{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import NearestCentroid as skNearestCentroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NearestCentroid():\n",
    "    def fit(self, X, y):\n",
    "        self.classes_ = np.unique(y)\n",
    "        self.centroids_ = np.zeros((len(self.classes_), X.shape[1]))\n",
    "        for i, c in enumerate(self.classes_):\n",
    "            self.centroids_[i] = np.mean(X[y == c], axis=0)\n",
    "        return self\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.classes_[np.argmin(cdist(X, self.centroids_), axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_iris(return_X_y=True)\n",
    "X = StandardScaler().fit_transform(X)\n",
    "clf1 = NearestCentroid().fit(X, y)\n",
    "clf2 = skNearestCentroid().fit(X, y)\n",
    "assert np.allclose(clf1.centroids_, clf2.centroids_)\n",
    "pred1 = clf1.predict(X)\n",
    "pred2 = clf2.predict(X)\n",
    "assert np.array_equal(pred1, pred2)"
   ]
  }
 ],
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