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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# 使用 Sklearn 进行特征缩放操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.        ]\n",
      " [0.41666667]\n",
      " [1.        ]]\n"
     ]
    }
   ],
   "source": [
    "weights = numpy.array([[115],[140],[175]])\n",
    "scaler = MinMaxScaler()\n",
    "# fit 会找到 min 值和 max 值，而 transform 会把数据转换到这个范围内\n",
    "# 所以使用 fit_transform 相当于同时做了两件事\n",
    "rescaled_weights = scaler.fit_transform(weights)\n",
    "print(rescaled_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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