├── .gitignore ├── .vscode └── settings.json ├── LICENSE ├── README.md ├── educoder-tasks ├── 1-机器学习 --- kNN算法 │ ├── t-1.ans.py │ ├── t-1.detail.py │ ├── t-2.ans.py │ └── t-2.detail.py ├── 2-机器学习之kNN算法 │ ├── t-1.md │ ├── t-2.py │ ├── t-3.py │ ├── t-4.py │ └── t-5.py ├── 3-机器学习 --- 感知机 │ ├── t-1.ans.py │ ├── t-1.detail.py │ └── t-2.py ├── 4-机器学习 --- 朴素贝叶斯分类器 │ ├── t-1.md │ ├── t-2.md │ ├── t-3.js │ ├── t-3.py │ ├── t-4.js │ ├── t-4.py │ ├── t-5.js │ └── t-5.py └── 5-机器学习之支持向量机 │ ├── t-1.md │ └── t-2.py └── lab ├── NN ├── index.py └── result.txt ├── data ├── stopwords.txt ├── test.csv ├── train.csv └── train_partial.csv └── svm ├── index.py └── result.txt /.gitignore: -------------------------------------------------------------------------------- 1 | __data__ -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "workbench.colorCustomizations": { 3 | "activityBar.activeBackground": "#7addb3", 4 | "activityBar.activeBorder": "#a867d8", 5 | "activityBar.background": "#7addb3", 6 | "activityBar.foreground": "#15202b", 7 | "activityBar.inactiveForeground": "#15202b99", 8 | "activityBarBadge.background": "#a867d8", 9 | "activityBarBadge.foreground": "#15202b", 10 | "sash.hoverBorder": "#7addb3", 11 | "statusBar.background": "#51d39c", 12 | "statusBar.border": "#51d39c", 13 | "statusBar.foreground": "#15202b", 14 | "statusBarItem.hoverBackground": "#31c084", 15 | "statusBarItem.remoteBackground": "#51d39c", 16 | "statusBarItem.remoteForeground": "#15202b", 17 | "tab.activeBorder": "#7addb3", 18 | "titleBar.activeBackground": "#51d39c", 19 | "titleBar.activeForeground": "#15202b", 20 | "titleBar.border": "#51d39c", 21 | "titleBar.inactiveBackground": "#51d39c99", 22 | "titleBar.inactiveForeground": "#15202b99" 23 | }, 24 | "peacock.color": "#51d39c", 25 | "editor.detectIndentation": false 26 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 xiong35 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 华中科技大学 19 级 机器学习相关作业&课设 2 | 3 | > 据观察每年的作业可能都不一样, 请确认我们的题目是否相同. 课堂为 机器学习 2021 4 | 5 | 说明: `educoder-tasks` 文件夹下: 6 | 7 | - `*.detail.py` 文件是带注释和较为详细说明和测试数据的文件, **不可**直接复制过去跑 8 | - `*.ans.py` 文件为太长不看版, 可以直接复制, 提交即可通过 9 | - 如果只有 py 文件, 说明这题太简单了没啥好说的..... 10 | 11 | **hxd 顺手点个 star 呗 ∠( ᐛ 」∠)\_** 12 | 13 | ## 目录结构 14 | 15 | ```txt 16 | | 17 | +--+ educoder-tasks # educoder 平台上的作业 18 | | 19 | +--+ 1-机器学习 --- kNN算法 20 | | | 21 | | +--- t-1.ans.py # 第1关：实现kNN算法 22 | | +--- t-1.detail.py 23 | | +--- t-2.ans.py # 第2关：红酒分类 24 | | +--- t-2.detail.py 25 | | 26 | +--+ 2-机器学习之kNN算法 27 | | | 28 | | +--- t-1.md # kNN算法原理 29 | | +--- t-2.py # 使用 sklearn 中的kNN算法进行分类 30 | | +--- t-3.py # 使用 sklearn 中的kNN算法进行回归 31 | | +--- t-4.py # 分析红酒数据 32 | | +--- t-5.py # 对数据进行标准化 33 | | # t-6.py (没写) # 使用kNN算法进行预测, 答案同 `../1-机器学习 --- kNN算法/t-2.ans.py` 34 | | 35 | +--+ 3-机器学习 --- 感知机 36 | | | 37 | | +--- t-1.ans.py # 感知机 - 西瓜好坏自动识别 38 | | +--- t-1.detail.py 39 | | +--- t-2.py # scikit-learn感知机实践 - 癌细胞精准识别 40 | | 41 | +--+ 4-机器学习 --- 朴素贝叶斯分类器 42 | | | 43 | | +--- t-1.md # 条件概率 44 | | +--- t-2.md # 贝叶斯公式 45 | | +--- t-3.js # 由于我们老师设置了不可粘贴, 所以搞了个 js 脚本请求修改代码, 以下 js 文件同 46 | | +--- t-3.py # 朴素贝叶斯分类算法流程 47 | | +--- t-4.js 48 | | +--- t-4.py # 拉普拉斯平滑 49 | | +--- t-5.js 50 | | +--- t-5.py # 新闻文本主题分类 51 | | 52 | +--+ 5-机器学习之支持向量机 53 | | 54 | +--- t-1.md # 线性可分支持向量机 55 | +--- t-2.py # 线性支持向量机 56 | ``` 57 | 58 | ## 万字长文(详细到像素), 手把手教你配置环境 59 | 60 | > 如果你只想要答案交作业而不用本地调试可跳过 61 | 62 | 1. 下载并安装 python3.x 63 | 2. pip 下载 `numpy`, `sklearn`, `pandas` 64 | -------------------------------------------------------------------------------- /educoder-tasks/1-机器学习 --- kNN算法/t-1.ans.py: -------------------------------------------------------------------------------- 1 | # encoding=utf8 2 | import numpy as np 3 | 4 | 5 | class kNNClassifier(object): 6 | def __init__(self, k): 7 | self.k = k 8 | self.train_feature = None 9 | self.train_label = None 10 | 11 | def fit(self, feature, label): 12 | self.train_feature = feature 13 | self.train_label = label 14 | 15 | def predict(self, feature): 16 | result = [] 17 | 18 | for feat in feature: 19 | diff = self.train_feature - feat 20 | sq_diff = diff ** 2 21 | dist = sq_diff.sum(axis=1) ** 0.5 22 | 23 | dist_index = dist.argsort() 24 | sorted_labels = self.train_label[dist_index] 25 | 26 | class_count = {} 27 | for i in range(self.k): 28 | label = sorted_labels[i] 29 | class_count[label] = class_count.get(label, 0) + 1 30 | sorted_class_count = sorted( 31 | class_count.items(), key=lambda x: x[1], reverse=True) 32 | result.append(sorted_class_count[0][0]) 33 | 34 | return result 35 | -------------------------------------------------------------------------------- /educoder-tasks/1-机器学习 --- kNN算法/t-1.detail.py: -------------------------------------------------------------------------------- 1 | # encoding=utf8 2 | import numpy as np 3 | 4 | 5 | class kNNClassifier(object): 6 | def __init__(self, k): 7 | ''' 8 | 初始化函数 9 | :param k:kNN算法中的k 10 | ''' 11 | self.k = k 12 | self.train_feature = None 13 | self.train_label = None 14 | 15 | def fit(self, feature, label): 16 | ''' 17 | kNN算法的训练过程 18 | :param feature: 训练集数据，类型为ndarray 19 | :param label: 训练集标签，类型为ndarray 20 | :return: 无返回 21 | ''' 22 | 23 | # 直接将数据保存为属性即可 24 | # 需注意此处 feature 是一个多维数组(一个样本会多个特征而不是只有一个) 25 | self.train_feature = feature 26 | # label 则是一个一维数组 27 | self.train_label = label 28 | 29 | def predict(self, feature): 30 | ''' 31 | kNN算法的预测过程 32 | :param feature: 测试集数据，类型为ndarray 33 | :return: 预测结果，类型为ndarray或list 34 | ''' 35 | 36 | # 创建一个空数组, 将返回的预测结果存在此处 37 | result = [] 38 | 39 | # 对于每个待分类的数据 40 | for feat in feature: 41 | # 计算当前样本和(对两个形状相同的 ndarray 做减法会返回一个对应位置的项相减的同样大小的 ndarray) 42 | # train_feature 是训练样本集, 其中每一项都和 feat 有同样的长度(即特征数) 43 | # M*N的 ndarray (记作`A`) 减去 1*N 的 ndarray (记作`b`) 会进行如下操作: 44 | # 将`b` copy M 份并合在一起, 产生一个新的 ndarray (记作`B`), 它与`A`有相同的形状 45 | # `A` 中的每一项 减去 `B` 中同样位置的项(A[i][j] - B[i][j]) 46 | # 返回运算后的 `A` 47 | # 所以此处的 diff 是一个二维数组, 含义为每一个训练数据和当前待测试数据对应项的差值 48 | diff = self.train_feature - feat 49 | 50 | # 计算出的 dist 为每个训练数据和当前测试数据的欧氏距离(一维数组) 51 | sq_diff = diff ** 2 52 | dist = sq_diff.sum(axis=1) ** 0.5 53 | 54 | # 将欧氏距离升序排序, argsort 不会返回排序后的 数组, 而是其 index 55 | # 58 行的 sorted_labels 即为其用法, 对 train_label 调用这份 index 即可将 train_label 按照 dist 的顺序排序 56 | dist_index = dist.argsort() 57 | # sorted_labels 即 把所有 train_label 按照(((其对应的测试数据和样本的距离)升序排序)的顺序)进行排序 58 | sorted_labels = self.train_label[dist_index] 59 | 60 | # 用来数每个邻居的标签的种类数的字典 61 | class_count = {} 62 | for i in range(self.k): 63 | # sorted_labels 中的前 k 个即是需要考虑的 k nearest neighbors 64 | label = sorted_labels[i] 65 | # 如果还没记录过当前标签, 则将其数量设置为1, 否则此数量++ 66 | class_count[label] = class_count.get(label, 0) + 1 67 | # sorted_class_count 是一个 tuple 的数组, 其中每个 tuple 为 `(标签, 数量)`, 此数组按`数量`降序排序 68 | sorted_class_count = sorted( 69 | class_count.items(), key=lambda x: x[1], reverse=True) 70 | # sorted_class_count[0][0] 即 `数量`最多元组 的 标签字段, 即最后预测的标签, 将其加入结果数组中 71 | result.append(sorted_class_count[0][0]) 72 | 73 | # 返回结果数组 74 | return result 75 | 76 | #******************************# 77 | # 测试如下 # 78 | # (数据为educoder上的测试数据) # 79 | #******************************# 80 | 81 | 82 | feats = np.array([[6.5, 2.8, 4.6, 1.5], 83 | [6.7, 2.5, 5.8, 1.8], 84 | [6.8, 3.0, 5.5, 2.1], 85 | [5.1, 3.5, 1.4, 0.3], 86 | [6.0, 2.2, 5.0, 1.5], 87 | [6.3, 2.9, 5.6, 1.8], 88 | [6.6, 2.9, 4.6, 1.3], 89 | [7.7, 2.6, 6.9, 2.3], 90 | [5.7, 3.8, 1.7, 0.3], 91 | [5.0, 3.6, 1.4, 0.2], 92 | [4.8, 3.0, 1.4, 0.3], 93 | [5.2, 2.7, 3.9, 1.4], 94 | [5.1, 3.4, 1.5, 0.2], 95 | [5.5, 3.5, 1.3, 0.2], 96 | [7.7, 3.8, 6.7, 2.2], 97 | [6.9, 3.1, 5.4, 2.1], 98 | [7.3, 2.9, 6.3, 1.8], 99 | [6.4, 2.8, 5.6, 2.2], 100 | [6.2, 2.8, 4.8, 1.8], 101 | [6.0, 3.4, 4.5, 1.6], 102 | [7.7, 2.8, 6.7, 2.0], 103 | [5.7, 3.0, 4.2, 1.2], 104 | [4.8, 3.4, 1.6, 0.2], 105 | [5.7, 2.5, 5.0, 2.0], 106 | [6.3, 2.7, 4.9, 1.8], 107 | [4.8, 3.0, 1.4, 0.1], 108 | [4.7, 3.2, 1.3, 0.2], 109 | [6.5, 3.0, 5.8, 2.2], 110 | [4.6, 3.4, 1.4, 0.3], 111 | [6.1, 3.0, 4.9, 1.8], 112 | [6.5, 3.2, 5.1, 2.0], 113 | [6.7, 3.1, 4.4, 1.4], 114 | [5.7, 2.8, 4.5, 1.3], 115 | [6.7, 3.3, 5.7, 2.5], 116 | [6.0, 3.0, 4.8, 1.8], 117 | [5.1, 3.8, 1.6, 0.2], 118 | [6.0, 2.2, 4.0, 1.0], 119 | [6.4, 2.9, 4.3, 1.3], 120 | [6.5, 3.0, 5.5, 1.8], 121 | [5.0, 2.3, 3.3, 1.0], 122 | [6.3, 3.3, 6.0, 2.5], 123 | [5.5, 2.5, 4.0, 1.3], 124 | [5.4, 3.7, 1.5, 0.2], 125 | [4.9, 3.1, 1.5, 0.1], 126 | [5.2, 4.1, 1.5, 0.1], 127 | [6.7, 3.3, 5.7, 2.1], 128 | [4.4, 3.0, 1.3, 0.2], 129 | [6.0, 2.7, 5.1, 1.6], 130 | [6.4, 2.7, 5.3, 1.9], 131 | [5.9, 3.0, 5.1, 1.8], 132 | [5.2, 3.5, 1.5, 0.2], 133 | [5.1, 3.3, 1.7, 0.5], 134 | [5.8, 2.7, 4.1, 1.0], 135 | [4.9, 3.1, 1.5, 0.1], 136 | [7.4, 2.8, 6.1, 1.9], 137 | [6.2, 2.9, 4.3, 1.3], 138 | [7.6, 3.0, 6.6, 2.1], 139 | [6.7, 3.0, 5.2, 2.3], 140 | [6.3, 2.3, 4.4, 1.3], 141 | [6.2, 3.4, 5.4, 2.3], 142 | [7.2, 3.6, 6.1, 2.5], 143 | [5.6, 2.9, 3.6, 1.3], 144 | [5.7, 4.4, 1.5, 0.4], 145 | [5.8, 2.7, 3.9, 1.2], 146 | [4.5, 2.3, 1.3, 0.3], 147 | [5.5, 2.4, 3.8, 1.1], 148 | [6.9, 3.1, 4.9, 1.5], 149 | [5.0, 3.4, 1.6, 0.4], 150 | [6.8, 2.8, 4.8, 1.4], 151 | [5.0, 3.5, 1.6, 0.6], 152 | [4.8, 3.4, 1.9, 0.2], 153 | [6.3, 3.4, 5.6, 2.4], 154 | [5.6, 2.8, 4.9, 2.0], 155 | [6.8, 3.2, 5.9, 2.3], 156 | [5.0, 3.3, 1.4, 0.2], 157 | [5.1, 3.7, 1.5, 0.4], 158 | [5.9, 3.2, 4.8, 1.8], 159 | [4.6, 3.1, 1.5, 0.2], 160 | [5.8, 2.7, 5.1, 1.9], 161 | [4.8, 3.1, 1.6, 0.2], 162 | [6.5, 3.0, 5.2, 2.0], 163 | [4.9, 2.5, 4.5, 1.7], 164 | [4.6, 3.2, 1.4, 0.2], 165 | [6.4, 3.2, 5.3, 2.3], 166 | [4.3, 3.0, 1.1, 0.1], 167 | [5.6, 3.0, 4.1, 1.3], 168 | [4.4, 2.9, 1.4, 0.2], 169 | [5.5, 2.4, 3.7, 1.0], 170 | [5.0, 2.0, 3.5, 1.0], 171 | [5.1, 3.5, 1.4, 0.2], 172 | [4.9, 3.0, 1.4, 0.2], 173 | [4.9, 2.4, 3.3, 1.0], 174 | [4.6, 3.6, 1.0, 0.2], 175 | [5.9, 3.0, 4.2, 1.5], 176 | [6.1, 2.9, 4.7, 1.4], 177 | [5.0, 3.4, 1.5, 0.2], 178 | [6.7, 3.1, 4.7, 1.5], 179 | [5.7, 2.9, 4.2, 1.3], 180 | [6.2, 2.2, 4.5, 1.5], 181 | [7.0, 3.2, 4.7, 1.4], 182 | [5.8, 2.7, 5.1, 1.9], 183 | [5.4, 3.4, 1.7, 0.2], 184 | [5.0, 3.0, 1.6, 0.2], 185 | [6.1, 2.6, 5.6, 1.4], 186 | [6.1, 2.8, 4.0, 1.3], 187 | [7.2, 3.0, 5.8, 1.6], 188 | [5.7, 2.6, 3.5, 1.0], 189 | [6.3, 2.8, 5.1, 1.5], 190 | [6.4, 3.1, 5.5, 1.8], 191 | [6.3, 2.5, 4.9, 1.5], 192 | [6.7, 3.1, 5.6, 2.4], 193 | [4.9, 3.1, 1.5, 0.1]]) 194 | labels = np.array([1, 2, 2, 0, 2, 2, 1, 2, 0, 0, 0, 1, 0, 0, 2, 2, 2, 2, 2, 1, 2, 1, 0, 2, 2, 0, 0, 2, 0, 2, 2, 1, 1, 2, 0, 1, 195 | 1, 2, 1, 2, 1, 0, 0, 0, 2, 0, 1, 2, 2, 0, 0, 1, 0, 2, 1, 2, 2, 1, 2, 2, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 2, 2, 2, 196 | 0, 0, 1, 0, 2, 0, 2, 2, 0, 2, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 2, 0, 0, 2, 1, 2, 1, 2, 2, 1, 2, 197 | 0, 0]) 198 | test = np.array([[5.8, 4.0, 1.2, 0.2], 199 | [5.1, 2.5, 3.0, 1.1], 200 | [6.6, 3.0, 4.4, 1.4], 201 | [5.4, 3.9, 1.3, 0.4], 202 | [7.9, 3.8, 6.4, 2.0], 203 | [6.3, 3.3, 4.7, 1.6], 204 | [6.9, 3.1, 5.1, 2.3], 205 | [5.1, 3.8, 1.9, 0.4], 206 | [4.7, 3.2, 1.6, 0.2], 207 | [6.9, 3.2, 5.7, 2.3], 208 | [5.6, 2.7, 4.2, 1.3], 209 | [5.4, 3.9, 1.7, 0.4], 210 | [7.1, 3.0, 5.9, 2.1], 211 | [6.4, 3.2, 4.5, 1.5], 212 | [6.0, 2.9, 4.5, 1.5], 213 | [4.4, 3.2, 1.3, 0.2], 214 | [5.8, 2.6, 4.0, 1.2], 215 | [5.6, 3.0, 4.5, 1.5], 216 | [5.4, 3.4, 1.5, 0.4], 217 | [5.0, 3.2, 1.2, 0.2], 218 | [5.5, 2.6, 4.4, 1.2], 219 | [5.4, 3.0, 4.5, 1.5], 220 | [6.7, 3.0, 5.0, 1.7], 221 | [5.0, 3.5, 1.3, 0.3], 222 | [7.2, 3.2, 6.0, 1.8], 223 | [5.7, 2.8, 4.1, 1.3], 224 | [5.5, 4.2, 1.4, 0.2], 225 | [5.1, 3.8, 1.5, 0.3], 226 | [6.1, 2.8, 4.7, 1.2], 227 | [6.3, 2.5, 5.0, 1.9], 228 | [6.1, 3.0, 4.6, 1.4], 229 | [7.7, 3.0, 6.1, 2.3], 230 | [5.6, 2.5, 3.9, 1.1], 231 | [6.4, 2.8, 5.6, 2.1], 232 | [5.8, 2.8, 5.1, 2.4], 233 | [5.3, 3.7, 1.5, 0.2], 234 | [5.5, 2.3, 4.0, 1.3], 235 | [5.2, 3.4, 1.4, 0.2]]) 236 | 237 | 238 | classifier = kNNClassifier(3) 239 | classifier.fit(feats, labels) 240 | 241 | result = classifier.predict(test) 242 | 243 | print(result) 244 | -------------------------------------------------------------------------------- /educoder-tasks/1-机器学习 --- kNN算法/t-2.ans.py: -------------------------------------------------------------------------------- 1 | from sklearn.neighbors import KNeighborsClassifier 2 | from sklearn.preprocessing import StandardScaler 3 | 4 | 5 | def classification(train_feature, train_label, test_feature): 6 | 7 | scaler = StandardScaler() 8 | train_feature = scaler.fit_transform(train_feature) 9 | 10 | classifier = KNeighborsClassifier(5) 11 | classifier.fit(train_feature, train_label) 12 | 13 | return classifier.predict(scaler.transform(test_feature)) 14 | -------------------------------------------------------------------------------- /educoder-tasks/1-机器学习 --- kNN算法/t-2.detail.py: -------------------------------------------------------------------------------- 1 | from sklearn.neighbors import KNeighborsClassifier 2 | from sklearn.preprocessing import StandardScaler 3 | 4 | 5 | def classification(train_feature, train_label, test_feature): 6 | ''' 7 | 对test_feature进行红酒分类 8 | :param train_feature: 训练集数据，类型为ndarray 9 | :param train_label: 训练集标签，类型为ndarray 10 | :param test_feature: 测试集数据，类型为ndarray 11 | :return: 测试集数据的分类结果 12 | ''' 13 | 14 | # 实例化一个 StandardScaler 对象 15 | scaler = StandardScaler() 16 | # scaler.fit_transform 会将数据进行标准化, 同时记录数据的均值和方差以便对后续测试数据执行同样的标准化 17 | std_train_feature = scaler.fit_transform(train_feature) 18 | 19 | # 实例化一个KNN分类器 20 | classifier = KNeighborsClassifier() 21 | # 使用标准化后的数据训练他 22 | classifier.fit(std_train_feature, train_label) 23 | 24 | # 返回(使用(训练过的分类器)预测(标准化后的数据)的结果) 25 | return classifier.predict(scaler.transform(test_feature)) 26 | -------------------------------------------------------------------------------- /educoder-tasks/2-机器学习之kNN算法/t-1.md: -------------------------------------------------------------------------------- 1 | # 答案 2 | 3 | 1. 列说法正确的是？ 4 | 5 | 1. [x] kNN 算法的预测效率与训练集的数量有关 6 | 2. [ ] kNN 算法只能用于二分类 7 | 3. [ ] kNN 算法只能用于回归 8 | 4. [x] kNN 算法属于监督学习 9 | 10 | 2. 列说法错误的是 11 | 12 | 1. [x] kNN 算法的训练阶段需要构建模型 13 | 2. [ ] kNN 算法中 k 这个参数可以根据实际情况来调整 14 | 3. [ ] kNN 算法支持多分类 15 | 4. [ ] kNN 算法不属于无监督学习 16 | -------------------------------------------------------------------------------- /educoder-tasks/2-机器学习之kNN算法/t-2.py: -------------------------------------------------------------------------------- 1 | from sklearn.neighbors import KNeighborsClassifier 2 | 3 | 4 | # 蛤, 这不是直接把题目描述复制过来就行了嘛?? 5 | def classification(train_feature, train_label, test_feature): 6 | ''' 7 | 使用KNeighborsClassifier对test_feature进行分类 8 | :param train_feature: 训练集数据 9 | :param train_label: 训练集标签 10 | :param test_feature: 测试集数据 11 | :return: 测试集预测结果 12 | ''' 13 | 14 | clf = KNeighborsClassifier() # 生成K近邻分类器 15 | clf.fit(train_feature, train_label) # 训练分类器 16 | predict_result = clf.predict(test_feature) 17 | 18 | return predict_result 19 | -------------------------------------------------------------------------------- /educoder-tasks/2-机器学习之kNN算法/t-3.py: -------------------------------------------------------------------------------- 1 | from sklearn.neighbors import KNeighborsRegressor 2 | 3 | 4 | # 蛤, 这不是直接把题目描述复制过来就行了嘛?? 5 | def regression(train_feature, train_label, test_feature): 6 | ''' 7 | 使用KNeighborsRegressor对test_feature进行分类 8 | :param train_feature: 训练集数据 9 | :param train_label: 训练集标签 10 | :param test_feature: 测试集数据 11 | :return: 测试集预测结果 12 | ''' 13 | 14 | clf = KNeighborsRegressor() # 生成K近邻分类器 15 | clf.fit(train_feature, train_label) # 训练分类器 16 | predict_result = clf.predict(test_feature) # 进行预测 17 | 18 | return predict_result 19 | -------------------------------------------------------------------------------- /educoder-tasks/2-机器学习之kNN算法/t-4.py: -------------------------------------------------------------------------------- 1 | 2 | import numpy as np 3 | 4 | 5 | # 想知道为什么可以按照题目描述里的指示看看这个数据集长啥样 6 | def alcohol_mean(data): 7 | ''' 8 | 返回红酒数据中红酒的酒精平均含量 9 | :param data: 红酒数据对象 10 | :return: 酒精平均含量，类型为float 11 | ''' 12 | return data.data[:, 0].mean() 13 | -------------------------------------------------------------------------------- /educoder-tasks/2-机器学习之kNN算法/t-5.py: -------------------------------------------------------------------------------- 1 | from sklearn.preprocessing import StandardScaler 2 | 3 | 4 | def scaler(data): 5 | ''' 6 | 返回标准化后的红酒数据 7 | :param data: 红酒数据对象 8 | :return: 标准化后的红酒数据，类型为ndarray 9 | ''' 10 | return StandardScaler().fit_transform(data.data) 11 | -------------------------------------------------------------------------------- /educoder-tasks/3-机器学习 --- 感知机/t-1.ans.py: -------------------------------------------------------------------------------- 1 | # encoding=utf8 2 | import numpy as np 3 | 4 | 5 | class Perceptron(object): 6 | def __init__(self, learning_rate=0.01, max_iter=200): 7 | self.lr = learning_rate 8 | self.max_iter = max_iter 9 | 10 | def fit(self, data, label): 11 | self.w = np.array([1.] * data.shape[1]) 12 | self.b = np.array([1.]) 13 | 14 | length = data.shape[0] 15 | for i in range(self.max_iter): 16 | has_error = False 17 | 18 | for i in range(length): 19 | x = data[i] 20 | y = x.dot(self.w) + self.b 21 | 22 | res = 1 if y > 0 else - 1 23 | if res == label[i]: 24 | continue 25 | 26 | else: 27 | has_error = True 28 | self.w -= self.lr * res * x 29 | self.b -= self.lr * res 30 | if not has_error: 31 | break 32 | 33 | def predict(self, data): 34 | predict = [] 35 | for x in data: 36 | y = self.w.dot(x) + self.b 37 | res = 1 if y > 0 else - 1 38 | predict.append(res) 39 | 40 | return predict 41 | -------------------------------------------------------------------------------- /educoder-tasks/3-机器学习 --- 感知机/t-1.detail.py: -------------------------------------------------------------------------------- 1 | # encoding=utf8 2 | import numpy as np 3 | 4 | 5 | # 构建感知机算法 6 | class Perceptron(object): 7 | def __init__(self, learning_rate=0.01, max_iter=200): 8 | self.lr = learning_rate 9 | self.max_iter = max_iter 10 | 11 | def fit(self, data, label): 12 | ''' 13 | input:data(ndarray):训练数据特征 14 | label(ndarray):训练数据标签 15 | output:w(ndarray):训练好的权重 16 | b(ndarry):训练好的偏置 17 | ''' 18 | # 编写感知机训练方法，w为权重，b为偏置 19 | self.w = np.array([1.]*data.shape[1]) # 将 weight 置为全 1 的数组 20 | self.b = np.array([1.]) # 将 bias 置为 1 21 | 22 | length = data.shape[0] # 迭代数据时会多次用到数据集长度 23 | 24 | for i in range(self.max_iter): # 如果超出最大迭代次数就停止训练 25 | has_error = False # 如果后续发现一次训练没有错误也停止训练 26 | 27 | for i in range(length): # 迭代训练数据 28 | x = data[i] # x 为当前数据 29 | # 即 x_1*w_1 + x_2*w_2 + ... + x_i*w_i + b, 用向量乘法会简介一些 30 | y = x.dot(self.w) + self.b 31 | 32 | res = 1 if y > 0 else - 1 # 算出来的 y 接近 1 就相当于预测结果是 1, 接近 -1 则预测 -1 33 | if res == label[i]: # 如果预测对了就不干事 34 | continue 35 | 36 | else: # 如果预测错了 37 | has_error = True 38 | # 按题目中给的公式更新 w 和 b 39 | self.w -= self.lr * res * x 40 | self.b -= self.lr * res 41 | if not has_error: 42 | break 43 | 44 | #********* End *********# 45 | def predict(self, data): 46 | ''' 47 | input:data(ndarray):测试数据特征 48 | output:predict(ndarray):预测标签 49 | ''' 50 | 51 | predict = [] 52 | for x in data: 53 | y = self.w.dot(x) + self.b 54 | res = 1 if y > 0 else - 1 55 | predict.append(res) 56 | 57 | return predict 58 | -------------------------------------------------------------------------------- /educoder-tasks/3-机器学习 --- 感知机/t-2.py: -------------------------------------------------------------------------------- 1 | # encoding=utf8 2 | import os 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn.linear_model.perceptron import Perceptron 6 | from sklearn.preprocessing import StandardScaler 7 | 8 | 9 | # 获取训练数据 10 | train_data = pd.read_csv('./step2/train_data.csv') 11 | # 获取训练标签 12 | train_label = pd.read_csv('./step2/train_label.csv') 13 | train_label = train_label['target'] 14 | # 获取测试数据 15 | test_data = pd.read_csv('./step2/test_data.csv') 16 | 17 | if os.path.exists('./step2/result.csv'): 18 | os.remove('./step2/result.csv') 19 | 20 | # 标准化数据 21 | scaler = StandardScaler() 22 | train_data = scaler.fit_transform(train_data) 23 | 24 | clf = Perceptron() 25 | clf.fit(train_data, train_label) 26 | pred = clf.predict(scaler.transform(test_data)) 27 | 28 | result = np.where(pred > 0.5, 1, 0) 29 | 30 | df = pd.DataFrame(result, columns=["result"]) 31 | df.to_csv('./step2/result.csv') 32 | -------------------------------------------------------------------------------- /educoder-tasks/4-机器学习 --- 朴素贝叶斯分类器/t-1.md: -------------------------------------------------------------------------------- 1 | 1. 对 2 | 2. 9/14 3 | -------------------------------------------------------------------------------- /educoder-tasks/4-机器学习 --- 朴素贝叶斯分类器/t-2.md: -------------------------------------------------------------------------------- 1 | 1. 0.97 2 | 2. 3/4 3 | -------------------------------------------------------------------------------- /educoder-tasks/4-机器学习 --- 朴素贝叶斯分类器/t-3.js: -------------------------------------------------------------------------------- 1 | fetch("https://data.educoder.net/api/myshixuns/yv9anftf4r/update_file.json", { 2 | headers: { 3 | accept: "application/json", 4 | "accept-language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6", 5 | "cache-control": "no-cache", 6 | "content-type": "application/json; charset=utf-8", 7 | pragma: "no-cache", 8 | prefer: "safe", 9 | "sec-ch-ua": 10 | '"Microsoft Edge";v="95", "Chromium";v="95", ";Not A Brand";v="99"', 11 | "sec-ch-ua-mobile": "?0", 12 | "sec-ch-ua-platform": '"Windows"', 13 | "sec-fetch-dest": "empty", 14 | "sec-fetch-mode": "cors", 15 | "sec-fetch-site": "same-site", 16 | }, 17 | referrer: "https://www.educoder.net/tasks/fa3x6tpnwjor", 18 | referrerPolicy: "unsafe-url", 19 | body: 20 | '{"path":"step3/student.py","evaluate":0,"content":"import numpy as np\\n\\n\\nclass NaiveBayesClassifier(object):\\n \\n def __init__(self):\\n self.label_prob = {}\\n self.condition_prob = {}\\n\\n def fit(self, feature, label):\\n for l in label:\\n self.label_prob[l] = self.label_prob.get(l, 0) + 1\\n \\n for k, v in self.label_prob.items():\\n self.label_prob[k] = v / len(label)\\n\\n label2data = {}\\n\\n for i, data in enumerate(feature):\\n l = label[i]\\n if not label2data.get(l, None):\\n label2data[l] = [data]\\n else:\\n label2data[l].append(data)\\n \\n for l, all_data in label2data.items():\\n feat_index2feat_count = {}\\n for data in all_data:\\n for i, d in enumerate(data):\\n feat_index2feat_count[i] = feat_index2feat_count.get(i, {})\\n feat_index2feat_count[i][d] = feat_index2feat_count[i].get(d, 0)+1\\n \\n feat_index2feat_count[\\"__count__\\"] = len(all_data)\\n\\n self.condition_prob[l] = feat_index2feat_count\\n \\n\\n\\n def predict(self, feature):\\n\\n ret_arr = []\\n\\n for fs in feature:\\n label2res = {}\\n for label, feat_index2feat_count in self.condition_prob.items():\\n p = self.label_prob[label]\\n\\n for i, f in enumerate(fs):\\n p *= feat_index2feat_count[i].get(f, 0) / feat_index2feat_count[\\"__count__\\"]\\n\\n label2res[label] = p\\n\\n max_prob = {}\\n for label, prob in label2res.items():\\n if(prob>max_prob.get(\\"prob\\", -1)):\\n max_prob = {\\"label\\": label, \\"prob\\": prob}\\n ret_arr.append(max_prob[\\"label\\"])\\n \\n return ret_arr\\n # ********* Begin *********#\\n \\n #********* End *********#","game_id":24420485}', 21 | method: "POST", 22 | mode: "cors", 23 | credentials: "include", 24 | }); 25 | -------------------------------------------------------------------------------- /educoder-tasks/4-机器学习 --- 朴素贝叶斯分类器/t-3.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | class NaiveBayesClassifier(object): 5 | 6 | def __init__(self): 7 | 8 | self.label_prob = {} 9 | 10 | self.condition_prob = {} 11 | 12 | def fit(self, feature, label): 13 | for l in label: 14 | self.label_prob[l] = self.label_prob.get(l, 0) + 1 15 | 16 | for k, v in self.label_prob.items(): 17 | self.label_prob[k] = v / len(label) 18 | 19 | label2data = {} 20 | for i, data in enumerate(feature): 21 | l = label[i] 22 | old_data = label2data.get(l) 23 | if not old_data: 24 | label2data[l] = [data] 25 | else: 26 | label2data[l].append(data) 27 | 28 | for l, all_data in label2data.items(): 29 | feat_index2feat_count = {} 30 | for data in all_data: 31 | for i, d in enumerate(data): 32 | feat_index2feat_count[i] = feat_index2feat_count.get(i, {}) 33 | feat_index2feat_count[i][d] = feat_index2feat_count[i].get( 34 | d, 0) + 1 35 | 36 | feat_index2feat_count["__count__"] = len(all_data) 37 | 38 | self.condition_prob[l] = feat_index2feat_count 39 | 40 | print(self.condition_prob, self.label_prob) 41 | 42 | def predict(self, feature): 43 | ''' 44 | 对数据进行预测，返回预测结果 45 | :param feature:测试数据集所有特征组成的ndarray 46 | :return: 47 | ''' 48 | ret_arr = [] 49 | 50 | for fs in feature: 51 | label2res = {} 52 | for label, feat_index2feat_count in self.condition_prob.items(): 53 | p = self.label_prob[label] 54 | 55 | for i, f in enumerate(fs): 56 | p *= feat_index2feat_count[i].get(f, 0) / \ 57 | feat_index2feat_count["__count__"] 58 | 59 | label2res[label] = p 60 | 61 | max_prob = {"label": "foo", "prob": -1} 62 | for label, prob in label2res.items(): 63 | if(prob > max_prob["prob"]): 64 | max_prob = {"label": label, "prob": prob} 65 | 66 | ret_arr.append(max_prob["label"]) 67 | 68 | return ret_arr 69 | 70 | # ********* Begin *********# 71 | 72 | #********* End *********# 73 | 74 | # P(好|条件) = P(条件|好)*P(好)/P(条件) 75 | 76 | 77 | feat = [[2, 1, 1], 78 | [1, 2, 2], 79 | [2, 2, 2], 80 | [2, 1, 2], 81 | [1, 2, 3]] 82 | 83 | label = [1, 0, 1, 0, 1] 84 | 85 | test = [[2, 1, 1], 86 | [1, 2, 2], 87 | [2, 2, 2], 88 | [2, 100, 2], 89 | [1, 2, 3]] 90 | 91 | 92 | p = NaiveBayesClassifier() 93 | p.fit(feat, label) 94 | res = p.predict(test) 95 | 96 | print(res) 97 | -------------------------------------------------------------------------------- /educoder-tasks/4-机器学习 --- 朴素贝叶斯分类器/t-4.js: -------------------------------------------------------------------------------- 1 | var code = `import numpy as np 2 | 3 | 4 | class NaiveBayesClassifier(object): 5 | 6 | def __init__(self): 7 | 8 | self.label_prob = {} 9 | 10 | self.condition_prob = {} 11 | 12 | def fit(self, feature, label): 13 | for l in label: 14 | self.label_prob[l] = self.label_prob.get(l, 0) + 1 15 | 16 | for k, v in self.label_prob.items(): 17 | self.label_prob[k] = (v+1) / (len(label) + 18 | len(self.label_prob.keys())) 19 | 20 | label2data = {} 21 | for i, data in enumerate(feature): 22 | l = label[i] 23 | old_data = label2data.get(l) 24 | if not old_data: 25 | label2data[l] = [data] 26 | else: 27 | label2data[l].append(data) 28 | 29 | for l, all_data in label2data.items(): 30 | feat_index2feat_count = {} 31 | for i in range(len(feature[0])): 32 | feat_index2feat_count[i] = {} 33 | for f in feature: 34 | d = f[i] 35 | feat_index2feat_count[i][d] = 0 36 | 37 | for data in all_data: 38 | for i, d in enumerate(data): 39 | feat_index2feat_count[i][d] = feat_index2feat_count[i].get( 40 | d, 0) + 1 41 | 42 | for i in feat_index2feat_count.keys(): 43 | for k in feat_index2feat_count[i].keys(): 44 | feat_index2feat_count[i][k] = ( 45 | feat_index2feat_count[i][k]+1) / (len(all_data)+len(feat_index2feat_count[i].keys())) 46 | 47 | self.condition_prob[l] = feat_index2feat_count 48 | 49 | def predict(self, feature): 50 | ''' 51 | 对数据进行预测，返回预测结果 52 | :param feature:测试数据集所有特征组成的ndarray 53 | :return: 54 | ''' 55 | ret_arr = [] 56 | 57 | for fs in feature: 58 | label2res = {} 59 | for label, feat_index2feat_count in self.condition_prob.items(): 60 | p = self.label_prob[label] 61 | 62 | for i, f in enumerate(fs): 63 | p *= (feat_index2feat_count[i].get(f, 0)) 64 | 65 | label2res[label] = p 66 | 67 | 68 | max_prob = {"label": "foo", "prob": -1} 69 | for label, prob in label2res.items(): 70 | if(prob > max_prob["prob"]): 71 | max_prob = {"label": label, "prob": prob} 72 | 73 | ret_arr.append(max_prob["label"]) 74 | 75 | return ret_arr 76 | 77 | 78 | feat = [[2, 1, 1], 79 | [1, 2, 2], 80 | [2, 2, 2], 81 | [2, 1, 2], 82 | [1, 2, 3]] 83 | 84 | label = [1, 0, 1, 0, 1] 85 | 86 | test = [[2, 1, 1], 87 | [1, 2, 2], 88 | [2, 2, 2], 89 | [2, 100, 2], 90 | [1, 2, 3]] 91 | 92 | 93 | p = NaiveBayesClassifier() 94 | p.fit(feat, label) 95 | res = p.predict(test) 96 | 97 | 98 | 99 | ` 100 | .replaceAll("\n", "\\n") 101 | .replaceAll('"', '\\"'); 102 | 103 | fetch("https://data.educoder.net/api/myshixuns/yv9anftf4r/update_file.json", { 104 | headers: { 105 | accept: "application/json", 106 | "accept-language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6", 107 | "cache-control": "no-cache", 108 | "content-type": "application/json; charset=utf-8", 109 | pragma: "no-cache", 110 | prefer: "safe", 111 | "sec-ch-ua": 112 | '"Microsoft Edge";v="95", "Chromium";v="95", ";Not A Brand";v="99"', 113 | "sec-ch-ua-mobile": "?0", 114 | "sec-ch-ua-platform": '"Windows"', 115 | "sec-fetch-dest": "empty", 116 | "sec-fetch-mode": "cors", 117 | "sec-fetch-site": "same-site", 118 | }, 119 | referrer: "https://www.educoder.net/tasks/43ac57wnjh9o", 120 | referrerPolicy: "unsafe-url", 121 | body: `{"path":"step4/student.py","evaluate":0,"content":"${code}","game_id":24420486}`, 122 | method: "POST", 123 | mode: "cors", 124 | credentials: "include", 125 | }); 126 | -------------------------------------------------------------------------------- /educoder-tasks/4-机器学习 --- 朴素贝叶斯分类器/t-4.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | class NaiveBayesClassifier(object): 5 | 6 | def __init__(self): 7 | 8 | self.label_prob = {} 9 | 10 | self.condition_prob = {} 11 | 12 | def fit(self, feature, label): 13 | for l in label: 14 | self.label_prob[l] = self.label_prob.get(l, 0) + 1 15 | 16 | for k, v in self.label_prob.items(): 17 | self.label_prob[k] = (v+1) / (len(label) + 18 | len(self.label_prob.keys())) 19 | 20 | label2data = {} 21 | for i, data in enumerate(feature): 22 | l = label[i] 23 | old_data = label2data.get(l) 24 | if not old_data: 25 | label2data[l] = [data] 26 | else: 27 | label2data[l].append(data) 28 | 29 | for l, all_data in label2data.items(): 30 | feat_index2feat_count = {} 31 | for i in range(len(feature[0])): 32 | feat_index2feat_count[i] = {} 33 | for f in feature: 34 | d = f[i] 35 | feat_index2feat_count[i][d] = 0 36 | 37 | for data in all_data: 38 | for i, d in enumerate(data): 39 | feat_index2feat_count[i][d] = feat_index2feat_count[i].get( 40 | d, 0) + 1 41 | 42 | for i in feat_index2feat_count.keys(): 43 | for k in feat_index2feat_count[i].keys(): 44 | feat_index2feat_count[i][k] = ( 45 | feat_index2feat_count[i][k]+1) / (len(all_data)+len(feat_index2feat_count[i].keys())) 46 | 47 | self.condition_prob[l] = feat_index2feat_count 48 | 49 | def predict(self, feature): 50 | ''' 51 | 对数据进行预测，返回预测结果 52 | :param feature:测试数据集所有特征组成的ndarray 53 | :return: 54 | ''' 55 | ret_arr = [] 56 | 57 | for fs in feature: 58 | label2res = {} 59 | for label, feat_index2feat_count in self.condition_prob.items(): 60 | p = self.label_prob[label] 61 | 62 | for i, f in enumerate(fs): 63 | p *= (feat_index2feat_count[i].get(f, 0)) 64 | 65 | label2res[label] = p 66 | 67 | max_prob = {"label": "foo", "prob": -1} 68 | for label, prob in label2res.items(): 69 | if(prob > max_prob["prob"]): 70 | max_prob = {"label": label, "prob": prob} 71 | 72 | ret_arr.append(max_prob["label"]) 73 | 74 | return ret_arr 75 | 76 | 77 | feat = [[2, 1, 1], 78 | [1, 2, 2], 79 | [2, 2, 2], 80 | [2, 1, 2], 81 | [1, 2, 3]] 82 | 83 | label = [1, 0, 1, 0, 1] 84 | 85 | test = [[2, 1, 1], 86 | [1, 2, 2], 87 | [2, 2, 2], 88 | [2, 100, 2], 89 | [1, 2, 3]] 90 | 91 | 92 | p = NaiveBayesClassifier() 93 | p.fit(feat, label) 94 | res = p.predict(test) 95 | -------------------------------------------------------------------------------- /educoder-tasks/4-机器学习 --- 朴素贝叶斯分类器/t-5.js: -------------------------------------------------------------------------------- 1 | var code = ` 2 | from sklearn.feature_extraction.text import CountVectorizer 3 | from sklearn.naive_bayes import MultinomialNB 4 | from sklearn.feature_extraction.text import TfidfTransformer 5 | 6 | 7 | def news_predict(train_sample, train_label, test_sample): 8 | vec = CountVectorizer() 9 | X_train_count_vectorizer = vec.fit_transform(train_sample) 10 | X_test_count_vectorizer = vec.transform(test_sample) 11 | 12 | tfidf = TfidfTransformer() 13 | X_train = tfidf.fit_transform(X_train_count_vectorizer) 14 | X_test = vec.transform(X_test_count_vectorizer) 15 | 16 | clf = MultinomialNB() 17 | 18 | clf.fit(X_train, train_label) 19 | result = clf.predict(X_test) 20 | 21 | ` 22 | .replaceAll("\n", "\\n") 23 | .replaceAll('"', '\\"'); 24 | 25 | fetch("https://data.educoder.net/api/myshixuns/yv9anftf4r/update_file.json", { 26 | headers: { 27 | accept: "application/json", 28 | "accept-language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6", 29 | "cache-control": "no-cache", 30 | "content-type": "application/json; charset=utf-8", 31 | pragma: "no-cache", 32 | prefer: "safe", 33 | "sec-ch-ua": 34 | '"Microsoft Edge";v="95", "Chromium";v="95", ";Not A Brand";v="99"', 35 | "sec-ch-ua-mobile": "?0", 36 | "sec-ch-ua-platform": '"Windows"', 37 | "sec-fetch-dest": "empty", 38 | "sec-fetch-mode": "cors", 39 | "sec-fetch-site": "same-site", 40 | }, 41 | referrer: "https://www.educoder.net/tasks/r2a6zphucbm3", 42 | referrerPolicy: "unsafe-url", 43 | body: `{"path":"step5/student.py","evaluate":0,"content":"${code}","game_id":24420487}`, 44 | method: "POST", 45 | mode: "cors", 46 | credentials: "include", 47 | }); 48 | -------------------------------------------------------------------------------- /educoder-tasks/4-机器学习 --- 朴素贝叶斯分类器/t-5.py: -------------------------------------------------------------------------------- 1 | 2 | from sklearn.feature_extraction.text import CountVectorizer 3 | from sklearn.naive_bayes import MultinomialNB 4 | from sklearn.feature_extraction.text import TfidfTransformer 5 | 6 | 7 | def news_predict(train_sample, train_label, test_sample): 8 | vec = CountVectorizer() 9 | X_train_count_vectorizer = vec.fit_transform(train_sample) 10 | X_test_count_vectorizer = vec.transform(test_sample) 11 | 12 | tfidf = TfidfTransformer() 13 | X_train = tfidf.fit_transform(X_train_count_vectorizer) 14 | X_test = tfidf.transform(X_test_count_vectorizer) 15 | 16 | clf = MultinomialNB(0.03) 17 | 18 | clf.fit(X_train, train_label) 19 | return clf.predict(X_test) 20 | -------------------------------------------------------------------------------- /educoder-tasks/5-机器学习之支持向量机/t-1.md: -------------------------------------------------------------------------------- 1 | 1. 黑线 2 | 2. 0.4 3 | 3. 3x+2y=0 4 | 4. 样本 y 到超平面的距离为 3 5 | 5. -x+y+1=0 6 | 6. - 7 | 1. 支持向量机的最终模型仅仅与支持向量有关。 8 | 2. 支持向量机的最终模型由离决策边界最近的几个点决定。 9 | -------------------------------------------------------------------------------- /educoder-tasks/5-机器学习之支持向量机/t-2.py: -------------------------------------------------------------------------------- 1 | # encoding=utf8 2 | import pandas as pd 3 | from sklearn.svm import LinearSVC 4 | 5 | 6 | def linearsvc_predict(train_data, train_label, test_data): 7 | 8 | # 获取训练数据 9 | train_data = pd.read_csv('./step1/train_data.csv') 10 | # 获取训练标签 11 | train_label = pd.read_csv('./step1/train_label.csv') 12 | train_label = train_label['target'] 13 | # 获取测试数据 14 | test_data = pd.read_csv('./step1/test_data.csv') 15 | 16 | svc = LinearSVC(penalty='l2', loss='squared_hinge', dual=True, tol=0.00001, C=0.71, multi_class='crammer_singer', 17 | fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=2000) 18 | 19 | svc.fit(train_data, train_label) 20 | 21 | predict = svc.predict(test_data) 22 | return predict 23 | -------------------------------------------------------------------------------- /lab/NN/index.py: -------------------------------------------------------------------------------- 1 | from keras.utils import to_categorical 2 | import scikitplot.plotters as skplt 3 | import matplotlib.pyplot as plt 4 | from sklearn.svm import SVC 5 | import os 6 | import jieba as jb 7 | import pandas as pd 8 | from gensim.models.doc2vec import TaggedDocument 9 | from gensim.models import Doc2Vec 10 | import numpy as np 11 | import matplotlib.pyplot as plt 12 | import numpy as np 13 | from keras.models import Sequential 14 | from keras.layers import Dense, Dropout 15 | from keras.optimizers import SGD 16 | import scikitplot.plotters as skplt 17 | import os 18 | 19 | 20 | def load_array(path, stopword_set=None, word_dict=None, is_test=False): 21 | print("### load_array ###") 22 | df = pd.read_csv(path) 23 | df.fillna('', inplace=True) 24 | 25 | lines = [] 26 | label = [] 27 | 28 | for i in range(0, df.shape[0]): 29 | try: 30 | lines.append(df["content"][i] + df["comment_all"][i]) 31 | if not is_test: 32 | label.append(df["label"][i]) 33 | except: 34 | pass 35 | 36 | data = word_break(lines) 37 | data, stopword_set = cleanup_stopword(data, stopword_set) 38 | data, word_dict = remove_spacial_word(data, word_dict) 39 | 40 | if is_test: 41 | label = None 42 | 43 | return data, label, stopword_set, word_dict 44 | 45 | 46 | def word_break(lines): 47 | print("### word_break ###") 48 | 49 | return [jb.lcut(l) for l in lines] 50 | 51 | 52 | def cleanup_stopword(dataset, stopword_set): 53 | print("### cleanup_stopword ###") 54 | if stopword_set is None: 55 | stopword_set = set() 56 | with open("../data/stopwords.txt", "r", encoding="utf-8") as fr: 57 | words = fr.readline() 58 | while words: 59 | stopword_set.add(words.replace("\n", "")) 60 | words = fr.readline() 61 | 62 | return [[w for w in data if w not in stopword_set] for data in dataset], stopword_set 63 | 64 | 65 | def remove_spacial_word(dataset, word_dict): 66 | print("### remove_spacial_word ###") 67 | if word_dict is None: 68 | word_dict = {} 69 | for data in dataset: 70 | for word in data: 71 | word_dict[word] = word_dict.get(word, 1) + 1 72 | 73 | for i in range(0, len(dataset)): 74 | new_data = [] 75 | for word in dataset[i]: 76 | if(word_dict.get(word, 0) > 1): 77 | new_data.append(word) 78 | dataset[i] = new_data 79 | 80 | return dataset, word_dict 81 | 82 | 83 | def array2vect(x_train, y_train, x_test, y_test): 84 | print("### array2vect ###") 85 | vector_dimension = 300 86 | 87 | x = [] 88 | 89 | total = [] 90 | total.extend(x_train) 91 | total.extend(x_test) 92 | 93 | for i, data in enumerate(total): 94 | x.append(TaggedDocument(data, ['Text' + '_%s' % str(i)])) 95 | 96 | text_model = Doc2Vec(min_count=1, window=5, vector_size=vector_dimension, sample=1e-4, negative=5, workers=7, epochs=10, 97 | seed=1) 98 | text_model.build_vocab(x) 99 | text_model.train(x, total_examples=text_model.corpus_count, 100 | epochs=10) 101 | 102 | train_size = len(x_train) 103 | test_size = len(x_test) 104 | 105 | text_train_arrays = np.zeros((train_size, vector_dimension)) 106 | text_test_arrays = np.zeros((test_size, vector_dimension)) 107 | train_labels = np.zeros(train_size) 108 | test_labels = np.zeros(test_size) 109 | 110 | for i in range(train_size): 111 | text_train_arrays[i] = text_model.docvecs['Text_' + str(i)] 112 | train_labels[i] = y_train[i] 113 | 114 | j = 0 115 | for i in range(train_size, train_size + test_size): 116 | text_test_arrays[j] = text_model.docvecs['Text_' + str(i)] 117 | if y_test is not None: 118 | test_labels[j] = y_test[j] 119 | j = j + 1 120 | 121 | return text_train_arrays, text_test_arrays, train_labels, test_labels 122 | 123 | 124 | def plot_cmat(yte, ypred): 125 | print("### plot_cmat ###") 126 | skplt.plot_confusion_matrix(yte, ypred) 127 | plt.show() 128 | 129 | 130 | def baseline_model(): 131 | '''Neural network with 3 hidden layers''' 132 | model = Sequential() 133 | 134 | model.add(Dense(256, input_dim=300, activation='relu', 135 | kernel_initializer='normal')) 136 | model.add(Dropout(0.3)) 137 | model.add(Dense(256, activation='relu', kernel_initializer='normal')) 138 | model.add(Dropout(0.5)) 139 | model.add(Dense(80, activation='relu', kernel_initializer='normal')) 140 | model.add(Dense(3, activation="softmax", kernel_initializer='normal')) 141 | 142 | # gradient descent 143 | sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) 144 | 145 | # configure the learning process of the model 146 | model.compile(loss='categorical_crossentropy', 147 | optimizer=sgd, metrics=['accuracy']) 148 | return model 149 | 150 | 151 | def main(): 152 | print("### main ###") 153 | if not os.path.isfile('./__data__/xtr.npy') or \ 154 | not os.path.isfile('./__data__/xte.npy') or \ 155 | not os.path.isfile('./__data__/ytr.npy') or \ 156 | not os.path.isfile('./__data__/yte.npy'): 157 | x_train, y_train, stopword_set, word_dict = load_array( 158 | "../data/train.csv") 159 | x_test, _, _, _ = load_array( 160 | "../data/test.csv", stopword_set, word_dict, is_test=True) 161 | xtr, xte, ytr, yte = array2vect(x_train, y_train, x_test, None) 162 | np.save('./__data__/xtr', xtr) 163 | np.save('./__data__/xte', xte) 164 | np.save('./__data__/ytr', ytr) 165 | np.save('./__data__/yte', yte) 166 | else: 167 | xtr = np.load('./__data__/xtr.npy') 168 | xte = np.load('./__data__/xte.npy') 169 | ytr = np.load('./__data__/ytr.npy') 170 | yte = np.load('./__data__/yte.npy') 171 | 172 | encoded_y = [] 173 | for y in ytr: 174 | encoded_y_item = [0, 0, 0] 175 | encoded_y_item[int(y)+1] = 1 176 | encoded_y.append(encoded_y_item) 177 | ytr = np.array(encoded_y) 178 | 179 | print("### data loaded ###") 180 | 181 | # Train the model 182 | model = baseline_model() 183 | model.summary() 184 | 185 | estimator = model.fit(xtr, ytr, epochs=20, batch_size=64) 186 | print("Model Trained!") 187 | 188 | probabs = model.predict_proba(xte) 189 | print("probabs.shape", probabs.shape) 190 | y_pred = np.argmax(probabs, axis=1) - 1 191 | 192 | np.savetxt('./result.txt', y_pred, fmt="%d", delimiter=" ") 193 | 194 | 195 | main() 196 | -------------------------------------------------------------------------------- /lab/NN/result.txt: 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1389 | 1 1390 | 1 1391 | 0 1392 | 0 1393 | 1 1394 | 0 1395 | -1 1396 | 0 1397 | -1 1398 | -1 1399 | -1 1400 | -1 1401 | 1 1402 | 1 1403 | -1 1404 | 1 1405 | 1 1406 | 0 1407 | 1 1408 | -1 1409 | 1 1410 | 1 1411 | 1 1412 | 0 1413 | -1 1414 | 1 1415 | 1 1416 | 1 1417 | -1 1418 | 1 1419 | 1 1420 | 0 1421 | 0 1422 | 0 1423 | 1 1424 | -1 1425 | -1 1426 | 0 1427 | -1 1428 | 1 1429 | 1 1430 | 0 1431 | -1 1432 | 1 1433 | 0 1434 | 0 1435 | -1 1436 | 0 1437 | 1 1438 | -1 1439 | 1 1440 | 0 1441 | -1 1442 | -1 1443 | 1 1444 | 0 1445 | -1 1446 | 1 1447 | 0 1448 | -1 1449 | -1 1450 | 1 1451 | 1 1452 | -1 1453 | -1 1454 | 1 1455 | 0 1456 | -1 1457 | 1 1458 | 0 1459 | 1 1460 | 1 1461 | 1 1462 | 1 1463 | -1 1464 | 1 1465 | -1 1466 | 1 1467 | -1 1468 | 0 1469 | -1 1470 | -1 1471 | -1 1472 | -1 1473 | 1 1474 | 1 1475 | 1 1476 | -1 1477 | -1 1478 | -1 1479 | 1 1480 | 1 1481 | -1 1482 | 1 1483 | 0 1484 | -1 1485 | 0 1486 | -1 1487 | 1 1488 | 1 1489 | 0 1490 | 0 1491 | -1 1492 | 0 1493 | 1 1494 | 1 1495 | 0 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1814 | -1 1815 | -1 1816 | -1 1817 | 1 1818 | -1 1819 | 1 1820 | 0 1821 | 0 1822 | 1 1823 | 1 1824 | 1 1825 | 1 1826 | -1 1827 | 1 1828 | -1 1829 | 0 1830 | -1 1831 | 0 1832 | 1 1833 | -1 1834 | -1 1835 | -1 1836 | 0 1837 | 1 1838 | -1 1839 | -1 1840 | 0 1841 | -1 1842 | -1 1843 | 1 1844 | 1 1845 | -1 1846 | 0 1847 | -1 1848 | -1 1849 | 0 1850 | -1 1851 | 1 1852 | 1 1853 | -1 1854 | 0 1855 | -1 1856 | 0 1857 | 0 1858 | 1 1859 | 0 1860 | 1 1861 | 0 1862 | 0 1863 | 1 1864 | -1 1865 | -1 1866 | 0 1867 | -1 1868 | 1 1869 | 0 1870 | -1 1871 | -1 1872 | 0 1873 | -1 1874 | -1 1875 | -1 1876 | -1 1877 | 1 1878 | 1 1879 | 1 1880 | -1 1881 | -1 1882 | 1 1883 | 0 1884 | 0 1885 | 0 1886 | -1 1887 | 0 1888 | 1 1889 | 0 1890 | -1 1891 | -1 1892 | 1 1893 | 0 1894 | 1 1895 | 0 1896 | -1 1897 | 1 1898 | 1 1899 | -1 1900 | -1 1901 | 1 1902 | -1 1903 | 1 1904 | -1 1905 | 0 1906 | -1 1907 | -1 1908 | 0 1909 | 0 1910 | -1 1911 | 0 1912 | 1 1913 | 1 1914 | -1 1915 | 1 1916 | 1 1917 | 0 1918 | -1 1919 | -1 1920 | -1 1921 | -1 1922 | 0 1923 | 1 1924 | 0 1925 | -1 1926 | -1 1927 | -1 1928 | -1 1929 | 0 1930 | -1 1931 | -1 1932 | 0 1933 | -1 1934 | 0 1935 | -1 1936 | 1 1937 | 1 1938 | 0 1939 | 0 1940 | 1 1941 | 1 1942 | -1 1943 | 0 1944 | 0 1945 | 1 1946 | -1 1947 | 1 1948 | -1 1949 | -1 1950 | 0 1951 | -1 1952 | 1 1953 | -1 1954 | -1 1955 | 1 1956 | -1 1957 | 0 1958 | 1 1959 | 1 1960 | -1 1961 | 0 1962 | -1 1963 | 1 1964 | 0 1965 | 0 1966 | 0 1967 | 1 1968 | -1 1969 | -1 1970 | 0 1971 | 1 1972 | -1 1973 | -1 1974 | 1 1975 | -1 1976 | 0 1977 | 1 1978 | 0 1979 | 1 1980 | 1 1981 | 0 1982 | 0 1983 | 1 1984 | 0 1985 | 1 1986 | -1 1987 | -1 1988 | 0 1989 | 0 1990 | 0 1991 | 0 1992 | 1 1993 | -1 1994 | -1 1995 | 1 1996 | 1 1997 | 0 1998 | 0 1999 | 0 2000 | 1 2001 | 1 2002 | -1 2003 | 0 2004 | -1 2005 | 1 2006 | -1 2007 | 1 2008 | 1 2009 | -1 2010 | -1 2011 | 0 2012 | -1 2013 | 1 2014 | -1 2015 | 1 2016 | 1 2017 | 0 2018 | -1 2019 | -1 2020 | -1 2021 | -1 2022 | -1 2023 | -1 2024 | 1 2025 | -1 2026 | 0 2027 | -1 2028 | 1 2029 | 1 2030 | 1 2031 | 1 2032 | 0 2033 | 1 2034 | -1 2035 | 0 2036 | 1 2037 | -1 2038 | -1 2039 | 0 2040 | -1 2041 | 0 2042 | 0 2043 | 1 2044 | -1 2045 | 1 2046 | 0 2047 | 1 2048 | -1 2049 | 1 2050 | -1 2051 | 1 2052 | -1 2053 | 0 2054 | -1 2055 | 1 2056 | 1 2057 | 1 2058 | -1 2059 | 0 2060 | -1 2061 | 0 2062 | -1 2063 | -1 2064 | 1 2065 | 1 2066 | 0 2067 | 1 2068 | 1 2069 | 0 2070 | -1 2071 | -1 2072 | -1 2073 | 0 2074 | -1 2075 | -1 2076 | 0 2077 | 0 2078 | 0 2079 | -1 2080 | -1 2081 | 0 2082 | -1 2083 | 0 2084 | 0 2085 | -1 2086 | 0 2087 | -1 2088 | 1 2089 | -1 2090 | 0 2091 | -1 2092 | 1 2093 | -1 2094 | -1 2095 | -1 2096 | 1 2097 | -1 2098 | 1 2099 | -1 2100 | 0 2101 | 0 2102 | 0 2103 | 0 2104 | 0 2105 | 0 2106 | -1 2107 | -1 2108 | 1 2109 | 1 2110 | -1 2111 | -1 2112 | -1 2113 | -1 2114 | 1 2115 | 0 2116 | 0 2117 | 1 2118 | 0 2119 | 1 2120 | 0 2121 | -1 2122 | 0 2123 | 0 2124 | 0 2125 | 0 2126 | -1 2127 | -1 2128 | 0 2129 | 0 2130 | -1 2131 | -1 2132 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2771 | -1 2772 | 1 2773 | 1 2774 | -1 2775 | 1 2776 | 1 2777 | 0 2778 | 0 2779 | -1 2780 | 1 2781 | -1 2782 | 0 2783 | -1 2784 | 1 2785 | -1 2786 | 1 2787 | 0 2788 | 0 2789 | -1 2790 | 0 2791 | 1 2792 | 1 2793 | -1 2794 | -1 2795 | 1 2796 | 1 2797 | -1 2798 | 0 2799 | -1 2800 | -1 2801 | -1 2802 | 0 2803 | 0 2804 | -1 2805 | -1 2806 | 0 2807 | 1 2808 | -1 2809 | 1 2810 | 0 2811 | 0 2812 | 1 2813 | 1 2814 | -1 2815 | 1 2816 | -1 2817 | 1 2818 | 0 2819 | -1 2820 | -1 2821 | 1 2822 | 0 2823 | 1 2824 | -1 2825 | 1 2826 | 0 2827 | 0 2828 | -1 2829 | 0 2830 | -1 2831 | 1 2832 | 1 2833 | 0 2834 | -1 2835 | 1 2836 | -1 2837 | -1 2838 | 0 2839 | 0 2840 | 0 2841 | -1 2842 | 0 2843 | 1 2844 | 0 2845 | -1 2846 | 1 2847 | -1 2848 | 1 2849 | 1 2850 | 0 2851 | 1 2852 | -1 2853 | 1 2854 | 0 2855 | 0 2856 | -1 2857 | -1 2858 | 1 2859 | 1 2860 | 1 2861 | 1 2862 | -1 2863 | 1 2864 | 1 2865 | -1 2866 | -1 2867 | -1 2868 | 0 2869 | 1 2870 | -1 2871 | 0 2872 | 0 2873 | -1 2874 | 0 2875 | -1 2876 | 1 2877 | 0 2878 | -1 2879 | 0 2880 | 1 2881 | -1 2882 | 1 2883 | 0 2884 | 1 2885 | 0 2886 | -1 2887 | -1 2888 | 1 2889 | 1 2890 | 1 2891 | 1 2892 | 0 2893 | 0 2894 | -1 2895 | -1 2896 | 1 2897 | -1 2898 | 1 2899 | 0 2900 | 0 2901 | 1 2902 | 0 2903 | 0 2904 | 0 2905 | 1 2906 | 1 2907 | 0 2908 | -1 2909 | 1 2910 | 0 2911 | 0 2912 | 0 2913 | 1 2914 | 1 2915 | 0 2916 | 0 2917 | 1 2918 | -1 2919 | -1 2920 | 1 2921 | 0 2922 | 1 2923 | -1 2924 | 0 2925 | -1 2926 | 0 2927 | 1 2928 | -1 2929 | 1 2930 | 1 2931 | 1 2932 | 1 2933 | 1 2934 | -1 2935 | 1 2936 | 0 2937 | -1 2938 | -1 2939 | 0 2940 | 1 2941 | 1 2942 | -1 2943 | 1 2944 | 0 2945 | 1 2946 | 1 2947 | 0 2948 | 1 2949 | 1 2950 | 1 2951 | 1 2952 | 0 2953 | 0 2954 | 0 2955 | -1 2956 | 0 2957 | 1 2958 | 0 2959 | 1 2960 | 0 2961 | 0 2962 | 1 2963 | 1 2964 | 1 2965 | 0 2966 | -1 2967 | 0 2968 | 1 2969 | 1 2970 | -1 2971 | -1 2972 | 0 2973 | -1 2974 | 0 2975 | -1 2976 | 0 2977 | 0 2978 | -1 2979 | -1 2980 | 0 2981 | 0 2982 | -1 2983 | -1 2984 | -1 2985 | 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3092 | 1 3093 | -1 3094 | -1 3095 | 1 3096 | 0 3097 | 1 3098 | 0 3099 | 1 3100 | -1 3101 | 1 3102 | -1 3103 | 0 3104 | -1 3105 | 0 3106 | -1 3107 | -1 3108 | 0 3109 | -1 3110 | 1 3111 | 1 3112 | -1 3113 | -1 3114 | 1 3115 | -1 3116 | 1 3117 | 1 3118 | -1 3119 | -1 3120 | 0 3121 | -1 3122 | -1 3123 | -1 3124 | -1 3125 | 0 3126 | 0 3127 | 1 3128 | 0 3129 | 0 3130 | 0 3131 | -1 3132 | -1 3133 | 0 3134 | 0 3135 | -1 3136 | 1 3137 | -1 3138 | 1 3139 | 0 3140 | 0 3141 | -1 3142 | 1 3143 | 1 3144 | -1 3145 | 1 3146 | -1 3147 | 1 3148 | 0 3149 | 1 3150 | -1 3151 | 1 3152 | -1 3153 | -1 3154 | 1 3155 | -1 3156 | 0 3157 | -1 3158 | -1 3159 | -1 3160 | 1 3161 | -1 3162 | 1 3163 | 1 3164 | -1 3165 | 0 3166 | 1 3167 | -1 3168 | 0 3169 | -1 3170 | 0 3171 | 0 3172 | -1 3173 | 0 3174 | -1 3175 | 0 3176 | -1 3177 | 0 3178 | -1 3179 | 0 3180 | 1 3181 | 0 3182 | 1 3183 | 1 3184 | 1 3185 | 1 3186 | -1 3187 | 0 3188 | -1 3189 | -1 3190 | 1 3191 | 1 3192 | 1 3193 | 1 3194 | 0 3195 | 0 3196 | 1 3197 | -1 3198 | 1 3199 | 0 3200 | 0 3201 | -1 3202 | 0 3203 | 0 3204 | 1 3205 | -1 3206 | -1 3207 | -1 3208 | -1 3209 | 0 3210 | -1 3211 | 1 3212 | -1 3213 | 1 3214 | -1 3215 | 0 3216 | 1 3217 | 0 3218 | 1 3219 | 1 3220 | -1 3221 | 0 3222 | 0 3223 | -1 3224 | 0 3225 | 1 3226 | -1 3227 | 0 3228 | -1 3229 | 1 3230 | 0 3231 | 1 3232 | 1 3233 | 1 3234 | 1 3235 | 1 3236 | -1 3237 | 0 3238 | -1 3239 | -1 3240 | -1 3241 | 0 3242 | 1 3243 | 1 3244 | 0 3245 | 1 3246 | 1 3247 | 0 3248 | -1 3249 | 0 3250 | -1 3251 | -1 3252 | -1 3253 | 1 3254 | 0 3255 | 1 3256 | -1 3257 | -1 3258 | -1 3259 | 1 3260 | -1 3261 | 0 3262 | -1 3263 | 1 3264 | -1 3265 | 1 3266 | 1 3267 | 0 3268 | 0 3269 | 1 3270 | -1 3271 | -1 3272 | 0 3273 | 1 3274 | 1 3275 | -1 3276 | 1 3277 | 1 3278 | -1 3279 | 0 3280 | -1 3281 | -1 3282 | 0 3283 | 1 3284 | -1 3285 | -1 3286 | -1 3287 | 1 3288 | -1 3289 | 1 3290 | 0 3291 | 1 3292 | -1 3293 | -1 3294 | -1 3295 | 1 3296 | 0 3297 | 1 3298 | -1 3299 | -1 3300 | 1 3301 | 0 3302 | 0 3303 | -1 3304 | -1 3305 | -1 3306 | 0 3307 | -1 3308 | -1 3309 | 0 3310 | 0 3311 | 1 3312 | 0 3313 | 0 3314 | -1 3315 | 0 3316 | 1 3317 | -1 3318 | -1 3319 | -1 3320 | -1 3321 | 1 3322 | 1 3323 | 0 3324 | -1 3325 | 1 3326 | -1 3327 | -1 3328 | 1 3329 | 0 3330 | -1 3331 | 0 3332 | 1 3333 | 1 3334 | 0 3335 | -1 3336 | -1 3337 | 1 3338 | 0 3339 | -1 3340 | -1 3341 | 1 3342 | 1 3343 | 0 3344 | -1 3345 | 1 3346 | -1 3347 | 0 3348 | 0 3349 | -1 3350 | -1 3351 | -1 3352 | -1 3353 | -1 3354 | 0 3355 | 0 3356 | 0 3357 | 0 3358 | 1 3359 | 0 3360 | 0 3361 | 1 3362 | -1 3363 | -1 3364 | -1 3365 | -1 3366 | 0 3367 | -1 3368 | 0 3369 | 0 3370 | 0 3371 | 0 3372 | 1 3373 | -1 3374 | 1 3375 | 0 3376 | 0 3377 | 1 3378 | 1 3379 | 0 3380 | 1 3381 | 0 3382 | 1 3383 | 1 3384 | 1 3385 | 1 3386 | 0 3387 | 1 3388 | 1 3389 | -1 3390 | 0 3391 | -1 3392 | 1 3393 | -1 3394 | -1 3395 | -1 3396 | 1 3397 | 0 3398 | 0 3399 | 0 3400 | -1 3401 | 0 3402 | -1 3403 | 0 3404 | -1 3405 | -1 3406 | 0 3407 | 1 3408 | 1 3409 | 0 3410 | 1 3411 | 1 3412 | 0 3413 | 0 3414 | 0 3415 | 0 3416 | -1 3417 | -1 3418 | -1 3419 | 1 3420 | 1 3421 | -1 3422 | 1 3423 | 0 3424 | 1 3425 | 0 3426 | -1 3427 | 0 3428 | -1 3429 | -1 3430 | -1 3431 | 1 3432 | -1 3433 | -1 3434 | -1 3435 | 1 3436 | 1 3437 | -1 3438 | -1 3439 | -1 3440 | 1 3441 | 0 3442 | 1 3443 | -1 3444 | 1 3445 | -1 3446 | -1 3447 | 0 3448 | -1 3449 | -1 3450 | 0 3451 | -1 3452 | 1 3453 | 1 3454 | 1 3455 | 0 3456 | -1 3457 | 1 3458 | 1 3459 | -1 3460 | -1 3461 | -1 3462 | -1 3463 | 0 3464 | 1 3465 | -1 3466 | 0 3467 | 0 3468 | -1 3469 | -1 3470 | -1 3471 | 0 3472 | -1 3473 | 0 3474 | 0 3475 | 1 3476 | -1 3477 | -1 3478 | 1 3479 | 1 3480 | -1 3481 | 1 3482 | 1 3483 | 1 3484 | 1 3485 | -1 3486 | 0 3487 | -1 3488 | -1 3489 | 1 3490 | 0 3491 | 1 3492 | 1 3493 | -1 3494 | 1 3495 | 1 3496 | 1 3497 | 1 3498 | -1 3499 | 1 3500 | -1 3501 | -1 3502 | 0 3503 | 0 3504 | 0 3505 | 0 3506 | 1 3507 | 0 3508 | -1 3509 | 0 3510 | 0 3511 | -1 3512 | -1 3513 | 0 3514 | 0 3515 | -1 3516 | -1 3517 | 0 3518 | -1 3519 | 1 3520 | 0 3521 | -1 3522 | 1 3523 | -1 3524 | 1 3525 | 1 3526 | 1 3527 | -1 3528 | 0 3529 | -1 3530 | 1 3531 | -1 3532 | 0 3533 | 1 3534 | 1 3535 | 1 3536 | 0 3537 | -1 3538 | 0 3539 | 1 3540 | -1 3541 | -1 3542 | 0 3543 | 0 3544 | 1 3545 | -1 3546 | 0 3547 | 0 3548 | 1 3549 | 0 3550 | 1 3551 | -1 3552 | 1 3553 | -1 3554 | -1 3555 | -1 3556 | -1 3557 | 0 3558 | -1 3559 | 1 3560 | -1 3561 | 0 3562 | 1 3563 | -1 3564 | -1 3565 | -1 3566 | -1 3567 | 0 3568 | -1 3569 | 0 3570 | -1 3571 | 1 3572 | 0 3573 | 0 3574 | -1 3575 | 0 3576 | 1 3577 | -1 3578 | 1 3579 | 1 3580 | -1 3581 | -1 3582 | 0 3583 | -1 3584 | 0 3585 | -1 3586 | 1 3587 | -1 3588 | 1 3589 | 1 3590 | 1 3591 | 1 3592 | -1 3593 | 1 3594 | 1 3595 | -1 3596 | 0 3597 | 1 3598 | -1 3599 | 1 3600 | 1 3601 | -1 3602 | 1 3603 | 1 3604 | 0 3605 | 1 3606 | 1 3607 | 1 3608 | 1 3609 | 1 3610 | -1 3611 | -1 3612 | 0 3613 | 1 3614 | 0 3615 | 0 3616 | 1 3617 | 1 3618 | 0 3619 | -1 3620 | -1 3621 | 0 3622 | 1 3623 | 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0 3731 | 1 3732 | 1 3733 | -1 3734 | 0 3735 | 1 3736 | 0 3737 | 0 3738 | 1 3739 | 0 3740 | 0 3741 | -1 3742 | -1 3743 | 1 3744 | 1 3745 | 1 3746 | -1 3747 | 0 3748 | 1 3749 | 1 3750 | -1 3751 | 0 3752 | -1 3753 | -1 3754 | 1 3755 | 0 3756 | 0 3757 | 0 3758 | 0 3759 | -1 3760 | -1 3761 | 0 3762 | 1 3763 | -1 3764 | 1 3765 | -1 3766 | -1 3767 | -1 3768 | -1 3769 | 0 3770 | -1 3771 | -1 3772 | 0 3773 | -1 3774 | 1 3775 | -1 3776 | 0 3777 | -1 3778 | -1 3779 | -1 3780 | -1 3781 | -1 3782 | 1 3783 | 1 3784 | 0 3785 | -1 3786 | -1 3787 | -1 3788 | 0 3789 | -1 3790 | -1 3791 | 1 3792 | -1 3793 | -1 3794 | 0 3795 | -1 3796 | 1 3797 | 0 3798 | -1 3799 | 1 3800 | -1 3801 | 0 3802 | 0 3803 | -1 3804 | 0 3805 | 0 3806 | -1 3807 | 0 3808 | 1 3809 | 0 3810 | 1 3811 | -1 3812 | 0 3813 | 0 3814 | 0 3815 | 1 3816 | 0 3817 | -1 3818 | -1 3819 | 1 3820 | 0 3821 | -1 3822 | -1 3823 | -1 3824 | -1 3825 | 0 3826 | 0 3827 | 0 3828 | -1 3829 | 0 3830 | 1 3831 | 1 3832 | -1 3833 | 1 3834 | 1 3835 | 1 3836 | 0 3837 | -1 3838 | 0 3839 | -1 3840 | 1 3841 | -1 3842 | -1 3843 | 1 3844 | 1 3845 | 1 3846 | 1 3847 | -1 3848 | 1 3849 | -1 3850 | -1 3851 | -1 3852 | 1 3853 | -1 3854 | 1 3855 | 0 3856 | -1 3857 | 0 3858 | 0 3859 | -1 3860 | 0 3861 | 1 3862 | 0 3863 | -1 3864 | 1 3865 | 1 3866 | -1 3867 | -1 3868 | 0 3869 | 0 3870 | -1 3871 | 0 3872 | 0 3873 | 1 3874 | 1 3875 | 0 3876 | 0 3877 | 0 3878 | 1 3879 | 1 3880 | -1 3881 | 0 3882 | 1 3883 | -1 3884 | -1 3885 | 1 3886 | 0 3887 | 0 3888 | 1 3889 | -1 3890 | 0 3891 | 0 3892 | 0 3893 | -1 3894 | 0 3895 | -1 3896 | -1 3897 | -1 3898 | -1 3899 | 1 3900 | 1 3901 | -1 3902 | 1 3903 | 0 3904 | 1 3905 | -1 3906 | -1 3907 | 0 3908 | 1 3909 | 0 3910 | 0 3911 | 1 3912 | -1 3913 | 0 3914 | 0 3915 | 0 3916 | -1 3917 | 1 3918 | 1 3919 | 0 3920 | 1 3921 | 0 3922 | -1 3923 | 1 3924 | -1 3925 | -1 3926 | 1 3927 | -1 3928 | 1 3929 | 1 3930 | -1 3931 | 1 3932 | 1 3933 | -1 3934 | 1 3935 | 1 3936 | -1 3937 | 1 3938 | 0 3939 | 1 3940 | -1 3941 | -1 3942 | 1 3943 | 1 3944 | 0 3945 | -1 3946 | 1 3947 | -1 3948 | 0 3949 | -1 3950 | -1 3951 | 1 3952 | -1 3953 | 1 3954 | 1 3955 | 1 3956 | -1 3957 | 1 3958 | 0 3959 | 1 3960 | -1 3961 | -1 3962 | 0 3963 | 0 3964 | 0 3965 | 0 3966 | 1 3967 | 1 3968 | -1 3969 | 1 3970 | 1 3971 | -1 3972 | -1 3973 | 1 3974 | 0 3975 | 0 3976 | 0 3977 | 0 3978 | 1 3979 | -1 3980 | 0 3981 | -1 3982 | -1 3983 | 0 3984 | -1 3985 | 1 3986 | -1 3987 | 0 3988 | 0 3989 | 0 3990 | 1 3991 | -1 3992 | 1 3993 | -1 3994 | 0 3995 | -1 3996 | -1 3997 | -1 3998 | 1 3999 | -1 4000 | -1 4001 | -1 4002 | 1 4003 | 1 4004 | 0 4005 | -1 4006 | 0 4007 | 0 4008 | 0 4009 | -1 4010 | 0 4011 | 1 4012 | -1 4013 | 1 4014 | -1 4015 | -1 4016 | 1 4017 | -1 4018 | 0 4019 | 1 4020 | 1 4021 | -1 4022 | 0 4023 | -1 4024 | 0 4025 | 1 4026 | 1 4027 | 0 4028 | -1 4029 | 1 4030 | 0 4031 | 1 4032 | -1 4033 | 1 4034 | 0 4035 | 1 4036 | 0 4037 | -1 4038 | -1 4039 | 1 4040 | -1 4041 | -1 4042 | 1 4043 | 1 4044 | -1 4045 | 1 4046 | -1 4047 | -1 4048 | 0 4049 | 1 4050 | 0 4051 | 1 4052 | 1 4053 | 0 4054 | -1 4055 | 1 4056 | 1 4057 | -1 4058 | -1 4059 | 0 4060 | -1 4061 | 0 4062 | 0 4063 | -1 4064 | 0 4065 | 0 4066 | -1 4067 | -1 4068 | 0 4069 | 0 4070 | -1 4071 | 0 4072 | 0 4073 | -1 4074 | -1 4075 | 1 4076 | -1 4077 | -1 4078 | 0 4079 | 0 4080 | -1 4081 | 0 4082 | 1 4083 | -1 4084 | 0 4085 | 1 4086 | -1 4087 | 0 4088 | 0 4089 | -1 4090 | 0 4091 | -1 4092 | -1 4093 | -1 4094 | 1 4095 | -1 4096 | -1 4097 | -1 4098 | 1 4099 | 0 4100 | -1 4101 | 1 4102 | 1 4103 | 1 4104 | 0 4105 | -1 4106 | 1 4107 | 0 4108 | -1 4109 | 0 4110 | 0 4111 | 1 4112 | -1 4113 | 1 4114 | -1 4115 | -1 4116 | -1 4117 | -1 4118 | 1 4119 | 1 4120 | 0 4121 | 0 4122 | 1 4123 | 1 4124 | 0 4125 | 1 4126 | 0 4127 | 1 4128 | 0 4129 | 0 4130 | -1 4131 | -1 4132 | -1 4133 | 1 4134 | 1 4135 | -1 4136 | 1 4137 | -1 4138 | 1 4139 | -1 4140 | 1 4141 | 1 4142 | 1 4143 | 0 4144 | 1 4145 | 1 4146 | 1 4147 | -1 4148 | 1 4149 | -1 4150 | -1 4151 | -1 4152 | 1 4153 | -1 4154 | 1 4155 | 0 4156 | 0 4157 | 0 4158 | -1 4159 | -1 4160 | 1 4161 | 1 4162 | 0 4163 | 1 4164 | -1 4165 | 1 4166 | -1 4167 | 0 4168 | 0 4169 | 0 4170 | -1 4171 | -1 4172 | -1 4173 | -1 4174 | 0 4175 | 1 4176 | 1 4177 | 1 4178 | -1 4179 | 0 4180 | -1 4181 | -1 4182 | -1 4183 | -1 4184 | -1 4185 | 0 4186 | -1 4187 | 0 4188 | -1 4189 | -1 4190 | 0 4191 | 1 4192 | 0 4193 | 0 4194 | -1 4195 | 0 4196 | -1 4197 | -1 4198 | 0 4199 | 0 4200 | 0 4201 | -1 4202 | 1 4203 | 0 4204 | 1 4205 | -1 4206 | 0 4207 | -1 4208 | 0 4209 | 1 4210 | 0 4211 | 0 4212 | -1 4213 | -1 4214 | -1 4215 | 0 4216 | 0 4217 | 1 4218 | 0 4219 | 1 4220 | 1 4221 | -1 4222 | 1 4223 | -1 4224 | 0 4225 | 1 4226 | 0 4227 | 0 4228 | -1 4229 | -1 4230 | 0 4231 | 0 4232 | 0 4233 | 0 4234 | 0 4235 | -1 4236 | -1 4237 | -1 4238 | 1 4239 | 0 4240 | 0 4241 | -1 4242 | 0 4243 | 0 4244 | 0 4245 | 1 4246 | 0 4247 | 0 4248 | -1 4249 | -1 4250 | 1 4251 | 1 4252 | 0 4253 | 1 4254 | 1 4255 | -1 4256 | 1 4257 | 1 4258 | 1 4259 | 0 4260 | 1 4261 | 1 4262 | 1 4263 | 1 4264 | 1 4265 | -1 4266 | 1 4267 | -1 4268 | -1 4269 | 0 4270 | 1 4271 | -1 4272 | -1 4273 | -1 4274 | -1 4275 | -1 4276 | -1 4277 | 1 4278 | 0 4279 | 0 4280 | -1 4281 | 1 4282 | 1 4283 | 0 4284 | 0 4285 | 1 4286 | 0 4287 | 0 4288 | 1 4289 | 0 4290 | 0 4291 | 1 4292 | -1 4293 | 0 4294 | 1 4295 | 0 4296 | 0 4297 | -1 4298 | -1 4299 | 1 4300 | -1 4301 | 1 4302 | -1 4303 | 1 4304 | 1 4305 | -1 4306 | 1 4307 | -1 4308 | 0 4309 | 0 4310 | -1 4311 | -1 4312 | -1 4313 | -1 4314 | 0 4315 | 0 4316 | -1 4317 | 1 4318 | 1 4319 | 0 4320 | -1 4321 | 0 4322 | 0 4323 | 1 4324 | 0 4325 | 0 4326 | -1 4327 | -1 4328 | -1 4329 | -1 4330 | 1 4331 | 0 4332 | 1 4333 | -1 4334 | 1 4335 | 1 4336 | 0 4337 | 0 4338 | 0 4339 | 0 4340 | -1 4341 | 1 4342 | 0 4343 | -1 4344 | 0 4345 | -1 4346 | 0 4347 | -1 4348 | -1 4349 | -1 4350 | 0 4351 | -1 4352 | -1 4353 | 0 4354 | 0 4355 | -1 4356 | 1 4357 | 1 4358 | 0 4359 | 0 4360 | 1 4361 | 1 4362 | 0 4363 | 1 4364 | 1 4365 | -1 4366 | 0 4367 | 1 4368 | -1 4369 | 1 4370 | 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| 而且 601 | 而况 602 | 而后 603 | 而外 604 | 而已 605 | 而是 606 | 而言 607 | 能 608 | 能否 609 | 腾 610 | 自 611 | 自个儿 612 | 自从 613 | 自各儿 614 | 自后 615 | 自家 616 | 自己 617 | 自打 618 | 自身 619 | 至 620 | 至于 621 | 至今 622 | 至若 623 | 致 624 | 般的 625 | 若 626 | 若夫 627 | 若是 628 | 若果 629 | 若非 630 | 莫不然 631 | 莫如 632 | 莫若 633 | 虽 634 | 虽则 635 | 虽然 636 | 虽说 637 | 被 638 | 要 639 | 要不 640 | 要不是 641 | 要不然 642 | 要么 643 | 要是 644 | 譬喻 645 | 譬如 646 | 让 647 | 许多 648 | 论 649 | 设使 650 | 设或 651 | 设若 652 | 诚如 653 | 诚然 654 | 该 655 | 说来 656 | 诸 657 | 诸位 658 | 诸如 659 | 谁 660 | 谁人 661 | 谁料 662 | 谁知 663 | 贼死 664 | 赖以 665 | 赶 666 | 起 667 | 起见 668 | 趁 669 | 趁着 670 | 越是 671 | 距 672 | 跟 673 | 较 674 | 较之 675 | 边 676 | 过 677 | 还 678 | 还是 679 | 还有 680 | 还要 681 | 这 682 | 这一来 683 | 这个 684 | 这么 685 | 这么些 686 | 这么样 687 | 这么点儿 688 | 这些 689 | 这会儿 690 | 这儿 691 | 这就是说 692 | 这时 693 | 这样 694 | 这次 695 | 这般 696 | 这边 697 | 这里 698 | 进而 699 | 连 700 | 连同 701 | 逐步 702 | 通过 703 | 遵循 704 | 遵照 705 | 那 706 | 那个 707 | 那么 708 | 那么些 709 | 那么样 710 | 那些 711 | 那会儿 712 | 那儿 713 | 那时 714 | 那样 715 | 那般 716 | 那边 717 | 那里 718 | 都 719 | 鄙人 720 | 鉴于 721 | 针对 722 | 阿 723 | 除 724 | 除了 725 | 除外 726 | 除开 727 | 除此之外 728 | 除非 729 | 随 730 | 随后 731 | 随时 732 | 随着 733 | 难道说 734 | 非但 735 | 非徒 736 | 非特 737 | 非独 738 | 靠 739 | 顺 740 | 顺着 741 | 首先 742 | ！ 743 | ， 744 | ： 745 | ； 746 | ？ 747 | 748 | 749 | … 750 | ... 751 | ...... 752 | @ 753 | ___ 754 | 【 755 | 】 756 | / 757 | # 758 | ; 759 | , 760 | " 761 | ' 762 | ( 763 | ) 764 | : 765 | 「 766 | 」 767 | [ 768 | ] 769 | - 770 | | 771 | ( 772 | ) -------------------------------------------------------------------------------- /lab/svm/index.py: -------------------------------------------------------------------------------- 1 | from sklearn.svm import SVC 2 | import jieba as jb 3 | import pandas as pd 4 | from gensim.models.doc2vec import TaggedDocument 5 | from gensim.models import Doc2Vec 6 | import numpy as np 7 | vector_dimension = 300 8 | 9 | 10 | ### main ### 11 | print("main") 12 | 13 | #### load data #### 14 | print("load data") 15 | ##### train data ##### 16 | print("train data") 17 | df = pd.read_csv("../data/train.csv") 18 | df.fillna('', inplace=True) 19 | 20 | train_data = [] 21 | train_label = [] 22 | 23 | for i in range(0, df.shape[0]): 24 | train_data.append(jb.lcut(df["content"][i] + df["comment_all"][i])) 25 | train_label.append(df["label"][i]) 26 | 27 | ##### test data ##### 28 | print("test data") 29 | test_data = [] 30 | df = pd.read_csv("../data/test.csv") 31 | df.fillna('', inplace=True) 32 | 33 | for i in range(0, df.shape[0]): 34 | test_data.append(jb.lcut(df["content"][i] + df["comment_all"][i])) 35 | 36 | #### word to vector #### 37 | print("word to vector") 38 | x = [] 39 | 40 | total = [] 41 | total.extend(train_data) 42 | total.extend(test_data) 43 | 44 | for i, data in enumerate(total): 45 | x.append(TaggedDocument(data, ['Text' + '_%s' % str(i)])) 46 | 47 | text_model = Doc2Vec(vector_size=vector_dimension) 48 | text_model.build_vocab(x) 49 | text_model.train(x, total_examples=text_model.corpus_count, 50 | epochs=10) 51 | 52 | train_size = len(train_data) 53 | test_size = len(test_data) 54 | 55 | train_arrays = np.zeros((train_size, vector_dimension)) 56 | test_arrays = np.zeros((test_size, vector_dimension)) 57 | train_labels = np.zeros(train_size) 58 | test_labels = np.zeros(test_size) 59 | 60 | for i in range(train_size): 61 | train_arrays[i] = text_model.docvecs['Text_' + str(i)] 62 | train_labels[i] = train_label[i] 63 | 64 | j = 0 65 | for i in range(train_size, train_size + test_size): 66 | test_arrays[j] = text_model.docvecs['Text_' + str(i)] 67 | j = j + 1 68 | 69 | 70 | #### fit model #### 71 | print("fit model") 72 | clf = SVC() 73 | clf.fit(train_arrays, train_labels) 74 | y_pred = clf.predict(test_arrays) 75 | np.savetxt('./result.txt', y_pred, fmt="%d", delimiter=" ") 76 | -------------------------------------------------------------------------------- /lab/svm/result.txt: 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1927 | 1 1928 | -1 1929 | 0 1930 | -1 1931 | 0 1932 | 0 1933 | 0 1934 | 0 1935 | -1 1936 | 1 1937 | 1 1938 | 0 1939 | 0 1940 | 0 1941 | 1 1942 | 1 1943 | 0 1944 | 0 1945 | 1 1946 | -1 1947 | 1 1948 | 0 1949 | 1 1950 | 0 1951 | 1 1952 | 1 1953 | -1 1954 | -1 1955 | 1 1956 | -1 1957 | 0 1958 | 1 1959 | 1 1960 | 1 1961 | 0 1962 | -1 1963 | 1 1964 | 0 1965 | -1 1966 | 1 1967 | 1 1968 | -1 1969 | -1 1970 | 0 1971 | -1 1972 | -1 1973 | -1 1974 | 1 1975 | -1 1976 | 0 1977 | 1 1978 | 0 1979 | 1 1980 | 0 1981 | 1 1982 | 0 1983 | 1 1984 | 0 1985 | 1 1986 | 1 1987 | -1 1988 | -1 1989 | 0 1990 | 0 1991 | 0 1992 | 1 1993 | -1 1994 | -1 1995 | 1 1996 | -1 1997 | 0 1998 | 0 1999 | 0 2000 | 1 2001 | 0 2002 | -1 2003 | 0 2004 | -1 2005 | 0 2006 | -1 2007 | 1 2008 | 1 2009 | -1 2010 | -1 2011 | 0 2012 | -1 2013 | 1 2014 | -1 2015 | 1 2016 | 0 2017 | 0 2018 | -1 2019 | 1 2020 | -1 2021 | 0 2022 | 1 2023 | 1 2024 | 1 2025 | -1 2026 | 1 2027 | -1 2028 | -1 2029 | 1 2030 | -1 2031 | 1 2032 | 0 2033 | 1 2034 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2248 | -1 2249 | 0 2250 | 0 2251 | -1 2252 | 1 2253 | -1 2254 | 1 2255 | 1 2256 | 0 2257 | 0 2258 | 0 2259 | 1 2260 | 0 2261 | -1 2262 | -1 2263 | 0 2264 | 0 2265 | 0 2266 | 0 2267 | 1 2268 | -1 2269 | 1 2270 | -1 2271 | -1 2272 | 1 2273 | 0 2274 | 1 2275 | 0 2276 | 0 2277 | -1 2278 | 0 2279 | 1 2280 | -1 2281 | 0 2282 | 0 2283 | 1 2284 | 1 2285 | -1 2286 | -1 2287 | 1 2288 | 1 2289 | -1 2290 | 1 2291 | 1 2292 | 0 2293 | -1 2294 | 0 2295 | -1 2296 | -1 2297 | 0 2298 | 1 2299 | 0 2300 | 0 2301 | -1 2302 | -1 2303 | 1 2304 | 0 2305 | -1 2306 | 0 2307 | 1 2308 | 0 2309 | 1 2310 | 0 2311 | 1 2312 | -1 2313 | -1 2314 | 0 2315 | 1 2316 | 1 2317 | 0 2318 | 1 2319 | 1 2320 | 1 2321 | -1 2322 | -1 2323 | -1 2324 | 1 2325 | 0 2326 | -1 2327 | -1 2328 | -1 2329 | 1 2330 | -1 2331 | 1 2332 | 1 2333 | -1 2334 | 0 2335 | 0 2336 | -1 2337 | 0 2338 | -1 2339 | -1 2340 | 1 2341 | 1 2342 | -1 2343 | 0 2344 | -1 2345 | -1 2346 | 0 2347 | 1 2348 | -1 2349 | 0 2350 | -1 2351 | -1 2352 | 0 2353 | 1 2354 | 1 2355 | 1 2356 | 0 2357 | -1 2358 | 1 2359 | 0 2360 | 1 2361 | 1 2362 | 0 2363 | 1 2364 | -1 2365 | 1 2366 | -1 2367 | 0 2368 | -1 2369 | 0 2370 | 0 2371 | -1 2372 | 0 2373 | 0 2374 | -1 2375 | 0 2376 | 0 2377 | 1 2378 | 1 2379 | -1 2380 | -1 2381 | 0 2382 | 1 2383 | -1 2384 | 1 2385 | -1 2386 | -1 2387 | 0 2388 | 1 2389 | -1 2390 | 0 2391 | -1 2392 | 0 2393 | 0 2394 | 1 2395 | -1 2396 | 0 2397 | -1 2398 | -1 2399 | -1 2400 | -1 2401 | 0 2402 | -1 2403 | -1 2404 | 0 2405 | 1 2406 | -1 2407 | -1 2408 | -1 2409 | -1 2410 | -1 2411 | 1 2412 | 1 2413 | 0 2414 | 1 2415 | -1 2416 | 0 2417 | 1 2418 | 1 2419 | 0 2420 | 0 2421 | 0 2422 | 0 2423 | 0 2424 | -1 2425 | 1 2426 | 0 2427 | 0 2428 | 0 2429 | -1 2430 | 0 2431 | 1 2432 | 0 2433 | 0 2434 | 1 2435 | -1 2436 | 0 2437 | -1 2438 | 1 2439 | 1 2440 | 1 2441 | -1 2442 | 0 2443 | -1 2444 | -1 2445 | 1 2446 | 0 2447 | -1 2448 | 1 2449 | 0 2450 | -1 2451 | -1 2452 | 1 2453 | -1 2454 | 0 2455 | 1 2456 | 0 2457 | -1 2458 | 1 2459 | -1 2460 | 1 2461 | 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4596 | 0 4597 | -1 4598 | -1 4599 | 1 4600 | 0 4601 | -1 4602 | 0 4603 | -1 4604 | 1 4605 | 1 4606 | -1 4607 | 1 4608 | -1 4609 | 0 4610 | 1 4611 | 1 4612 | -1 4613 | 0 4614 | 1 4615 | -1 4616 | -1 4617 | -1 4618 | 0 4619 | -1 4620 | -1 4621 | 0 4622 | 1 4623 | -1 4624 | 0 4625 | -1 4626 | 0 4627 | 1 4628 | 1 4629 | 0 4630 | 0 4631 | 0 4632 | 0 4633 | -1 4634 | -1 4635 | 0 4636 | -1 4637 | -1 4638 | 0 4639 | 1 4640 | 1 4641 | -1 4642 | 1 4643 | 0 4644 | 0 4645 | 0 4646 | -1 4647 | 0 4648 | 1 4649 | 1 4650 | -1 4651 | -1 4652 | 1 4653 | -1 4654 | -1 4655 | -1 4656 | -1 4657 | 1 4658 | -1 4659 | 1 4660 | 0 4661 | -1 4662 | 0 4663 | -1 4664 | -1 4665 | 0 4666 | 1 4667 | 0 4668 | -1 4669 | 0 4670 | -1 4671 | -1 4672 | 1 4673 | -1 4674 | -1 4675 | 0 4676 | -1 4677 | -1 4678 | -1 4679 | 0 4680 | 1 4681 | -1 4682 | 0 4683 | -1 4684 | 1 4685 | 0 4686 | -1 4687 | -1 4688 | 1 4689 | -1 4690 | 0 4691 | 1 4692 | -1 4693 | -1 4694 | 1 4695 | 1 4696 | 1 4697 | 0 4698 | 0 4699 | 0 4700 | 0 4701 | 1 4702 | 1 4703 | -1 4704 | -1 4705 | 0 4706 | 1 4707 | 0 4708 | -1 4709 | 0 4710 | -1 4711 | 1 4712 | -1 4713 | 0 4714 | 0 4715 | 1 4716 | -1 4717 | 1 4718 | -1 4719 | -1 4720 | 1 4721 | -1 4722 | -1 4723 | -1 4724 | 1 4725 | 0 4726 | 0 4727 | -1 4728 | 1 4729 | 0 4730 | -1 4731 | 1 4732 | -1 4733 | 1 4734 | 0 4735 | 1 4736 | -1 4737 | 0 4738 | 1 4739 | -1 4740 | 0 4741 | 0 4742 | -1 4743 | -1 4744 | 0 4745 | 0 4746 | 1 4747 | -1 4748 | 0 4749 | 0 4750 | 0 4751 | -1 4752 | 1 4753 | 0 4754 | 0 4755 | 1 4756 | 0 4757 | 0 4758 | -1 4759 | 0 4760 | -1 4761 | -1 4762 | 1 4763 | 0 4764 | 0 4765 | 0 4766 | 1 4767 | -1 4768 | -1 4769 | 1 4770 | -1 4771 | 1 4772 | 0 4773 | -1 4774 | 1 4775 | 0 4776 | 1 4777 | -1 4778 | 0 4779 | 1 4780 | 0 4781 | 1 4782 | -1 4783 | 1 4784 | 0 4785 | 1 4786 | 1 4787 | 0 4788 | 1 4789 | 0 4790 | 1 4791 | 0 4792 | 1 4793 | -1 4794 | 0 4795 | -1 4796 | -1 4797 | 0 4798 | 1 4799 | -1 4800 | -1 4801 | 1 4802 | 1 4803 | 1 4804 | -1 4805 | 0 4806 | 0 4807 | -1 4808 | 0 4809 | -1 4810 | 1 4811 | -1 4812 | 0 4813 | -1 4814 | 0 4815 | 0 4816 | -1 4817 | 1 4818 | 1 4819 | -1 4820 | -1 4821 | -1 4822 | 0 4823 | -1 4824 | -1 4825 | 1 4826 | 0 4827 | 0 4828 | 1 4829 | 1 4830 | 1 4831 | 1 4832 | -1 4833 | 0 4834 | -1 4835 | 1 4836 | 1 4837 | 0 4838 | 1 4839 | -1 4840 | 0 4841 | 0 4842 | -1 4843 | -1 4844 | 1 4845 | 1 4846 | -1 4847 | -1 4848 | 1 4849 | -1 4850 | -1 4851 | 0 4852 | 1 4853 | 0 4854 | 1 4855 | -1 4856 | -1 4857 | 0 4858 | 1 4859 | 0 4860 | 1 4861 | 0 4862 | -1 4863 | 0 4864 | 1 4865 | 1 4866 | -1 4867 | 0 4868 | -1 4869 | 0 4870 | -1 4871 | 0 4872 | -1 4873 | 1 4874 | 0 4875 | -1 4876 | 0 4877 | 1 4878 | -1 4879 | 0 4880 | 0 4881 | -1 4882 | -1 4883 | 0 4884 | 0 4885 | -1 4886 | -1 4887 | 1 4888 | -1 4889 | 1 4890 | 0 4891 | 0 4892 | 0 4893 | 1 4894 | 0 4895 | -1 4896 | 0 4897 | 0 4898 | 0 4899 | 1 4900 | 0 4901 | -1 4902 | 0 4903 | 0 4904 | 1 4905 | -1 4906 | 0 4907 | -1 4908 | 0 4909 | -1 4910 | 0 4911 | 0 4912 | 0 4913 | 1 4914 | 1 4915 | -1 4916 | -1 4917 | 0 4918 | -1 4919 | 0 4920 | -1 4921 | 0 4922 | -1 4923 | -1 4924 | -1 4925 | 1 4926 | 0 4927 | -1 4928 | -1 4929 | 0 4930 | 1 4931 | -1 4932 | 1 4933 | 0 4934 | 0 4935 | -1 4936 | -1 4937 | 0 4938 | 0 4939 | 0 4940 | -1 4941 | 1 4942 | 1 4943 | 0 4944 | 0 4945 | 1 4946 | 0 4947 | -1 4948 | 0 4949 | -1 4950 | -1 4951 | 1 4952 | -1 4953 | 1 4954 | 0 4955 | 0 4956 | 0 4957 | 1 4958 | 0 4959 | 1 4960 | -1 4961 | 0 4962 | 1 4963 | 0 4964 | 0 4965 | 0 4966 | -1 4967 | 0 4968 | -1 4969 | 1 4970 | 0 4971 | -1 4972 | 0 4973 | -1 4974 | -1 4975 | -1 4976 | -1 4977 | 1 4978 | 0 4979 | 0 4980 | 1 4981 | -1 4982 | -1 4983 | 0 4984 | 0 4985 | 1 4986 | 0 4987 | 0 4988 | 1 4989 | 0 4990 | 0 4991 | -1 4992 | 0 4993 | 0 4994 | 1 4995 | 1 4996 | -1 4997 | -1 4998 | 1 4999 | 0 5000 | 1 5001 | -1 5002 | 0 5003 | 1 5004 | 0 5005 | 0 5006 | 0 5007 | 1 5008 | 1 5009 | 0 5010 | 0 5011 | 0 5012 | 1 5013 | -1 5014 | 1 5015 | 0 5016 | 0 5017 | -1 5018 | 0 5019 | 0 5020 | 1 5021 | 0 5022 | 1 5023 | 1 5024 | 1 5025 | 1 5026 | -1 5027 | 0 5028 | 0 5029 | 0 5030 | 0 5031 | -1 5032 | -1 5033 | 1 5034 | 0 5035 | -1 5036 | 0 5037 | -1 5038 | 1 5039 | 0 5040 | -1 5041 | -1 5042 | 1 5043 | 0 5044 | -1 5045 | 1 5046 | 0 5047 | 1 5048 | 0 5049 | 0 5050 | 1 5051 | -1 5052 | 0 5053 | 0 5054 | -1 5055 | 1 5056 | -1 5057 | -1 5058 | 0 5059 | 0 5060 | 1 5061 | 1 5062 | -1 5063 | 0 5064 | 0 5065 | 1 5066 | 1 5067 | 0 5068 | 0 5069 | 0 5070 | 1 5071 | 1 5072 | 0 5073 | 0 5074 | 0 5075 | 0 5076 | 1 5077 | -1 5078 | -1 5079 | 0 5080 | 1 5081 | 1 5082 | 0 5083 | 0 5084 | 1 5085 | 1 5086 | 0 5087 | -1 5088 | 0 5089 | -1 5090 | 0 5091 | 1 5092 | -1 5093 | -1 5094 | -1 5095 | 0 5096 | -1 5097 | 1 5098 | 0 5099 | -1 5100 | 0 5101 | 0 5102 | 1 5103 | -1 5104 | -1 5105 | -1 5106 | -1 5107 | 1 5108 | 1 5109 | -1 5110 | -1 5111 | 0 5112 | -1 5113 | 1 5114 | 0 5115 | 1 5116 | -1 5117 | -1 5118 | 0 5119 | 0 5120 | 1 5121 | -1 5122 | -1 5123 | -1 5124 | 0 5125 | -1 5126 | 0 5127 | 0 5128 | -1 5129 | 1 5130 | 0 5131 | 0 5132 | 0 5133 | 1 5134 | 1 5135 | -1 5136 | -1 5137 | 1 5138 | -1 5139 | -1 5140 | -1 5141 | 1 5142 | -1 5143 | 0 5144 | -1 5145 | 0 5146 | -1 5147 | 1 5148 | -1 5149 | 0 5150 | 0 5151 | 1 5152 | -1 5153 | 1 5154 | 1 5155 | -1 5156 | -1 5157 | 1 5158 | -1 5159 | 0 5160 | -1 5161 | 0 5162 | 1 5163 | 0 5164 | 1 5165 | 1 5166 | 1 5167 | 1 5168 | 0 5169 | -1 5170 | -1 5171 | 0 5172 | 1 5173 | 1 5174 | 0 5175 | 1 5176 | 0 5177 | 1 5178 | -1 5179 | -1 5180 | 0 5181 | -1 5182 | -1 5183 | 1 5184 | 0 5185 | -1 5186 | -1 5187 | 1 5188 | 0 5189 | 0 5190 | -1 5191 | 1 5192 | -1 5193 | 0 5194 | 1 5195 | 0 5196 | -1 5197 | 0 5198 | 1 5199 | 0 5200 | 0 5201 | -1 5202 | -1 5203 | 1 5204 | -1 5205 | -1 5206 | 1 5207 | 1 5208 | 0 5209 | 0 5210 | 1 5211 | -1 5212 | 0 5213 | 0 5214 | 0 5215 | -1 5216 | 0 5217 | -1 5218 | 0 5219 | 1 5220 | -1 5221 | 0 5222 | -1 5223 | 0 5224 | 1 5225 | 0 5226 | -1 5227 | -1 5228 | -1 5229 | 1 5230 | 0 5231 | 0 5232 | 1 5233 | 1 5234 | 1 5235 | 0 5236 | -1 5237 | 1 5238 | 0 5239 | -1 5240 | 1 5241 | -1 5242 | 1 5243 | 0 5244 | -1 5245 | 1 5246 | 1 5247 | 1 5248 | 1 5249 | 0 5250 | 1 5251 | 1 5252 | 0 5253 | 0 5254 | -1 5255 | 1 5256 | 1 5257 | 1 5258 | -1 5259 | -1 5260 | -1 5261 | -1 5262 | 1 5263 | 1 5264 | -1 5265 | 0 5266 | 1 5267 | -1 5268 | -1 5269 | 0 5270 | 0 5271 | -1 5272 | -1 5273 | 1 5274 | 1 5275 | -1 5276 | -1 5277 | 1 5278 | 1 5279 | 1 5280 | 1 5281 | 1 5282 | -1 5283 | 0 5284 | 1 5285 | -1 5286 | 0 5287 | 1 5288 | 1 5289 | 1 5290 | -1 5291 | -1 5292 | 1 5293 | 0 5294 | -1 5295 | -1 5296 | 1 5297 | -1 5298 | -1 5299 | -1 5300 | 1 5301 | -1 5302 | -1 5303 | -1 5304 | 1 5305 | 1 5306 | 1 5307 | -1 5308 | 0 5309 | 0 5310 | 0 5311 | 1 5312 | 0 5313 | -1 5314 | 1 5315 | 1 5316 | -1 5317 | -1 5318 | -1 5319 | -1 5320 | 0 5321 | 0 5322 | 1 5323 | 1 5324 | -1 5325 | -1 5326 | 1 5327 | 0 5328 | 1 5329 | 0 5330 | -1 5331 | 1 5332 | 0 5333 | 1 5334 | -1 5335 | -1 5336 | -1 5337 | -1 5338 | 1 5339 | 0 5340 | -1 5341 | 0 5342 | 0 5343 | 0 5344 | -1 5345 | 0 5346 | 0 5347 | -1 5348 | -1 5349 | 0 5350 | 1 5351 | 0 5352 | -1 5353 | 1 5354 | 1 5355 | 0 5356 | -1 5357 | -1 5358 | 1 5359 | -1 5360 | 1 5361 | 0 5362 | 1 5363 | 0 5364 | -1 5365 | 1 5366 | -1 5367 | 1 5368 | -1 5369 | -1 5370 | 1 5371 | 0 5372 | -1 5373 | 1 5374 | 0 5375 | -1 5376 | 1 5377 | 0 5378 | -1 5379 | 1 5380 | 1 5381 | 1 5382 | 1 5383 | -1 5384 | -1 5385 | 0 5386 | -1 5387 | 0 5388 | -1 5389 | 1 5390 | 0 5391 | 0 5392 | -1 5393 | 1 5394 | 0 5395 | 0 5396 | 1 5397 | 1 5398 | 1 5399 | -1 5400 | 1 5401 | 0 5402 | -1 5403 | 0 5404 | 0 5405 | -1 5406 | -1 5407 | 1 5408 | 0 5409 | -1 5410 | 0 5411 | 1 5412 | 1 5413 | 0 5414 | 0 5415 | 1 5416 | 1 5417 | 1 5418 | -1 5419 | 0 5420 | 0 5421 | -1 5422 | 1 5423 | -1 5424 | 0 5425 | 0 5426 | 1 5427 | 0 5428 | 0 5429 | 0 5430 | -1 5431 | 0 5432 | 0 5433 | 0 5434 | -1 5435 | 1 5436 | 1 5437 | 0 5438 | 1 5439 | 1 5440 | -1 5441 | -1 5442 | 1 5443 | 0 5444 | 0 5445 | -1 5446 | 0 5447 | -1 5448 | 0 5449 | 1 5450 | 1 5451 | -1 5452 | -1 5453 | 1 5454 | 1 5455 | -1 5456 | -1 5457 | -1 5458 | -1 5459 | 1 5460 | -1 5461 | 1 5462 | -1 5463 | -1 5464 | 0 5465 | -1 5466 | 1 5467 | -1 5468 | 0 5469 | 0 5470 | 1 5471 | 0 5472 | 1 5473 | 0 5474 | 0 5475 | 0 5476 | 0 5477 | 0 5478 | 1 5479 | -1 5480 | 0 5481 | 1 5482 | 0 5483 | 0 5484 | 0 5485 | 1 5486 | -1 5487 | 1 5488 | -1 5489 | -1 5490 | -1 5491 | -1 5492 | 1 5493 | 1 5494 | 1 5495 | 0 5496 | -1 5497 | -1 5498 | 1 5499 | 1 5500 | -1 5501 | 0 5502 | 0 5503 | 1 5504 | 1 5505 | 1 5506 | 0 5507 | 0 5508 | 1 5509 | 1 5510 | 0 5511 | 1 5512 | 1 5513 | -1 5514 | 0 5515 | -1 5516 | 1 5517 | -1 5518 | 1 5519 | 1 5520 | 0 5521 | 0 5522 | 0 5523 | 0 5524 | 1 5525 | 1 5526 | -1 5527 | 0 5528 | 0 5529 | -1 5530 | 1 5531 | 1 5532 | -1 5533 | -1 5534 | 0 5535 | 1 5536 | -1 5537 | -1 5538 | 1 5539 | 0 5540 | 0 5541 | -1 5542 | 0 5543 | 0 5544 | 0 5545 | 1 5546 | -1 5547 | 0 5548 | 1 5549 | 1 5550 | -1 5551 | 1 5552 | -1 5553 | 0 5554 | 1 5555 | 1 5556 | 1 5557 | 1 5558 | 0 5559 | 0 5560 | -1 5561 | -1 5562 | -1 5563 | 0 5564 | -1 5565 | -1 5566 | 0 5567 | -1 5568 | 0 5569 | 1 5570 | 0 5571 | -1 5572 | 1 5573 | 1 5574 | 0 5575 | -1 5576 | 1 5577 | 0 5578 | 1 5579 | 0 5580 | 0 5581 | 0 5582 | -1 5583 | 0 5584 | 1 5585 | 0 5586 | -1 5587 | -1 5588 | 0 5589 | -1 5590 | 1 5591 | -1 5592 | -1 5593 | 1 5594 | 1 5595 | 1 5596 | -1 5597 | 1 5598 | -1 5599 | 0 5600 | 0 5601 | 1 5602 | 0 5603 | 0 5604 | 0 5605 | -1 5606 | 1 5607 | 1 5608 | -1 5609 | -1 5610 | -1 5611 | -1 5612 | -1 5613 | -1 5614 | 0 5615 | 0 5616 | 0 5617 | -1 5618 | -1 5619 | 1 5620 | -1 5621 | 1 5622 | -1 5623 | 0 5624 | -1 5625 | -1 5626 | -1 5627 | -1 5628 | 1 5629 | -1 5630 | 0 5631 | 0 5632 | 1 5633 | -1 5634 | -1 5635 | 1 5636 | 1 5637 | -1 5638 | 0 5639 | 1 5640 | 1 5641 | 1 5642 | 0 5643 | -1 5644 | -1 5645 | 1 5646 | 1 5647 | 0 5648 | 0 5649 | -1 5650 | 0 5651 | 0 5652 | -1 5653 | 1 5654 | -1 5655 | 0 5656 | -1 5657 | -1 5658 | -1 5659 | 0 5660 | 0 5661 | -1 5662 | -1 5663 | 0 5664 | 1 5665 | 0 5666 | -1 5667 | -1 5668 | 0 5669 | 0 5670 | -1 5671 | -1 5672 | -1 5673 | 0 5674 | -1 5675 | 0 5676 | 1 5677 | -1 5678 | -1 5679 | -1 5680 | 0 5681 | -1 5682 | 0 5683 | 0 5684 | 1 5685 | -1 5686 | -1 5687 | 1 5688 | 0 5689 | -1 5690 | 1 5691 | -1 5692 | 0 5693 | -1 5694 | 1 5695 | 0 5696 | -1 5697 | 1 5698 | -1 5699 | 0 5700 | -1 5701 | 1 5702 | 1 5703 | 1 5704 | 0 5705 | 1 5706 | 1 5707 | -1 5708 | -1 5709 | 0 5710 | 0 5711 | -1 5712 | -1 5713 | 0 5714 | 1 5715 | -1 5716 | 0 5717 | 1 5718 | -1 5719 | -1 5720 | 1 5721 | 1 5722 | 1 5723 | 1 5724 | 1 5725 | 1 5726 | 0 5727 | 0 5728 | 0 5729 | 0 5730 | 0 5731 | 1 5732 | 0 5733 | -1 5734 | 1 5735 | 0 5736 | -1 5737 | 1 5738 | 0 5739 | -1 5740 | 0 5741 | 1 5742 | -1 5743 | -1 5744 | -1 5745 | 0 5746 | -1 5747 | -1 5748 | -1 5749 | -1 5750 | 0 5751 | 0 5752 | -1 5753 | 0 5754 | -1 5755 | 1 5756 | 1 5757 | 1 5758 | 0 5759 | -1 5760 | 0 5761 | 0 5762 | -1 5763 | 1 5764 | 0 5765 | -1 5766 | -1 5767 | 1 5768 | 0 5769 | -1 5770 | 0 5771 | 1 5772 | 1 5773 | -1 5774 | -1 5775 | 0 5776 | 1 5777 | 0 5778 | 0 5779 | 0 5780 | 1 5781 | 0 5782 | -1 5783 | 1 5784 | -1 5785 | 1 5786 | 1 5787 | 1 5788 | 0 5789 | 1 5790 | 1 5791 | 0 5792 | -1 5793 | 1 5794 | 0 5795 | -1 5796 | 0 5797 | 0 5798 | 0 5799 | -1 5800 | 0 5801 | -1 5802 | 1 5803 | 0 5804 | 1 5805 | -1 5806 | 1 5807 | 1 5808 | -1 5809 | 0 5810 | 1 5811 | 0 5812 | 1 5813 | -1 5814 | 0 5815 | 1 5816 | 0 5817 | 0 5818 | 1 5819 | -1 5820 | 1 5821 | -1 5822 | 1 5823 | 0 5824 | -1 5825 | -1 5826 | -1 5827 | 1 5828 | 0 5829 | 1 5830 | 0 5831 | 0 5832 | 0 5833 | -1 5834 | 1 5835 | -1 5836 | 1 5837 | -1 5838 | 0 5839 | 1 5840 | 0 5841 | 1 5842 | 1 5843 | -1 5844 | -1 5845 | 1 5846 | 0 5847 | 1 5848 | 1 5849 | 0 5850 | -1 5851 | -1 5852 | 1 5853 | 1 5854 | 0 5855 | 1 5856 | 1 5857 | 1 5858 | -1 5859 | -1 5860 | 1 5861 | 0 5862 | 0 5863 | -1 5864 | -1 5865 | 1 5866 | 1 5867 | 1 5868 | -1 5869 | 1 5870 | 0 5871 | 1 5872 | 0 5873 | 1 5874 | 0 5875 | 1 5876 | 0 5877 | -1 5878 | 0 5879 | 1 5880 | -1 5881 | 0 5882 | 1 5883 | 0 5884 | 0 5885 | -1 5886 | 0 5887 | -1 5888 | 0 5889 | 1 5890 | 1 5891 | 0 5892 | -1 5893 | -1 5894 | -1 5895 | 1 5896 | -1 5897 | -1 5898 | 1 5899 | 0 5900 | -1 5901 | -1 5902 | -1 5903 | 0 5904 | -1 5905 | 0 5906 | -1 5907 | -1 5908 | 1 5909 | -1 5910 | 1 5911 | -1 5912 | 0 5913 | 1 5914 | 0 5915 | 1 5916 | 0 5917 | 1 5918 | 0 5919 | 1 5920 | 1 5921 | 1 5922 | 1 5923 | -1 5924 | 1 5925 | -1 5926 | -1 5927 | 1 5928 | 1 5929 | 0 5930 | 1 5931 | 1 5932 | 0 5933 | 0 5934 | 0 5935 | -1 5936 | -1 5937 | 1 5938 | -1 5939 | 1 5940 | -1 5941 | 0 5942 | -1 5943 | 0 5944 | 0 5945 | 1 5946 | -1 5947 | -1 5948 | -1 5949 | -1 5950 | 0 5951 | -1 5952 | 0 5953 | -1 5954 | -1 5955 | -1 5956 | 1 5957 | 1 5958 | -1 5959 | 0 5960 | 0 5961 | 1 5962 | -1 5963 | -1 5964 | 0 5965 | 1 5966 | 1 5967 | 0 5968 | 0 5969 | -1 5970 | 0 5971 | -1 5972 | 1 5973 | -1 5974 | -1 5975 | 1 5976 | 0 5977 | 0 5978 | 0 5979 | 0 5980 | -1 5981 | 1 5982 | -1 5983 | 1 5984 | 0 5985 | 0 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