day 09 - monte carlo - pi.ipynb

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    "import numpy as np"
   ]
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   "source": [
    "## algorithm"
   ]
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   "source": [
    "def pi(n, batch=1000):\n",
    "    t = 0\n",
    "    for i in range(n // batch):\n",
    "        p = np.random.rand(batch, 2)\n",
    "        p = (p * p).sum(axis=1)\n",
    "        t += (p <= 1).sum()\n",
    "    return 4 * t / n"
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  },
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   "metadata": {},
   "source": [
    "## run"
   ]
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       "3.1400000000000001"
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     "execution_count": 3,
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   "source": [
    "pi(10 ** 3)"
   ]
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   "execution_count": 4,
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       "3.1418360000000001"
      ]
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     "execution_count": 4,
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   "source": [
    "pi(10 ** 6)"
   ]
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   "execution_count": 5,
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    {
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       "3.14145728"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pi(10 ** 8)"
   ]
  },
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