f3a4", + "metadata": {}, + "outputs": [], + "source": [ + "import stormvogel.model\n", + "import stormvogel.visualization\n", + "from stormvogel.layout import Layout\n", + "from stormvogel.show import show" + ] + }, + { + "cell_type": "markdown", + "id": "0b09222b", + "metadata": {}, + "source": [ + "Let's create an MDP and add all states:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1f2120a8", + "metadata": {}, + "outputs": [], + "source": [ + "mdp = stormvogel.model.new_mdp(\"Monty Hall\")\n", + "\n", + "init = mdp.get_initial_state()\n", + "study = mdp.action(\"study\")\n", + "not_study = mdp.action(\"don't study\")\n", + "\n", + "studied = mdp.new_state(\"studied\")\n", + "not_studied = mdp.new_state(\"didn't study\")\n", + "pass_test = mdp.new_state(\"pass test\")\n", + "fail_test = mdp.new_state(\"fail test\")\n", + "end = mdp.new_state(\"end\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1143b6c0", + "metadata": {}, + "outputs": [], + "source": [ + "init.set_transitions([\n", + " (study, studied),\n", + " (not_study, not_studied)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "1db64312", + "metadata": {}, + "source": [ + "If you studied, then there is a 90% chance that you pass the test." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "64bfb789", + "metadata": {}, + "outputs": [], + "source": [ + "studied.set_transitions([\n", + " (9/10, pass_test),\n", + " (1/10, fail_test)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "600839f5", + "metadata": {}, + "source": [ + "If you did not study, then there is only a 40% chance that you pass the test." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "89d1d749", + "metadata": {}, + "outputs": [], + "source": [ + "not_studied.set_transitions([\n", + " (4/10, pass_test),\n", + " (6/10, fail_test)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "b0c4965f", + "metadata": {}, + "source": [ + "After the test, you are done" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2f8fb42e", + "metadata": {}, + "outputs": [], + "source": [ + "pass_test.set_transitions([(1, end)])\n", + "fail_test.set_transitions([(1, end)])" + ] + }, + { + "cell_type": "markdown", + "id": "14d7ab92", + "metadata": {}, + "source": [ + "We'll add some rewards for passing the test (nice!) and for not studying (had some free time)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bfcbb7fa", + "metadata": {}, + "outputs": [], + "source": [ + "reward_model = mdp.add_rewards(\"R\")\n", + "reward_model.set(pass_test, 100)\n", + "reward_model.set(fail_test, 0)\n", + 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b/stormvogel/lib.py @@ -1,10 +1,51 @@ -from typing import Any +"""Some example applications of the stormvogel API.""" + +from functools import reduce +from typing import Any, Iterable import stormvogel.model import matplotlib.pyplot as plt - def naive_value_iteration( - model: stormvogel.model.Model, steps: int, starting_state: stormvogel.model.State + model: stormvogel.model.Model, epsilon: float, target_state: stormvogel.model.State +) -> list[list[stormvogel.model.Number]]: + """Run naive value iteration. The result is a 2D list where result[n][m] is the probability to be in state m at step n. + + Args: + model (stormvogel.model.Model): Target model. + steps (int): Amount of steps. + target_state (stormvogel.model.State): Target state of the model. + + Returns: + list[list[float]]: The result is a 2D list where result[n][m] is the value of state m at iteration n. + """ + if epsilon <= 0: + RuntimeError("The algorithm will not terminate if epsilon is zero.") + + # Create a dynamic matrix (list of lists) to store the result. + values_matrix = [[0 for state in model.get_states()]] + values_matrix[0][target_state.id] = 1 + + terminate = False + while not terminate: + old_values = values_matrix[len(values_matrix) - 1] + new_values = [None for state in model.get_states()] + for sid, state in model.get_states().items(): + transitions = model.get_transitions(state) + # Now we have to take a decision for an action. + actions = transitions.transition.keys() + action_values = {} + for action, branch in transitions.transition.items(): + branch_value = sum([prob * old_values[state.id] for (prob, state) in branch.branch]) # type: ignore + action_values[action] = branch_value + # We take the action with the highest value. + highest_value = max(action_values.values()) + new_values[sid] = highest_value + values_matrix.append(new_values) # type: ignore + terminate = sum([abs(x-y) for (x, y) in zip(new_values, old_values)]) < epsilon # type: ignore + return values_matrix # type: ignore + +def dtmc_evolution( + model: stormvogel.model.Model, steps: int ) -> list[list[float]]: """Run naive value iteration. The result is a 2D list where result[n][m] is the probability to be in state m at step n. @@ -23,7 +64,7 @@ def naive_value_iteration( # Create a matrix and set the value for the starting state to 1 on the first step. matrix_steps_states = [[0.0 for s in model.states] for x in range(steps)] - matrix_steps_states[0][starting_state.id] = 1 + matrix_steps_states[0][model.get_initial_state().id] = 1 # Apply the updated values for each step. for current_step in range(steps - 1):
Mass controls degree of repulsion of nodes.
Font