├── CAICT
├── icingPredict.ipynb
└── index
├── README.md
└── phm2017
├── index
├── phm2017.ipynb
└── phm2017.rar
/CAICT/icingPredict.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 1,
6 | "metadata": {
7 | "collapsed": false,
8 | "scrolled": false
9 | },
10 | "outputs": [
11 | {
12 | "name": "stdout",
13 | "output_type": "stream",
14 | "text": [
15 | "d:\\0python\\PHM\\CAICT2017\n"
16 | ]
17 | }
18 | ],
19 | "source": [
20 | "cd d:\\0python\\PHM\\CAICT2017"
21 | ]
22 | },
23 | {
24 | "cell_type": "markdown",
25 | "metadata": {},
26 | "source": [
27 | "## 导入包"
28 | ]
29 | },
30 | {
31 | "cell_type": "code",
32 | "execution_count": 2,
33 | "metadata": {
34 | "collapsed": false
35 | },
36 | "outputs": [],
37 | "source": [
38 | "import numpy as np\n",
39 | "import matplotlib.pyplot as plt\n",
40 | "import pandas as pd\n",
41 | "import datetime as dt\n",
42 | "from sklearn.neighbors import KNeighborsClassifier\n",
43 | "from sklearn.model_selection import cross_val_score\n",
44 | "from sklearn.svm import SVC\n",
45 | "from sklearn.model_selection import train_test_split\n",
46 | "import tensorflow as tf"
47 | ]
48 | },
49 | {
50 | "cell_type": "markdown",
51 | "metadata": {},
52 | "source": [
53 | "## 导入数据"
54 | ]
55 | },
56 | {
57 | "cell_type": "code",
58 | "execution_count": 3,
59 | "metadata": {
60 | "collapsed": true
61 | },
62 | "outputs": [],
63 | "source": [
64 | "dataIn15 = pd.read_csv('train/15/15_data.csv')\n",
65 | "normalPer15 = pd.read_csv('train/15/15_normalInfo.csv')\n",
66 | "failurePer15 = pd.read_csv('train/15/15_failureInfo.csv')\n",
67 | "timeIn15 = dataIn15['time']\n",
68 | "dataIn21 = pd.read_csv('train/21/21_data.csv')\n",
69 | "normalPer21 = pd.read_csv('train/21/21_normalInfo.csv')\n",
70 | "failurePer21 = pd.read_csv('train/21/21_failureInfo.csv')\n",
71 | "timeIn21 = dataIn21['time']\n",
72 | "dataIn08 = pd.read_csv('test/08/08_data.csv')"
73 | ]
74 | },
75 | {
76 | "cell_type": "code",
77 | "execution_count": 4,
78 | "metadata": {
79 | "collapsed": false
80 | },
81 | "outputs": [
82 | {
83 | "data": {
84 | "text/html": [
85 | "\n",
86 | "\n",
99 | "
\n",
100 | " \n",
101 | " \n",
102 | " | \n",
103 | " startTime | \n",
104 | " endTime | \n",
105 | "
\n",
106 | " \n",
107 | " \n",
108 | " \n",
109 | " | 0 | \n",
110 | " 2015-11-01 20:20:16 | \n",
111 | " 2015-11-03 23:47:32 | \n",
112 | "
\n",
113 | " \n",
114 | " | 1 | \n",
115 | " 2015-11-04 14:04:51 | \n",
116 | " 2015-11-04 18:26:08 | \n",
117 | "
\n",
118 | " \n",
119 | " | 2 | \n",
120 | " 2015-11-05 11:06:59 | \n",
121 | " 2015-11-09 02:44:31 | \n",
122 | "
\n",
123 | " \n",
124 | " | 3 | \n",
125 | " 2015-11-09 12:30:09 | \n",
126 | " 2015-11-09 19:23:32 | \n",
127 | "
\n",
128 | " \n",
129 | " | 4 | \n",
130 | " 2015-11-10 00:04:50 | \n",
131 | " 2015-11-15 23:03:06 | \n",
132 | "
\n",
133 | " \n",
134 | "
\n",
135 | "