cmdstan_rng_issue.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cmdstanpy.model as cmdstan_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cmdstan_model._find_cmdstan(path='~/cmdstan-2.18.1/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_1 = cmdstan_model.Model(\"\"\"\n",
    "generated quantities {\n",
    "    vector[10] x;\n",
    "    for (i in 1:10)\n",
    "        x[i] = normal_rng(0, 1);\n",
    "}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fit_1 = model_1.sample(\n",
    "    algorithm='fixed_param',\n",
    "    num_warmup=0,\n",
    "    num_samples=1,\n",
    "    chains=1,\n",
    "    seed=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accept_stat__': array([ 0.]),\n",
       " 'lp__': array([ 0.]),\n",
       " 'x': array([[ 0.511159, -2.02228 ,  1.53777 , -0.917215,  1.69472 , -1.39996 ,\n",
       "         -0.200255,  0.430888, -0.819349,  0.946784]])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_1.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_2 = cmdstan_model.Model(\"\"\"\n",
    "generated quantities {\n",
    "    vector[10] x;\n",
    "    for (i in 1:10)\n",
    "        x[i] = normal_rng(0, 1);\n",
    "}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fit_2 = model_1.sample(\n",
    "    algorithm='fixed_param',\n",
    "    num_warmup=0,\n",
    "    num_samples=1,\n",
    "    chains=1,\n",
    "    seed=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accept_stat__': array([ 0.]),\n",
       " 'lp__': array([ 0.]),\n",
       " 'x': array([[-0.00505226,  1.78455   , -2.02283   , -0.297191  , -0.904203  ,\n",
       "         -1.25453   , -0.0316722 ,  0.228872  ,  0.718348  , -0.684812  ]])}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_2.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Users/rndpolytope/.cache/cmdstanpy/model-e4c22992', 'id=98540', 'sample', 'algorithm=fixed_param', 'num_warmup=0', 'num_samples=1', 'output', 'file=/var/folders/ly/_ltwkx7n73q9s1xfz8720mvc0000gn/T/tmpycedgg_4/output.csv', 'refresh=1']\n"
     ]
    }
   ],
   "source": [
    "print(fit_1.cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Users/rndpolytope/.cache/cmdstanpy/model-e4c22992', 'id=98540', 'sample', 'algorithm=fixed_param', 'num_warmup=0', 'num_samples=1', 'output', 'file=/var/folders/ly/_ltwkx7n73q9s1xfz8720mvc0000gn/T/tmp6gzzt466/output.csv', 'refresh=1']\n"
     ]
    }
   ],
   "source": [
    "print(fit_2.cmd)"
   ]
  }
 ],
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