{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import Normalizer as skNormalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Normalizer():\n",
    "    def __init__(self, norm='l2'):\n",
    "        self.norm = norm\n",
    "\n",
    "    def fit(self, X):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        if norm == 'l1':\n",
    "            norms = np.sum(np.abs(X), axis=1)\n",
    "        elif norm == 'l2':\n",
    "            norms = np.sqrt(np.sum(np.square(X), axis=1))\n",
    "        elif norm == 'max':\n",
    "            norms = np.max(X, axis=1)\n",
    "        return X / norms[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, _ = load_iris(return_X_y=True)\n",
    "for norm in ['l1', 'l2', 'max']:\n",
    "    sc1 = Normalizer(norm=norm).fit(X)\n",
    "    sc2 = skNormalizer(norm=norm).fit(X)\n",
    "    Xt1 = sc1.transform(X)\n",
    "    Xt2 = sc2.transform(X)\n",
    "    assert np.allclose(Xt1, Xt2)"
   ]
  }
 ],
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