rms benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data = np.random.normal(size=(100, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 µs ± 59.3 ns per loop (mean ± std. dev. of 10 runs, 100000 loops each)\n",
      "16.1 µs ± 252 ns per loop (mean ± std. dev. of 10 runs, 100000 loops each)\n",
      "13 µs ± 1.33 µs per loop (mean ± std. dev. of 10 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r 10 -n 100000 np.sqrt(np.mean(np.square(data)))\n",
    "%timeit -r 10 -n 100000 np.linalg.norm(data.flat, ord=2) / np.sqrt(data.size)\n",
    "%timeit -r 10 -n 100000 np.linalg.norm(data.ravel(), ord=2) / np.sqrt(data.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thumbs up!\n"
     ]
    }
   ],
   "source": [
    "assert np.allclose(np.sqrt(np.mean(np.square(data))), np.linalg.norm(data.ravel(), ord=2) / np.sqrt(data.size))\n",
    "print(\"thumbs up!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.8 µs ± 69.1 ns per loop (mean ± std. dev. of 10 runs, 100000 loops each)\n",
      "21.3 µs ± 92 ns per loop (mean ± std. dev. of 10 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r 10 -n 100000 np.sqrt(np.mean(np.square(data), axis=0))\n",
    "%timeit -r 10 -n 100000 np.linalg.norm(data, ord=2, axis=0) / np.sqrt(data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thumbs up!\n"
     ]
    }
   ],
   "source": [
    "assert np.allclose(np.sqrt(np.mean(np.square(data), axis=0)), np.linalg.norm(data, ord=2, axis=0) / np.sqrt(data.shape[0]))\n",
    "print(\"thumbs up!\")"
   ]
  }
 ],
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