mirror of
https://github.com/apachecn/ailearning.git
synced 2026-02-03 10:24:39 +08:00
113 lines
3.7 KiB
Plaintext
113 lines
3.7 KiB
Plaintext
{
|
||
"metadata": {
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.3-final"
|
||
},
|
||
"orig_nbformat": 2,
|
||
"kernelspec": {
|
||
"name": "python_defaultSpec_1599819467604",
|
||
"display_name": "Python 3.6.3 64-bit ('python3.6': virtualenv)"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2,
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from numpy import linalg as la\n",
|
||
"from numpy import *"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def loadExData3():\n",
|
||
" # 利用SVD提高推荐效果，菜肴矩阵\n",
|
||
" return[[2, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0],\n",
|
||
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n",
|
||
" [0, 0, 0, 0, 0, 0, 0, 1, 0, 4, 0],\n",
|
||
" [3, 3, 4, 0, 3, 0, 0, 2, 2, 0, 0],\n",
|
||
" [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n",
|
||
" [0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n",
|
||
" [4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 5],\n",
|
||
" [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 4],\n",
|
||
" [0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n",
|
||
" [0, 0, 0, 3, 0, 0, 0, 0, 4, 5, 0],\n",
|
||
" [1, 1, 2, 1, 1, 2, 1, 0, 4, 5, 0]]\n",
|
||
"\n",
|
||
"myMat = mat(loadExData3())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": "matrix([[2, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 0, 0, 1, 0, 4, 0],\n [3, 3, 4, 0, 3, 0, 0, 2, 2, 0, 0],\n [5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n [4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 5],\n [0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 4],\n [0, 0, 0, 0, 0, 0, 5, 0, 0, 5, 0],\n [0, 0, 0, 3, 0, 0, 0, 0, 4, 5, 0],\n [1, 1, 2, 1, 1, 2, 1, 0, 4, 5, 0]])"
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 3
|
||
}
|
||
],
|
||
"source": [
|
||
"myMat"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def recommend(dataMat, user, N=3, simMeas=cosSim, estMethod=standEst):\n",
|
||
" \"\"\"svdEst( )\n",
|
||
" Args:\n",
|
||
" dataMat 训练数据集\n",
|
||
" user 用户编号\n",
|
||
" simMeas 相似度计算方法\n",
|
||
" estMethod 使用的推荐算法\n",
|
||
" Returns:\n",
|
||
" 返回最终 N 个推荐结果\n",
|
||
" \"\"\"\n",
|
||
" # 寻找未评级的物品\n",
|
||
" # 对给定的用户建立一个未评分的物品列表\n",
|
||
" \n",
|
||
" unratedItems = nonzero(dataMat[user, :].A == 0)[1]\n",
|
||
" # 如果不存在未评分物品，那么就退出函数\n",
|
||
" if len(unratedItems) == 0:\n",
|
||
" return 'you rated everything'\n",
|
||
" # 物品的编号和评分值\n",
|
||
" itemScores = []\n",
|
||
" # 在未评分物品上进行循环\n",
|
||
" for item in unratedItems:\n",
|
||
" # 获取 item 该物品的评分\n",
|
||
" estimatedScore = estMethod(dataMat, user, simMeas, item)\n",
|
||
" itemScores.append((item, estimatedScore))\n",
|
||
" # 按照评分得分 进行逆排序，获取前N个未评级物品进行推荐\n",
|
||
" return sorted(itemScores, key=lambda jj: jj[1], reverse=True)[: N]\n",
|
||
"\n",
|
||
"\n",
|
||
"print(recommend(myMat, 1, estMethod=svdEst))"
|
||
]
|
||
}
|
||
]
|
||
} |