num2range.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def number_normalization(source_num, source_min_num, source_max_num, target_min_num=0, target_max_num=1):\n",
    "    dataset = np.array([source_min_num, source_num, source_max_num]).reshape(-1, 1)\n",
    "    my_scaler = MinMaxScaler(feature_range=(target_min_num, target_max_num))  # 10分制\n",
    "    new_dataset = my_scaler.fit_transform(dataset)\n",
    "    target_num = new_dataset[1][0]\n",
    "    return target_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_score = 17\n",
    "source_min_score = -8\n",
    "source_max_score = 42\n",
    "target_min_score = 0\n",
    "target_max_score = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_score = number_normalization(source_score, source_min_score, source_max_score,\n",
    "                                    target_min_score, target_max_score)\n",
    "target_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = np.array([source_min_score, source_score, source_max_score]).reshape(-1, 1)\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(target_min_score, target_max_score))  # 10分制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-8],\n",
       "       [17],\n",
       "       [42]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0. ],\n",
       "       [0.5],\n",
       "       [1. ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset = scaler.fit_transform(dataset)\n",
    "new_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dataset[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_score = new_dataset[1][0]\n",
    "target_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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