├── code ├── README.md ├── model │ └── 倒排索引 │ │ └── data │ │ ├── b.txt │ │ ├── c.txt │ │ └── a.txt ├── my_tensorflow │ ├── src │ │ ├── __init__.py │ │ ├── layers │ │ │ ├── match │ │ │ │ └── __init__.py │ │ │ ├── __init__.py │ │ │ ├── cnn.py │ │ │ └── dense.py │ │ ├── regularizers │ │ │ ├── __init__.py │ │ │ └── L1L2.py │ │ ├── initializers │ │ │ └── __init__.py │ │ ├── utils │ │ │ ├── math_op.py │ │ │ ├── array_op.py │ │ │ └── __init__.py │ │ └── activations │ │ │ ├── relu.py │ │ │ └── __init__.py │ ├── README.md │ └── demo.py └── cpp │ └── 面向对象-不通过继承实现多态-动态序列.cpp ├── 算法 ├── 专题-经典算法问题.md ├── 专题-位运算.md ├── 专题-海量数据处理.md ├── 专题-局部敏感哈希.md ├── README.md ├── 专题-大数运算.md ├── 备忘-必备算法.md └── 题解-LeetCode.md ├── assets ├── 门卷积.jpg ├── 门卷积.png ├── 共现矩阵.png ├── dilation.gif ├── 数据清洗与特征处理.jpg ├── 普通卷积与膨胀卷积.png ├── conv_dilation.gif ├── confusion_matrix.png ├── padding_strides.gif ├── 公式_2018071891909.png ├── 公式_2018071893503.png ├── 公式_2018071895945.png ├── 公式_2018080694809.png ├── 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TIM截图20180819132752.png ├── no_padding_no_strides.gif ├── padding_strides_odd.gif ├── full_padding_no_strides.gif ├── same_padding_no_strides.gif ├── padding_strides_transposed.gif ├── arbitrary_padding_no_strides.gif ├── contours_evaluation_optimizers.gif ├── no_padding_strides_transposed.gif ├── padding_strides_odd_transposed.gif ├── no_padding_no_strides_transposed.gif ├── full_padding_no_strides_transposed.gif ├── saddle_point_evaluation_optimizers.gif ├── same_padding_no_strides_transposed.gif ├── conv_no_padding_no_strides_transposed.gif └── arbitrary_padding_no_strides_transposed.gif ├── papers ├── [1611]-BiDAF.pdf ├── [1505]-Highway.pdf ├── [1509]-CharCNN.pdf └── 语义匹配 │ ├── [1709]-DIIN.pdf │ ├── [1702]-BiMPM.pdf │ └── DSSM │ ├── [2013]-DSSM.pdf │ ├── [2014]-CDSSM.pdf │ ├── [2015]-LSTM_DSSM.pdf │ └── [2015]-MV_DSSM.pdf ├── 机器学习-深度学习-NLP ├── DL-专题-损失函数.md ├── DL-TODO-RNN.md ├── DL-深度学习实践.md ├── ML-专题-XGBoost.md ├── README.md ├── 专题-Bash命令.md ├── NLP-TODO-中文分词.md ├── 专题-工具库.md ├── NLP-自然语言处理实践.md └── NLP-TODO-问答模型.md ├── 编程语言 ├── README.md └── Cpp-面向对象编程.md ├── .gitignore ├── ToDo.md ├── notes ├── 备忘-Markdown小技巧.md ├── 备忘-Python相关工具.md └── 备忘-常用子函数.md ├── 数学 └── README.md └── 笔试面经 └── 笔试-网易.md /code/README.md: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /算法/专题-经典算法问题.md: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /code/model/倒排索引/data/b.txt: -------------------------------------------------------------------------------- 1 | html a b c -------------------------------------------------------------------------------- /code/model/倒排索引/data/c.txt: -------------------------------------------------------------------------------- 1 | abc h html -------------------------------------------------------------------------------- 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my_tensorflow 2 | === 3 | 4 | Requirements 5 | --- 6 | - Tensorflow 1.0+ (1.8) 7 | 8 | Reference 9 | --- 10 | - Keras 11 | - TensorLayer -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/DL-专题-损失函数.md: -------------------------------------------------------------------------------- 1 | 专题-损失函数 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [交叉熵](#交叉熵) 9 | - [计算示例](#计算示例) 10 | 11 | 12 | 13 | ## 交叉熵 14 | 15 | 16 | ### 计算示例 -------------------------------------------------------------------------------- /code/my_tensorflow/src/layers/__init__.py: -------------------------------------------------------------------------------- 1 | """常见层与常用层的写法 2 | """ 3 | 4 | from .dense import * 5 | from .cnn import * 6 | from .highway import * 7 | from .attention import * 8 | from .match import * 9 | 10 | -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/DL-TODO-RNN.md: -------------------------------------------------------------------------------- 1 | DL-RNN 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [RNN 的几种设计模型（3）](#rnn-的几种设计模型3) 9 | 10 | 11 | 12 | ## RNN 的几种设计模型（3） 13 | > DL-《深度学习整理》#[RNN 的几种基本设计模式](#rnn-的几种基本设计模式) -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/DL-深度学习实践.md: -------------------------------------------------------------------------------- 1 | DL-深度学习实践 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [Batch Normalization 批正则化 TODO](#batch-normalization-批正则化-todo) 9 | 10 | 11 | 12 | ## Batch Normalization 批正则化 TODO -------------------------------------------------------------------------------- /算法/专题-海量数据处理.md: -------------------------------------------------------------------------------- 1 | 专题-海量数据处理 2 | === 3 | 4 | Reference 5 | --- 6 | - [海量数据处理面试题集锦](https://blog.csdn.net/v_july_v/article/details/6685962) - CSDN博客 7 | - [十道海量数据处理面试题与十个方法大总结](https://blog.csdn.net/v_JULY_v/article/details/6279498) - CSDN博客 -------------------------------------------------------------------------------- /code/my_tensorflow/src/regularizers/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | 正则化函数 3 | `Tensor -> Tensor or None` 4 | 5 | Examples: 6 | l2_regularizer = l2(0.01) 7 | tf.get_variable(..., regularizer=l2_regularizer, ...) 8 | """ 9 | from .L1L2 import * 10 | -------------------------------------------------------------------------------- /编程语言/README.md: -------------------------------------------------------------------------------- 1 | 编程语言相关知识点总结 2 | === 3 | - 以 C++ 和 Python 为主 4 | 5 | **Index** 6 | --- 7 | - [C/C++ 语言知识点](./CCpp编程语言.md) 8 | - [专题-左值与右值](./Cpp专题-左值与右值) 9 | 10 | ## Reference 11 | - - huihut/[interview: C/C++面试知识总结](https://github.com/huihut/interview) 12 | 13 | ## ToDo 14 | - Cpp专题-STL 15 | - Cpp专题-面向对象 -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/ML-专题-XGBoost.md: -------------------------------------------------------------------------------- 1 | ML-专题-XGBoost 2 | === 3 | 4 | 预备知识 5 | - [集成学习](./ML-机器学习算法.md#集成学习) 6 | - [决策树](./ML-机器学习算法.md#决策树) 7 | 8 | Index 9 | --- 10 | 11 | 12 | - [AdaBoost](#adaboost) 13 | - [GDBT](#gdbt) 14 | - [XGBoost](#xgboost) 15 | 16 | 17 | 18 | ## AdaBoost 19 | 20 | 21 | ## GDBT 22 | 23 | 24 | ## XGBoost 25 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # IDE 2 | .idea/ 3 | .vscode/ 4 | 5 | # folder 6 | __*/ 7 | .ipynb_checkpoints/ 8 | out/ 9 | 10 | # file 11 | *.zip 12 | *.tgz 13 | _test*.py 14 | _ToDo.md 15 | Untitled.ipynb 16 | MyCV.pdf 17 | GSD_*.pdf 18 | 19 | # root 20 | /demo.py 21 | /test.py 22 | 23 | # prevent being filtered 24 | !__init__.py 25 | 26 | # Python 27 | .py[cod] 28 | *.egg 29 | *.egg-info 30 | /dist/ 31 | __pycache__/ 32 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/initializers/__init__.py: -------------------------------------------------------------------------------- 1 | """变量初始化器""" 2 | 3 | import tensorflow as tf 4 | 5 | 6 | '''常用的 Tensorflow 基本初始化器''' 7 | # 零 8 | zeros = tf.initializers.zeros 9 | # 常量 10 | constant = tf.initializers.constant 11 | # 标准正太分布 12 | normal = tf.initializers.random_normal 13 | # 截断正态分布 14 | truncated_normal = tf.initializers.truncated_normal 15 | # 均匀分布 16 | uniform = tf.initializers.random_uniform 17 | # 18 | -------------------------------------------------------------------------------- /ToDo.md: -------------------------------------------------------------------------------- 1 | Updates Log 2 | === 3 | 4 | ## ToDo 5 | - Tensorflow - char embedding layer 6 | - Tensorflow - Pointer Layer 7 | - 算法 - 专题-海量数据处理 8 | - 机器学习 - 专题-XGBoost 9 | - 深度学习 - GAN 与 RL 基本模型 10 | - CRF 条件随机场 / 图模型 11 | - Linux Shell 常用命令 12 | - wc/sed/awk/grep/sort/uniq/paste/cat/head/tail 13 | - DL - 专题 - 加速方法 14 | 15 | ## 2018-8-18 16 | - DL-专题-优化算法 17 | - 梯度下降法 18 | - SGD、小批量 SGD 19 | - 牛顿法与梯度下降 20 | 21 | 22 | ## 2018-8-17 23 | - 机器学习基础 - 方差与偏差 24 | - 机器学习基础 - 判别模型与生成模型 25 | 26 | ## 2018-8-16 27 | - 算法 - 洗牌与采样 -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/README.md: -------------------------------------------------------------------------------- 1 | 机器学习 & 深度学习 2 | === 3 | 4 | ## 机器学习 5 | - [机器学习基础](./ML-机器学习基础.md) 6 | - [机器学习算法](./ML-机器学习算法.md) 7 | - [机器学习实践](./ML-机器学习实践.md) 8 | 9 | ## 深度学习 10 | - [深度学习基础](./DL-深度学习基础.md) 11 | - 深度学习实践 TODO 12 | - [专题-CNN](./DL-专题-CNN.md) 13 | - [专题-优化算法](./DL-专题-优化算法.md) 14 | - SGD 15 | - 带动量的 SGD 16 | - 自适应学习率的 SGD（Adam 等） 17 | - 专题-损失函数 TODO 18 | 19 | ## 自然语言处理 20 | - [NLP 基础](NLP-自然语言处理基础.md) 21 | - NLP 实践 TODO 22 | - [专题-词向量](./NLP-专题-词向量.md) 23 | - [专题-序列建模](./NLP-专题-序列建模.md) 24 | 25 | 26 | ## 公共基础 TODO -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/专题-Bash命令.md: -------------------------------------------------------------------------------- 1 | 专题-Bash命令 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [Bash 必背命令](#bash-必背命令) 9 | - [wc/sed/awk/grep/sort/uniq/paste/cat/head/tail](#wcsedawkgrepsortuniqpastecatheadtail) 10 | - [---](#---) 11 | - [wc](#wc) 12 | - [使用场景](#使用场景) 13 | - [统计文件中出现次数最多的前 10 个词](#统计文件中出现次数最多的前-10-个词) 14 | 15 | 16 | 17 | # Bash 必背命令 18 | 19 | ## wc/sed/awk/grep/sort/uniq/paste/cat/head/tail 20 | 21 | ## --- 22 | 23 | ## wc 24 | 25 | # 使用场景 26 | ## 统计文件中出现次数最多的前 10 个词 27 | ```bash 28 | 29 | ``` -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/NLP-TODO-中文分词.md: -------------------------------------------------------------------------------- 1 | **RoadMap** 2 | --- 3 | 4 | 5 | # 中文分词 6 | 7 | ## Reference 8 | > [漫话中文自动分词和语义识别（上）：中文分词算法](http://www.matrix67.com/blog/archives/4212) | Matrix67 9 | - 中文分词的**难点** 10 | - **交集型歧义** 11 | - **组合型歧义** 12 | - **未登录词** 13 | - 人名 14 | - 地名 15 | - 机构名 16 | - 缩略语 17 | - 网络用语 18 | - 中文分词的历史 19 | - 规则表/词典——**前缀树** 20 | - 最长匹配 21 | - 最少词数 22 | - 惩罚方法 23 | - 基于有向无环图的最大概率组合 24 | - 基于语言模型的最大概率组合 25 | - 字标注 26 | - HMM 27 | - CRF 28 | > jieba 分词就同时结合了两者：有向图的最大概率 + HMM 模型 29 | 30 | ## 基于 AC 自动机的中文分词 31 | > [【中文分词系列】 1. 基于AC自动机的快速分词](https://kexue.fm/archives/3908) - 科学空间|Scientific Spaces 32 | -------------------------------------------------------------------------------- /算法/专题-局部敏感哈希.md: -------------------------------------------------------------------------------- 1 | 局部敏感哈希 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [什么是局部敏感哈希](#什么是局部敏感哈希) 9 | - [LSH 的特点](#lsh-的特点) 10 | - [LSH 的应用场景](#lsh-的应用场景) 11 | 12 | 13 | 14 | 15 | ## 什么是局部敏感哈希 16 | > [Locality-sensitive hashing](https://en.wikipedia.org/wiki/Locality-sensitive_hashing) - Wikipedia 17 | - **局部敏感哈希**（Locality-Sensitive Hashing, LSH）指的是一类特殊的 Hash 函数——它们能使相似的高维数据在经过**降维**后依然保持一定的相似性。 18 | - LSH 本质上是一种利用 Hash 进行快速**相似度计算**的算法。 19 | - 具体来说，相似的高维数据经过 LSH 处理后，会以较高的**概率**进入相同的“**桶(bucket)**”中，从而达到分类相似数据的目的。 20 | 21 | ### LSH 的特点 22 | - **速度快**——利用 Hash 特点，LSH 特别用于处理**海量数据**的相似性计算； 23 | 24 | 25 | ### LSH 的应用场景 -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/专题-工具库.md: -------------------------------------------------------------------------------- 1 | 专题-工具库 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [工具列表](#工具列表) 9 | - [jieba 分词](#jieba-分词) 10 | - [分词](#分词) 11 | 12 | 13 | 14 | # 工具列表 15 | - Stanford Core NLP 16 | - 语义分析 17 | - NLTK 18 | - 分词（西文）、分句、读取语义树 19 | - 词干提取 20 | - jieba 21 | - 中文分词、词性标注 22 | 23 | # jieba 分词 24 | > fxsjy/[jieba: 结巴中文分词](https://github.com/fxsjy/jieba) 25 | 26 | ## 分词 27 | **代码示例** 28 | ```Python 29 | import jieba 30 | 31 | # 全模式 32 | seg_list = jieba.cut("我来到北京清华大学", cut_all=True) 33 | print("【全模式】: " + "/ ".join(seg_list)) 34 | 35 | # 精确模式（默认） 36 | seg_list = jieba.cut("我来到北京清华大学", cut_all=False) 37 | # seg_list = jieba.cut("他来到了网易杭研大厦") 38 | print("【精确模式】: " + "/ ".join(seg_list)) 39 | print(", ".join(seg_list)) 40 | 41 | # 新词识别 42 | 43 | 44 | # 搜索引擎模式 45 | seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造") 46 | print(", ".join(seg_list)) 47 | ``` -------------------------------------------------------------------------------- /code/my_tensorflow/src/utils/math_op.py: -------------------------------------------------------------------------------- 1 | """Math 2 | 3 | References: 4 | https://www.tensorflow.org/api_guides/python/math_ops 5 | """ 6 | 7 | import tensorflow as tf 8 | 9 | 10 | def dot(x, y): 11 | """Multiplies 2 tensors (and/or variables) and returns a *tensor*. 12 | """ 13 | x_shape = list(x.get_shape()) 14 | y_shape = list(y.get_shape()) 15 | x_ndim = len(x_shape) 16 | y_ndim = len(y_shape) 17 | 18 | if x_ndim is not None and (x_ndim > 2 or y_ndim > 2): 19 | y_permute_dim = list(range(y_ndim)) 20 | y_permute_dim = [y_permute_dim.pop(-2)] + y_permute_dim 21 | xt = tf.reshape(x, [-1, x_shape[-1]]) 22 | yt = tf.reshape(tf.transpose(y, perm=y_permute_dim), [y_shape[-2], -1]) 23 | return tf.reshape(tf.matmul(xt, yt), 24 | x_shape[:-1] + y_shape[:-2] + y_shape[-1:]) 25 | else: 26 | return tf.matmul(x, y) 27 | -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/NLP-自然语言处理实践.md: -------------------------------------------------------------------------------- 1 | NLP-自然语言处理实践 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | ## NLP 怎么做数据增强 9 | - 数据增强，具体来说就是对原数据集进行一定程度的改动，从而得到 10 | 11 | ### 双向翻译 12 | - 利用 NMT 做双向翻译——将语言A 翻译到其他语言，再翻译回语言 A
13 | 这个过程相当于对样本进行了改写，使得训练样本的数量大大增加 14 | - QANet 中的做法： 15 |
16 | 17 | - 对材料中每个句子通过翻译引擎得到`k`句法语候选，然后将每句法语转回英语，得到`k^2`个改写的句子，从中随机选择一句作为 18 | - 改写后答案的位置也可能改变，如何寻找**新答案的位置**？
19 | 具体到 SQuAD 任务就是 `(d,q,a) -> (d’, q, a’)`，问题不变，对文档 `d` 翻译改写，由于改写后原始答案 `a` 现在可能已经不在改写后的段落 `d’` 里了，所以需要从改写后的段落 `d’` 里抽取新的答案 `a’`，采用的方法是计算 `d’` 里每个单词和原始答案里 start/end words 之间的 **character-level 2-gram score**，分数最高的单词就被选择为新答案 `a’` 的 start/end word。 20 | > 中文没有里面没有 character-level 2-gram，可以考虑词向量之间的相似度 21 | 22 | ### 同义词替换 TODO 23 | - 图像中对图片进行缩放、平移、旋转等操作不会改变图片本身含义；语音中对语音的声调、语速、噪声稍加改动也不会改变其结果 24 | - 文本中能做的类似改动比较少，同义词替换算一种 25 | - 哪些词应当被替换 26 | - 应该是用哪个同义词来替换 -------------------------------------------------------------------------------- /notes/备忘-Markdown小技巧.md: -------------------------------------------------------------------------------- 1 | 备忘-Markdown小技巧 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [自动更新目录](#自动更新目录) 9 | - [图片居中](#图片居中) 10 | - [隐藏代码块](#隐藏代码块) 11 | - [Latex 公式](#latex-公式) 12 | 13 | 14 | 15 | ## 自动更新目录 16 | - VSCode 插件 [`Markdown TOC`](https://marketplace.visualstudio.com/items?itemName=AlanWalk.markdown-toc) 17 | 18 | ## 图片居中 19 | - 不带链接 20 | ``` 21 |
22 | ``` 23 | - 带链接 24 | ``` 25 |
26 | ``` 27 | - `height=""`用于控制图片的大小 28 | 29 | ## 隐藏代码块 30 | ``` 31 |
示例：动态序列（点击展开） 32 | 33 | // 代码块，注意上下都要保留空行 34 | 35 |
36 | ``` 37 | 38 | ## Latex 公式 39 | > 在线 LaTeX 公式编辑器 http://www.codecogs.com/latex/eqneditor.php 40 | - 斜体加粗 41 | ``` 42 | \boldsymbol{x} 43 | ``` 44 | - 期望 45 | ``` 46 | \mathbb{E} 47 | ``` -------------------------------------------------------------------------------- /数学/README.md: -------------------------------------------------------------------------------- 1 | **知识回顾** 2 | --- 3 | - [微积分的本质](./微积分的本质.md) 4 | - [深度学习的核心-梯度下降与反向传播](./深度学习的核心.md) 5 | 6 | **数学问题** 7 | --- 8 | 9 | 10 | - [1. 在圆环上随机选取 3 个点，这 3 个点组成锐角三角形的概率](#1-在圆环上随机选取-3-个点这-3-个点组成锐角三角形的概率) 11 | 12 | 13 | 14 | 15 | # 1. 在圆环上随机选取 3 个点，这 3 个点组成锐角三角形的概率 16 | > 今日头条-算法工程师-实习 17 | 18 | - 答案：1/4 19 | 20 | - 简单回答：易知，当A、B、C三点都在同一个半圆内时，三角形ABC必是直角或钝角三角形；只有当三点不在同一个半圆内，才可以组成锐角三角形。于是问题等价于“在圆周上任取三个不同的点，求它们不在同一半圆内的概率”，故其概率为 `1/2 * 1/2 = 1/4`（先固定点 A，B 和 C 分别与之在同一个半圆内的概率为 1/2） 21 | > [在一圆周上任意取三个点构成锐角三角形的概率是多少?](https://www.zybang.com/question/7ad30f46d6eebb125b6421479d24bdc3.html) - 作业帮 22 | 23 | - 使用积分计算该概率 24 | 25 | [![](../assets/公式_20180624211704.png)](http://www.codecogs.com/eqnedit.php?latex=\int_{0}^{\pi}\frac{\theta&space;}{2\pi}\cdot&space;\frac{1}{\pi}=\frac{\theta^2}{4\pi^2}\Big|^\pi_0=\frac{1}{4}) 26 | > [在一圆周上任意取三个点构成锐角三角形的概率是多少](https://zhidao.baidu.com/question/1884315387170029428.html) - 百度知道 27 | 28 | > [【官方双语】如何优雅地解答最难数学竞赛的压轴题？ 1:50-4:25, 5:40-7:50](https://www.bilibili.com/video/av17275211) - bilibili 29 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/utils/array_op.py: -------------------------------------------------------------------------------- 1 | """Tensor Transformations 2 | 3 | References: 4 | https://www.tensorflow.org/api_guides/python/array_ops 5 | keras.backend 6 | """ 7 | 8 | import tensorflow as tf 9 | import keras.backend as K 10 | 11 | 12 | def permute(x, perm): 13 | """ 14 | Examples: 15 | x.shape == [128, 32, 1] 16 | x = permute(x, [0, 2, 1]) 17 | x.shape == [128, 1, 32] 18 | 19 | y.shape == [128, 64, 32] 20 | y = permute(x, [2, 1, 0]) 21 | y.shape == [32, 64, 128] 22 | 23 | References: 24 | K.permute_dimensions() 25 | """ 26 | return tf.transpose(x, perm) 27 | 28 | 29 | def repeat(x, n): 30 | """ 31 | Examples: 32 | x.shape == [batch_size, n_input] 33 | x = repeat(x, n_step) 34 | x.shape == [batch_size, n_step, n_input] 35 | 36 | References: 37 | K.repeat() 38 | tf.tile() 39 | """ 40 | assert x.get_shape().ndims == 2 41 | x = tf.expand_dims(x, axis=1) # -> [batch_size, 1, n_input] 42 | return tf.tile(x, [1, n, 1]) # -> [batch_size, n, n_input] 43 | 44 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/regularizers/L1L2.py: -------------------------------------------------------------------------------- 1 | """ 2 | L1 和 L2 正则化 3 | 4 | References: 5 | keras.regularizers 6 | """ 7 | import tensorflow as tf 8 | import numpy as np 9 | 10 | 11 | class L1L2Regularizer(object): 12 | """L1 L2 正则化 13 | 14 | Examples: 15 | l2_regularizer = l2(0.01) 16 | tf.get_variable(..., regularizer=l2_regularizer, ...) 17 | """ 18 | 19 | def __init__(self, l1=0., l2=0.): 20 | """ 21 | Args: 22 | l1(float): L1 正则化的系数 23 | l2(float): L2 正则化的系数 24 | """ 25 | self.l1 = np.asarray(l1, dtype=np.float32) 26 | self.l2 = np.asarray(l2, dtype=np.float32) 27 | 28 | def __call__(self, x): 29 | """ 30 | Args: 31 | x: 注意 x.dtype == float32 32 | """ 33 | # x = tf.cast(x, dtype=tf.float32) # 交给外部处理 34 | loss_regularization = 0. 35 | if self.l1: 36 | loss_regularization += tf.reduce_sum(self.l1 * tf.abs(x)) 37 | if self.l2: 38 | loss_regularization += tf.reduce_sum(self.l2 * tf.square(x)) 39 | return loss_regularization 40 | 41 | 42 | """预定义好的正则化器 43 | """ 44 | l1_regularizer = L1L2Regularizer(l1=0.01) 45 | 46 | l2_regularizer = L1L2Regularizer(l2=0.01) 47 | 48 | l1_l2_regularizer = L1L2Regularizer(l1=0.01, l2=0.01) 49 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/layers/cnn.py: -------------------------------------------------------------------------------- 1 | """卷积层 2 | 3 | References: 4 | tensorlayer.layers.Conv2dLayer 5 | """ 6 | import tensorflow as tf 7 | 8 | from ..activations import relu 9 | from ..utils import get_wb 10 | 11 | 12 | def conv2d(x, kernel_size, out_channels, 13 | act_fn=relu, 14 | strides=1, 15 | padding="SAME", 16 | name=None, 17 | reuse=None): 18 | """2-D 卷积层 19 | Input shape: [batch_size, in_w, in_h, in_channels] 20 | Output shape: [batch_size, out_w, out_h, out_channels] 21 | 22 | Args: 23 | x(tf.Tensor): 24 | kernel_size(int or list of int): 25 | out_channels(int): 26 | act_fn(function): 27 | strides(int or list of int): 28 | padding(str): 29 | name(str): 30 | reuse(bool): 31 | 32 | Returns: 33 | 34 | """ 35 | if isinstance(kernel_size, int): 36 | kernel_size = [kernel_size] * 2 37 | if isinstance(strides, int): 38 | strides = [strides] * 4 39 | 40 | assert len(kernel_size) == 2 41 | assert len(strides) == 4 42 | 43 | in_channels = int(x.get_shape()[-1]) 44 | kernel_shape = list(kernel_size) + [in_channels, out_channels] 45 | 46 | with tf.variable_scope(name or "conv2d", reuse=reuse): 47 | W, b = get_wb(kernel_shape) 48 | 49 | o = tf.nn.conv2d(x, W, strides=strides, padding=padding) + b 50 | o = act_fn(o) 51 | 52 | return o 53 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/activations/relu.py: -------------------------------------------------------------------------------- 1 | """ReLU 系列激活函数""" 2 | import tensorflow as tf 3 | 4 | from ..utils import get_w, get_shape 5 | from ..initializers import constant 6 | 7 | 8 | def relu(x): 9 | """ReLU 10 | `o = max(0., x)` 11 | """ 12 | return tf.nn.relu(x) 13 | 14 | 15 | def relu6(x): 16 | """ 17 | `o = min(max(x, 0), 6)` 18 | """ 19 | return tf.nn.relu6(x) 20 | 21 | 22 | def crelu(x, axis=-1): 23 | """Concatenated ReLU 24 | """ 25 | return tf.nn.crelu(x, axis=axis) 26 | 27 | 28 | def leaky_relu(x, alpha=0.1): 29 | """渗透 ReLU 30 | `o = max(alpha * x, x)` 31 | """ 32 | return tf.nn.leaky_relu(x, alpha) 33 | 34 | 35 | def clip_relu(x, max_value): 36 | """截断 ReLU 37 | `o = min(max(0., x), max_value)` 38 | """ 39 | o = tf.nn.relu(x) 40 | o = tf.minimum(o, max_value) 41 | return o 42 | 43 | 44 | def parametric_relu(x, channel_shared=False, alpha_init=constant(0.), name="parametric_relu", reuse=None): 45 | """参数化 ReLU 46 | 47 | References: 48 | tflearn.prelu 49 | """ 50 | if channel_shared: 51 | alpha_shape = get_shape(x)[-1:] 52 | else: 53 | alpha_shape = [1] 54 | 55 | with tf.variable_scope(name, reuse=reuse): 56 | alpha = get_w(alpha_shape, w_initializer=alpha_init, name="alpha") 57 | # o = relu(x) + 0.5 * tf.multiply(alpha, x - tf.abs(x)) # TFLearn 58 | o = leaky_relu(x, alpha) # TensorLayer / 59 | 60 | return o 61 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/activations/__init__.py: -------------------------------------------------------------------------------- 1 | """激活函数""" 2 | 3 | from .relu import * 4 | 5 | 6 | def linear(x): 7 | """""" 8 | return x 9 | 10 | 11 | def identity(x): 12 | """""" 13 | return tf.identity(x) 14 | 15 | 16 | def sigmoid(x): 17 | """""" 18 | return tf.nn.sigmoid(x) 19 | 20 | 21 | def hard_sigmoid(x): 22 | """ 23 | x = 0. x < -2.5 24 | = 1. x > 2.5 25 | = 0.2 * x + 0.5 otherwise 26 | """ 27 | x = (0.2 * x) + 0.5 28 | x = tf.clip_by_value(x, 0., 1.) 29 | return x 30 | 31 | 32 | def tanh(x): 33 | """""" 34 | return tf.nn.tanh(x) 35 | 36 | 37 | def softplus(x): 38 | """""" 39 | return tf.nn.softplus(x) 40 | 41 | 42 | def softsign(x): 43 | """ 44 | o = x / (1 + abs(x)) 45 | """ 46 | return tf.nn.softsign(x) 47 | 48 | 49 | def softmax(x, axis=-1): 50 | """ 51 | Examples: 52 | n_dim = x.get_shape().ndims 53 | assert n_dim >= 2 54 | 55 | if n_dim == 2: 56 | return tf.nn.softmax(x) 57 | else: 58 | e = tf.exp(x - tf.reduce_max(x, axis=axis, keepdims=True)) 59 | s = tf.reduce_sum(e, axis=axis, keepdims=True) 60 | return e / s 61 | """ 62 | return tf.nn.softmax(x, axis=axis) 63 | 64 | 65 | def elu(x): 66 | """指数线性单元""" 67 | return tf.nn.elu(x) 68 | 69 | 70 | def selu(x): 71 | """缩放型指数线性单元""" 72 | alpha = 1.6732632423543772848170429916717 73 | scale = 1.0507009873554804934193349852946 74 | o = tf.nn.elu(x) 75 | return scale * tf.where(x > 0, o, alpha * o) 76 | -------------------------------------------------------------------------------- /code/cpp/面向对象-不通过继承实现多态-动态序列.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | using namespace std; 3 | 4 | class num_sequence { 5 | public: 6 | // PtrType 是一个指针，指向 num_sequence 的成员函数， 7 | // 该成员函数必须只接受一个 int 型参数，以及返回类型为 void 8 | typedef void (num_sequence::*PtrType)(int); 9 | 10 | enum { cnt_seq = 2 }; // 预定义了两种序列 11 | enum ns_type { 12 | ns_fibonacci, ns_square 13 | }; 14 | 15 | // 构造函数：默认指向斐波那契数列 16 | num_sequence(): _pmf(func_tbl[ns_fibonacci]) { } 17 | 18 | // 调整指针指向 19 | void set_sequence(ns_type nst) { 20 | switch (nst) { 21 | case ns_fibonacci: case ns_square: 22 | _pmf = func_tbl[nst]; 23 | break; 24 | default: 25 | cerr << "invalid sequence type\n"; 26 | } 27 | } 28 | 29 | void print(int n) { 30 | (this->*_pmf)(n); // 通过指针选择需要调用的函数 31 | } 32 | 33 | // _pmf 可以指向以下任何一个函数 34 | void fibonacci(int n) { 35 | int f = 1; 36 | int g = 1; 37 | for (int i = 2; i <= n; i++) 38 | g = g + f, f = g - f; 39 | cout << f << endl; 40 | } 41 | 42 | void square(int n) { 43 | cout << n * n << endl; 44 | } 45 | 46 | private: 47 | PtrType _pmf; 48 | static PtrType func_tbl[cnt_seq]; // 保存所有序列函数的指针 49 | }; 50 | 51 | // static 成员变量初始化 52 | num_sequence::PtrType 53 | num_sequence::func_tbl[cnt_seq] = { 54 | &num_sequence::fibonacci, 55 | &num_sequence::square, 56 | }; 57 | 58 | int main() { 59 | 60 | auto ns = num_sequence(); 61 | ns.print(5); // 5 62 | ns.set_sequence(num_sequence::ns_square); // 调整函数指针以获得多态的效果 63 | ns.print(5); // 25 64 | 65 | cout << endl; 66 | system("PAUSE"); 67 | return 0; 68 | } 69 | 70 | -------------------------------------------------------------------------------- /notes/备忘-Python相关工具.md: -------------------------------------------------------------------------------- 1 | 备忘-IPython小技巧 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [IPython](#ipython) 9 | - [自动重新加载模块](#自动重新加载模块) 10 | - [Anaconda](#anaconda) 11 | - [虚拟环境相关](#虚拟环境相关) 12 | - [PyCharm](#pycharm) 13 | - [Python Console](#python-console) 14 | - [常用库安装](#常用库安装) 15 | - [PyTorch 安装](#pytorch-安装) 16 | 17 | 18 | 19 | ## IPython 20 | 21 | ### 自动重新加载模块 22 | ``` 23 | %load_ext autoreload 24 | %autoreload 2 25 | ``` 26 | - 这个有时候也不太好用 27 | - 需要反复测试的，建议使用 Jupyter Notebook 28 | 29 | 30 | ## Anaconda 31 | 32 | ### 虚拟环境相关 33 | - 创建虚拟环境 34 | ``` 35 | conda create -n env_name anaconda python=3 36 | ``` 37 | - 复制虚拟环境 38 | ``` 39 | conda create --name dst_name --clone src_name 40 | ``` 41 | - 删除虚拟环境 42 | ``` 43 | conda remove --name nev_name --all 44 | ``` 45 | 46 | 47 | ## PyCharm 48 | 49 | ### Python Console 50 | - 默认 51 | ``` 52 | import sys; print('Python %s on %s' % (sys.version, sys.platform)) 53 | sys.path.extend([WORKING_DIR_AND_PYTHON_PATHS]) 54 | ``` 55 | - 修改为，避免每次重新导入 56 | ``` 57 | %load_ext autoreload 58 | %autoreload 2 59 | 60 | import sys; print('Python %s on %s' % (sys.version, sys.platform)) 61 | sys.path.extend([WORKING_DIR_AND_PYTHON_PATHS]) 62 | 63 | import numpy as np 64 | import tensorflow as tf 65 | ``` 66 | 67 | ## 常用库安装 68 | 69 | ### PyTorch 安装 70 | - Windows Python3.6 CPU 71 | - conda 提供的源很慢，不建议 72 | - 官网提供的 pip 安装方法，在安装 `torchvision` 时会报错 73 | - 正确安装方法（2018年8月17日15:09:40） 74 | ``` 75 | pip3 install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp36-cp36m-win_amd64.whl 76 | pip3 install --no-deps torchvision 77 | ``` 78 | > 相比官方，只添加了 `--no-deps` 参数 79 | - 推荐先下载在安装 80 | ``` 81 | pip3 install torch-0.4.1-cp36-cp36m-win_amd64.whl 82 | ``` 83 | -------------------------------------------------------------------------------- /code/my_tensorflow/demo.py: -------------------------------------------------------------------------------- 1 | from typing import ( 2 | TypeVar, Iterator, Iterable, overload, Container, 3 | Sequence, MutableSequence, Mapping, MutableMapping, Tuple, List, Any, Dict, Callable, Generic, 4 | Set, AbstractSet, FrozenSet, MutableSet, Sized, Reversible, SupportsInt, SupportsFloat, 5 | SupportsBytes, SupportsAbs, SupportsRound, IO, Union, ItemsView, KeysView, ValuesView, 6 | ByteString, Optional, AnyStr, Type, 7 | ) 8 | from src.utils import tf_float 9 | 10 | print(tf_float) 11 | 12 | import tensorflow as tf 13 | import numpy as np 14 | 15 | import tensorlayer as tl 16 | import tflearn as tfl 17 | 18 | tfl.prelu() 19 | tfl.utils.get_incoming_shape() 20 | 21 | from src.layers import multi_dense, Dense 22 | 23 | from tensorlayer.layers import DenseLayer, Conv2dLayer, RNNLayer, InputLayer, FlattenLayer 24 | 25 | from keras.layers import Dense, Conv2D, Merge, Flatten 26 | 27 | import keras.initializers 28 | 29 | import keras.utils.layer_utils 30 | 31 | import keras.activations 32 | 33 | tl.activation.lrelu 34 | tl.layers.PReluLayer 35 | 36 | tf.nn.leaky_relu 37 | 38 | np.arange() 39 | 40 | tf.nn.rnn_cell.LSTMCell 41 | 42 | import keras.backend as K 43 | 44 | K.softmax() 45 | K.permute_dimensions() 46 | 47 | tf.constant 48 | 49 | x = tf.constant(np.arange(16).reshape([8, 2]), dtype=tf.float32) 50 | 51 | o = multi_dense(x, [3, 4, 5], name="DenSe") 52 | 53 | # sess = tf.Session(graph=tf.get_default_graph()) 54 | tf.reset_default_graph() 55 | sess = tf.InteractiveSession() 56 | 57 | sess.run(tf.global_variables_initializer()) 58 | 59 | sess.run(o) 60 | 61 | 62 | def f(x): 63 | """ 64 | 65 | Args: 66 | x(function): 67 | 68 | Returns: 69 | 70 | """ 71 | 72 | 73 | def b(): 74 | """""" 75 | 76 | 77 | f(1) 78 | 79 | sorted() 80 | -------------------------------------------------------------------------------- /算法/README.md: -------------------------------------------------------------------------------- 1 | **题解** 2 | --- 3 | - [剑指Offer 题解](./题解-剑指Offer.md) 4 | - [Leetcode 题解](./题解-LeetCode.md) 5 | 6 | **专题** 7 | --- 8 | - [位运算](./专题-位运算.md) 9 | - [动态规划](./专题-动态规划.md) 10 | - [洗牌与采样](./专题-洗牌与采样.md) 11 | - [蓄水池抽样](./专题-洗牌与采样.md#蓄水池采样抽样) 12 | - [局部敏感哈希](./专题-局部敏感哈希.md) TODO 13 | - [经典算法问题](./专题-经典算法问题.md) TODO 14 | - 鹰蛋问题 15 | - ... 16 | 17 | **备忘** 18 | --- 19 | - [IO 模板](./备忘-IO模板.md) 20 | - [必备算法](./备忘-必备算法.md) 21 | 22 | Index 23 | --- 24 | 25 | 26 | - [Trick](#trick) 27 | - [时间复杂度](#时间复杂度) 28 | - [敏感空间](#敏感空间) 29 | - [排序](#排序) 30 | - [稳定排序与不稳定排序](#稳定排序与不稳定排序) 31 | - [堆排序-建堆的时间复杂度 `O(N)`](#堆排序-建堆的时间复杂度-on) 32 | 33 | 34 | 35 | # Trick 36 | 37 | ## 时间复杂度 38 | > [百度：ACM trick](https://www.baidu.com/s?wd=ACM%20trick) 39 | - 暴力枚举永远是第一个考虑的方法，但也有一些前提 40 | - 如果输入规模 `< 1e4`，那么可以尝试考虑 `O(n^2)` 的算法（两次循环暴力枚举）；如果 `≥ 1e5`，那么可能就需要考虑 `O(nlogn)` 的算法了——这不是绝对的，也可以通过剪枝等操作加速。 41 | - [C++] string 拼接的速度依次为：1）使用 `stringstream`；2）使用 `append()`；3）使用 `s += c`；4）使用 `s = s + c`——如果有大量拼接操作，要避免使用最后一种方法。 42 | 43 | ## 敏感空间 44 | > [ACM Trick点&&常用操作记录(持续更新)（敏感空间）](https://blog.csdn.net/feynman1999/article/details/79588347) - CSDN博客 45 | - `long long` 上界：`2^63-1 = 9,223,372,036,854,775,807 ≈ 9.223372e+18` 46 | - 阶乘：`20! = 2432902008176640000 ≈ 2.432902e+18` OK，`21!`超 47 | - Fibnacci 数列：`fib[92] = 7540113804746346429 ≈ 7.540114e+18` OK，`fib[93]` 超 48 | - Catalan 数： 49 | - 整数划分： 50 | - **数组大小**：如果内存限制为 2MB，那么最大可开辟的 int 数组为 `2*1024*1024/4 = 524288 ≈ 50,0000`，char 数组为 `2*1024*1024 = 2,097,152 ≈ 200,0000` 51 | - 实际单个数组是远开不了那么大，比如 windows 默认的栈好像只有 1MB 52 | - 在无法修改栈大小的情况下，可以使用 `new` 或 `malloc` 在**堆**上开辟内存 53 | ```C 54 | int *pa = malloc(sizeof(int)*1024*1024); 55 | int *pb = new int[1024*1024]; 56 | ``` 57 | 58 | # 排序 59 | 60 | ## 稳定排序与不稳定排序 61 | **稳定排序** 62 | - 冒泡排序、插入排序、归并排序、基数排序 63 | 64 | **不稳定排序** 65 | - 选择排序、快速排序、希尔排序、堆排序 66 | 67 | ## 堆排序-建堆的时间复杂度 `O(N)` 68 | - 堆排序的时间复杂度是 `O(NlogN)` 没有问题 69 | - 但是建堆的时间复杂度是 `O(N)` 70 | > [为什么建立一个二叉堆的时间为O(N)而不是O(Nlog(N))?](https://www.zhihu.com/question/264693363/answer/291397356) - 知乎 -------------------------------------------------------------------------------- /算法/专题-大数运算.md: -------------------------------------------------------------------------------- 1 | 专题-大数运算 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [大数取模](#大数取模) 9 | - [取模运算的性质](#取模运算的性质) 10 | - [快速幂取模](#快速幂取模) 11 | - [大数加/减/乘/除](#大数加减乘除) 12 | 13 | 14 | 15 | ## 大数取模 16 | 17 | ### 取模运算的性质 18 |
19 | 20 | - 因为 `(a%n) - (b%n)` 可能小于 `n`，所以 `+n` 21 | - 因为 `(a%n)(b%n)` 可能溢出，计算前应该强转为 `long long` 22 | 23 | **Code - C++** 24 | - 输入 `a` 为长度小于 1000 的字符串，`b` 为小于 `100000` 的整数 25 | ```C++ 26 | int big_mod(const string& a, int b) { 27 | long ret = 0; // 防止 ret * 10 溢出 28 | for (auto c : a) { 29 | ret = ((ret * 10) % b + (c - '0') % b) % b; // ret = ((ret * 10) + (c - '0')) % b 30 | } 31 | return (int)ret; 32 | } 33 | 34 | /* 示例说明 35 | 1234 % 11 == ((((0*10 + 1)*10 + 2)*10 + 3)*10 + 4) % 11 36 | == ((((0*10 + 1)*10 + 2)*10 + 3)*10 % 11 + 4 % 11) % 11 37 | == ((((0*10 + 1)*10 + 2)*10 % 11 + 3 % 11)*10 % 11 + 4 % 11) % 11 38 | == ((((0*10 + 1)*10 % 11 + 2 % 11)*10 % 11 + 3 % 11)*10 % 11 + 4 % 11) % 11 39 | == ((((0*10 % 11 + 1 % 11)*10 % 11 + 2 % 11)*10 % 11 + 3 % 11)*10 % 11 + 4 % 11) % 11 40 | ``` 41 | 42 | ### 快速幂取模 43 | - 计算 `a^n % b` 44 | - **基本方法**：根据取模的性质 3 —— `ab % m == (a%m)(b%m) % m` 45 | ```C++ 46 | int big_mod(int a, int n, int b) { 47 | long long ret = 1; 48 | while(n--) { 49 | ret *= a % b; 50 | ret %= b; 51 | } 52 | return (int)ret; 53 | 54 | } 55 | ``` 56 | - 时间复杂度 `O(N)` 57 | - **快速幂取模** 58 | ```C++ 59 | int big_mod(int a, int n, int b) { 60 | long long ret = 1; 61 | while(n) { 62 | if (n & 1) 63 | ret = (ret*a) % b; 64 | a = (a*a) % b; 65 | n >>= 1; 66 | } 67 | return (int)ret; 68 | } 69 | ``` 70 | - 代码跟快速幂很像 71 | - 示例说明 72 | ``` 73 | 2^10 % 11 == (2^5 % 11)(2^5 % 11) % 11 74 | == ((2 % 11)(2^4 % 11))((2 % 11)(2^4 % 11)) % 11 75 | == ... 76 | ``` 77 | 78 | ## 大数加/减/乘/除 79 | > [大数的四则运算（加法、减法、乘法、除法） - 落枫飘飘](https://www.cnblogs.com/wuqianling/p/5387099.html) - 博客园 -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/NLP-TODO-问答模型.md: -------------------------------------------------------------------------------- 1 | NLP-问答模型 2 | === 3 | 4 | 问答模型简述 5 | --- 6 | - 问答模型可以概括为 Seq2Seq 模型，但通常会比较复杂 7 | > [SQuAD文本理解挑战赛十大模型解读](https://www.sohu.com/a/216468593_500659)_搜狐科技_搜狐网 8 | 9 | Index 10 | --- 11 | 12 | 13 | - [QANet 模型](#qanet-模型) 14 | - [BiDAF 模型](#bidaf-模型) 15 | - [模型结构](#模型结构) 16 | - [参数规模 TODO](#参数规模-todo) 17 | - [论文/代码/参考](#论文代码参考) 18 | - [decaNLP 模型](#decanlp-模型) 19 | - [论文/代码](#论文代码) 20 | 21 | 22 | 23 | ## QANet 模型 24 | 25 | ## BiDAF 模型 26 | 27 | ### 模型结构 28 | - 模型共 6 层 29 |
30 | 31 | - **嵌入层**（char + word） 32 | - 记**材料**（context）和**查询**（query）分别为 33 |
34 | 35 | - 词向量使用预训练的 GloVe，OOV 可训练 36 | - 字向量由 CharCNN 训练 37 | > NLP-词向量#[CharCNN](./NLP-词向量.md#charcnn-字向量) 38 | - CharCNN 负责将**每个单词**映射到一个**向量** 39 | - 实际为单词中每个**字符向量**的叠加平均，具体由一个**卷积层和池化层**实现 40 | - 字符嵌入层实际上生成的也是一个词向量，但是使用的字符信息，而不是上下文信息 41 | - 每个单词的表示由词向量和字向量拼接而成，然后经过两层 highway 得到 centext vector 42 | - **Word Embedding Layer** 43 | - 使用预训练好的 GloVe 词向量 44 | > NLP-词向量#[GloVe](./NLP-词向量.md#glove) 45 | - 默认词向量和字符向量的维度相同 46 | - **输入 Embedding** 由 Character Embedding 和 Word Embedding **拼接**而成 47 | - **Contextual Embedding Layer** 48 | - 利用周围词语的语境线索（contextual cues）来**改善**（refine）词的嵌入。 49 | - 双向 LSTM 50 | - **Attention Flow Layer** 51 | - 耦合（couple）材料与问题，并为上下文中的**每个词**生成一组**查询感知特征向量**（query-aware feature vectors） 52 | - **Modeling Layer** 53 | - 使用 RNN 扫描（scan）上下文 54 | - **Output Layer** 55 | - 产生问题的答案 56 | 57 | ### 参数规模 TODO 58 | 59 | 60 | ### 论文/代码/参考 61 | - [1611.01603] [Bidirectional Attention Flow for Machine Comprehension](https://arxiv.org/abs/1611.01603) 62 | - [allenai/bi-att-flow](https://github.com/allenai/bi-att-flow/tree/dev) (TF1.0+) - GitHub 63 | - BiDAF https://allenai.github.io/bi-att-flow/ 64 | - [论文笔记 - Bi-Directional Attention Flow for Machine Comprehension](http://www.shuang0420.com/2018/04/01/论文笔记%20-%20Bi-Directional%20Attention%20Flow%20for%20Machine%20Comprehension/) | 徐阿衡 65 | 66 | ## decaNLP 模型 67 | > [一个模型搞定十大自然语言任务](https://www.toutiao.com/i6569393480089469454)｜论文+代码 68 | 69 | ### 论文/代码 70 | - [1806.08730] [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730) 71 | - [salesforce/decaNLP](https://github.com/salesforce/decaNLP) - GitHub 72 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/layers/dense.py: -------------------------------------------------------------------------------- 1 | """ 2 | 全连接层 3 | 4 | References: 5 | tensorlayer.layers.DenseLayer 6 | keras.layers.Dense 7 | """ 8 | import tensorflow as tf 9 | 10 | from ..utils import get_wb 11 | from ..activations import relu 12 | from ..activations import linear 13 | 14 | 15 | def dense(x, n_unit, act_fn=relu, name=None, reuse=None): 16 | """全连接层 17 | Input shape: [batch_size, n_input] 18 | Output shape: [batch_size, n_unit] 19 | 20 | 如果需要 reuse 推荐使用类实现的 `Dense` 21 | 22 | Args: 23 | x(tf.Tensor): 24 | n_unit(int): 25 | act_fn: 26 | name(str): 27 | reuse(bool): 28 | """ 29 | # n_input = tf.shape(x)[-1] # err: need int but tensor 30 | n_input = int(x.get_shape()[-1]) 31 | with tf.variable_scope(name or "dense", reuse=reuse): 32 | W, b = get_wb([n_input, n_unit]) 33 | o = act_fn(tf.matmul(x, W) + b) 34 | return o 35 | 36 | 37 | def multi_dense(x, n_unit_ls, act_fn=relu, name=None): 38 | """多层全连接 39 | Input shape: [batch_size, n_input] 40 | Output shape: [batch_size, n_unit_list[-1]] 41 | 42 | Args: 43 | x(tf.Tensor): 44 | n_unit_ls(list of int): 45 | act_fn: 46 | name(str): 47 | """ 48 | # n_layer = len(n_unit_list) 49 | name = name or "dense" 50 | for i, n_unit in enumerate(n_unit_ls): 51 | x = dense(x, n_unit, act_fn=act_fn, name="{}-{}".format(name, i)) 52 | 53 | return x 54 | 55 | 56 | def linear_dense(x, n_unit, name=None, reuse=None): 57 | """线性全连接层 58 | Input shape: [batch_size, n_input] 59 | Output shape: [batch_size, n_unit] 60 | 61 | Args: 62 | x(tf.Tensor): 63 | n_unit(int): 64 | name(str): 65 | reuse(bool) 66 | """ 67 | return dense(x, n_unit, act_fn=linear, name=(name or "linear_dense"), reuse=reuse) 68 | 69 | 70 | class Dense(object): 71 | """全连接层的类实现，方便 reuse""" 72 | 73 | def __init__(self, n_unit, act_fn=relu, name=None): 74 | """""" 75 | self.n_unit = n_unit 76 | self.act_fn = act_fn 77 | self.name = name 78 | 79 | self._built = False 80 | 81 | def _build(self, n_input): 82 | """""" 83 | with tf.variable_scope(self.name or "dense"): 84 | self.W, self.b = get_wb([n_input, self.n_unit]) 85 | 86 | def _call(self, x): 87 | """""" 88 | o = self.act_fn(tf.matmul(x, self.W) + self.b) 89 | return o 90 | 91 | def __call__(self, x): 92 | """""" 93 | n_input = int(x.get_shape()[-1]) 94 | if not self._built: 95 | self._build(n_input) 96 | self._built = True 97 | 98 | return self._call(x) 99 | -------------------------------------------------------------------------------- /编程语言/Cpp-面向对象编程.md: -------------------------------------------------------------------------------- 1 | C++-面向对象编程 2 | === 3 | 4 | 面向对象编程概述 5 | --- 6 | - 面向对象编程的两个主要特征：**继承**、**多态** 7 | - C++ 中是通过间接利用“**指向父类**”的**指针**或**引用**来操作其子类对象，从而达到多态的目的；如果直接操作某个实例，那么多态将无从谈起 8 | - 具体来说，**多态**是通过**动态绑定**机制，达到在**运行时**确定实际被调用的是哪个子类类型，进而调用对应的 **override** 方法 9 | 10 | Reference 11 | --- 12 | - 《Essential C++》 第 4/5 章 - Lippman, 侯捷 13 | 14 | Index 15 | --- 16 | 17 | 18 | - [不通过继承实现多态](#不通过继承实现多态) 19 | - [抽象基类](#抽象基类) 20 | 21 | 22 | 23 | ## 不通过继承实现多态 24 | > 4.11 指针，指向 Class member function 25 | - 核心想法：手工调整函数指针，模拟**动态绑定**的效果 26 |
示例：动态序列（点击展开） 27 | 28 | ```Cpp 29 | class num_sequence { 30 | public: 31 | // PtrType 是一个指针，指向 num_sequence 的成员函数， 32 | // 该成员函数必须只接受一个 int 型参数，以及返回类型为 void 33 | typedef void (num_sequence::*PtrType)(int); 34 | 35 | enum { cnt_seq = 2 }; // 预定义了两种序列 36 | enum ns_type { 37 | ns_fibonacci, ns_square 38 | }; 39 | 40 | // 构造函数：默认指向斐波那契数列 41 | num_sequence(): _pmf(func_tbl[ns_fibonacci]) { } 42 | 43 | // 调整指针指向 44 | void set_sequence(ns_type nst) { 45 | switch (nst) { 46 | case ns_fibonacci: case ns_square: 47 | _pmf = func_tbl[nst]; 48 | break; 49 | default: 50 | cerr << "invalid sequence type\n"; 51 | } 52 | } 53 | 54 | void print(int n) { 55 | (this->*_pmf)(n); // 通过指针选择需要调用的函数 56 | } 57 | 58 | // _pmf 可以指向以下任何一个函数 59 | void fibonacci(int n) { 60 | int f = 1; 61 | int g = 1; 62 | for (int i = 2; i <= n; i++) 63 | g = g + f, f = g - f; 64 | cout << f << endl; 65 | } 66 | 67 | void square(int n) { 68 | cout << n * n << endl; 69 | } 70 | 71 | private: 72 | PtrType _pmf; 73 | static PtrType func_tbl[cnt_seq]; // 保存所有序列函数的指针 74 | // 为了兼容性，不推荐写成 `static vector没有空格> _seq;` 75 | }; 76 | 77 | // static 成员变量初始化 78 | num_sequence::PtrType 79 | num_sequence::func_tbl[cnt_seq] = { 80 | &num_sequence::fibonacci, 81 | &num_sequence::square, 82 | }; 83 | 84 | int main() { 85 | 86 | auto ns = num_sequence(); 87 | ns.print(5); // 5 88 | ns.set_sequence(num_sequence::ns_square); // 调整函数指针以获得多态的效果 89 | ns.print(5); // 25 90 | 91 | cout << endl; 92 | system("PAUSE"); 93 | return 0; 94 | } 95 | ``` 96 | 97 |
98 | 99 | > [源文件](../code/cpp/面向对象-不通过继承实现多态-动态序列.cpp) 100 | 101 | ## 抽象基类 102 | - 仅仅为了**设计**/**定义规范**而存在 -------------------------------------------------------------------------------- /算法/备忘-必备算法.md: -------------------------------------------------------------------------------- 1 | 备忘-必备算法 2 | === 3 | - 一些必备算法，主要是 C++ 版本 4 | Index 5 | --- 6 | 7 | 8 | - [二分查找](#二分查找) 9 | - [离散版](#离散版) 10 | - [`my_binary_search(vector, int)`](#my_binary_searchvectorint-int) 11 | - [`my_lower_bound(vector, int)`](#my_lower_boundvectorint-int) 12 | - [`my_upper_bound(vector, int)`](#my_upper_boundvectorint-int) 13 | - [排序](#排序) 14 | - [堆排序](#堆排序) 15 | - [建堆的时间复杂度](#建堆的时间复杂度) 16 | 17 | 18 | 19 | 20 | ## 二分查找 21 | 22 | ### 离散版 23 | 24 | #### `my_binary_search(vector, int)` 25 | - 没有重复元素时，目标值若存在，则返回索引；若不存在，返回 -1 26 | - 存在重复元素时，目标值若存在，则返回最小索引；若不存在，返回 -1 27 | ```C++ 28 | int my_binary_search(vector& nums, int v) { 29 | if (nums.size() < 1) return - 1; 30 | 31 | int lo = -1, hi = nums.size(); // hi = nums.size() - 1 32 | 33 | while (hi - lo > 1) { 34 | int mid = lo + (hi - lo) / 2; 35 | if (nums[mid] < v) 36 | lo = mid; 37 | else 38 | hi = mid; 39 | } 40 | 41 | return nums[lo + 1] == v ? lo + 1 : -1; 42 | } 43 | ``` 44 | 45 | #### `my_lower_bound(vector, int)` 46 | - 返回大于、等于目标值的最小索引（第一个大于或等于目标值的索引） 47 | ```C++ 48 | int my_lower_bound(vector& nums, int v) { 49 | if (nums.size() < 1) return -1; 50 | 51 | int lo = -1, hi = nums.size(); // hi = nums.size() - 1 52 | 53 | while (hi - lo > 1) { // 退出循环时有：lo + 1 == hi 54 | int mid = lo + (hi - lo) / 2; 55 | if (nums[mid] < v) 56 | lo = mid; // 因为始终将 lo 端当做开区间，所以没有必要 `lo = mid + 1;` 57 | else 58 | hi = mid; // 而在 else 中，mid 可能就是最后的结果，所以不能 `hi = mid - 1` 59 | } 60 | 61 | return lo + 1; // 相比 binary_search，只有返回值不同 62 | } 63 | ``` 64 | - **为什么返回 `lo + 1`？** 65 | - 模板开始时将 (lo, hi) 看做是一个开区间，通过不断二分，最终这个区间中只会含有一个值，即 (lo, hi] 66 | - 返回 lo+1 的含义是，结果就在 lo 的下一个； 67 | - 在迭代的过程中，hi 会从开区间变为闭区间，而 lo 始终是开区间，返回 lo+1 显得更加统一。 68 | - 当然，这跟迭代的写法是相关的，你也可以使最终的结果区间是 [lo, hi)，这取决于个人习惯。 69 | 70 | #### `my_upper_bound(vector, int)` 71 | - 返回大于目标值的最小索引（第一个大于目标值的索引） 72 | ```C++ 73 | int my_upper_bound(vector& nums, int v) { 74 | if (nums.size() < 1) return -1; 75 | 76 | int lo = -1, hi = nums.size(); // hi = nums.size() - 1 77 | 78 | while (hi - lo > 1) { 79 | int mid = lo + (hi - lo) / 2; 80 | 81 | if (nums[mid] <= v) // 相比 lower_bound，唯一不同点：`<` -> `<=` 82 | lo = mid; 83 | else 84 | hi = mid; 85 | } 86 | 87 | return lo + 1; 88 | } 89 | ``` 90 | 91 | ## 排序 92 | 93 | ### 堆排序 94 | ```C++ 95 | 96 | ``` 97 | 98 | #### 建堆的时间复杂度 99 | > [为什么建立一个二叉堆的时间为O(N)而不是O(Nlog(N))?](https://www.zhihu.com/question/264693363/answer/291397356) - 知乎 100 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/utils/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | 工具函数 3 | """ 4 | from pprint import pprint 5 | from functools import reduce 6 | from operator import mul 7 | 8 | from .array_op import * 9 | from .math_op import * 10 | from ..regularizers import l2_regularizer 11 | 12 | import numpy as np 13 | import tensorflow as tf 14 | 15 | tf_float = tf.float32 16 | zeros = tf.initializers.zeros 17 | truncated_normal = tf.initializers.truncated_normal 18 | 19 | SPLIT_LINE = "-----------" 20 | 21 | 22 | def foo(): 23 | print("foo") 24 | 25 | 26 | def get_shape(x): 27 | """ 28 | References: 29 | tflearn.utils.get_incoming_shape 30 | """ 31 | if isinstance(x, (tf.Tensor, tf.SparseTensor)): 32 | return x.get_shape().as_list() 33 | elif type(x) in [np.array, np.ndarray, list, tuple]: 34 | return list(np.shape(x)) 35 | else: 36 | raise Exception("Invalid `x`.") 37 | 38 | 39 | def get_wb(shape, 40 | w_initializer=truncated_normal, 41 | b_initializer=zeros, 42 | w_regularizer=l2_regularizer, 43 | b_regularizer=l2_regularizer, 44 | name=None): 45 | """""" 46 | name = "" if name is None else name + '_' 47 | W = tf.get_variable(name + 'W', shape=shape, 48 | dtype=tf_float, initializer=w_initializer, regularizer=w_regularizer) 49 | b = tf.get_variable(name + 'b', shape=shape[-1:], 50 | dtype=tf_float, initializer=b_initializer, regularizer=b_regularizer) 51 | return W, b 52 | 53 | 54 | def get_w(shape, 55 | w_initializer=truncated_normal, 56 | w_regularizer=l2_regularizer, 57 | name=None): 58 | name = "" if name is None else name + '_' 59 | W = tf.get_variable(name + 'W', shape, dtype=tf_float, initializer=w_initializer, 60 | regularizer=w_regularizer) 61 | return W 62 | 63 | 64 | def get_params_dict(): 65 | """以字典形式获取所有 trainable 参数""" 66 | param_dict = dict() 67 | for var in tf.trainable_variables(): 68 | param_dict[var.name] = {"shape": list(map(int, var.shape)), 69 | "number": int(reduce(mul, var.shape, 1))} 70 | return param_dict 71 | 72 | 73 | def print_params_dict(): 74 | """""" 75 | print(SPLIT_LINE) 76 | print("params_dict") 77 | param_dict = get_params_dict() 78 | # pprint(param_dict, indent=2) 79 | for k, v in param_dict.items(): 80 | print(' ', k, '\t', end='') 81 | pprint(v, indent=2) 82 | # for vk, vv in v.items(): 83 | # print(vk, '-', vv, '\t', end='') 84 | # print() 85 | 86 | 87 | def get_params_number(): 88 | """获取参数总量""" 89 | param_dict = get_params_dict() 90 | return sum((item["number"] for item in param_dict.values())) 91 | 92 | 93 | def print_params_number(): 94 | """""" 95 | print(SPLIT_LINE) 96 | print("params_number:", get_params_number()) 97 | -------------------------------------------------------------------------------- /算法/题解-LeetCode.md: -------------------------------------------------------------------------------- 1 | 题解-LeetCode 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [数组](#数组) 9 | - [三数之和（双指针）](#三数之和双指针) 10 | - [最接近的三数之和（双指针）](#最接近的三数之和双指针) 11 | 12 | 13 | 14 | ## 数组 15 | 16 | ### 三数之和（双指针） 17 | > https://leetcode-cn.com/problems/3sum/description/ 18 | 19 | **问题描述** 20 | ``` 21 | 给定一个包含 n 个整数的数组 nums，判断 nums 中是否存在三个元素 a，b，c ，使得 a + b + c = 0 ？ 22 | 找出所有满足条件且不重复的三元组。 23 | ``` 24 | 25 | **思路** 26 | - 因为需要输出所有结果，所以不推荐使用 map 来做 27 | - 判断 `a + b + c = 0`，实际上等价于判断 `-a = b + c` 28 | - 基本思路：对数组排序后，对每个 `a`，用首尾双指针进行遍历，具体过程看代码更清晰 29 | - 去重的方法：排序后，跳过相同的数即可 30 | - 注意边界条件 31 | 32 | **C++** 33 | ```C++ 34 | class Solution { 35 | public: 36 | vector> threeSum(vector& nums) { 37 | if (nums.size() < 3) return vector>(); // 输入数量小于 3 直接退出 38 | 39 | sort(nums.begin(), nums.end()); // 排序 40 | 41 | vector> ret; 42 | for (int i = 0; i <= nums.size() - 3; i++) { 43 | if (i > 0 && nums[i] == nums[i - 1]) continue; // 跳过第一个数相同的情况 44 | 45 | int target = -nums[i]; 46 | int lo = i + 1; 47 | int hi = nums.size() - 1; 48 | while (lo < hi) { 49 | if (nums[lo] + nums[hi] < target) 50 | lo++; 51 | else if (nums[lo] + nums[hi] > target) 52 | hi--; 53 | else { 54 | ret.push_back({ nums[i], nums[lo], nums[hi] }); 55 | lo++, hi--; // 不要忘了这双指针都要移动 56 | 57 | while (lo < hi && nums[lo] == nums[lo - 1]) lo++; // 跳过第二个数相同的情况 58 | while (lo < hi && nums[hi] == nums[hi + 1]) hi--; // 跳过第三个数相同的情况 59 | } 60 | } 61 | } 62 | return ret; 63 | } 64 | }; 65 | ``` 66 | 67 | ### 最接近的三数之和（双指针） 68 | > https://leetcode-cn.com/problems/3sum-closest/description/ 69 | 70 | **题目描述** 71 | ``` 72 | 给定一个包括 n 个整数的数组 nums 和 一个目标值 target。找出 nums 中的三个整数，使得它们的和与 target 最接近。返回这三个数的和。假定每组输入只存在唯一答案。 73 | 74 | 例如，给定数组 nums = [-1，2，1，-4], 和 target = 1. 75 | 76 | 与 target 最接近的三个数的和为 2. (-1 + 2 + 1 = 2). 77 | ``` 78 | 79 | **思路** 80 | - 同[三数之和（双指针）](#三数之和双指针) 81 | - 区别仅在于循环内的操作不同 82 | 83 | **C++** 84 | ```C++ 85 | class Solution { 86 | public: 87 | int threeSumClosest(vector& nums, int target) { 88 | if (nums.size() < 3) return 0; 89 | 90 | sort(nums.begin(), nums.end()); // 别忘了排序 91 | 92 | auto ret = nums[0] + nums[1] + nums[2]; // 保存答案 93 | for (int i = 0; i <= nums.size()-3; i++) { 94 | int lo = i + 1; 95 | int hi = nums.size() - 1; 96 | 97 | while (lo < hi) { 98 | auto sum = nums[i] + nums[lo] + nums[hi]; 99 | if (abs(target - sum) < abs(target - ret)) { 100 | ret = sum; 101 | if (ret == target) 102 | return ret; 103 | } 104 | sum < target ? lo++ : hi--; 105 | } 106 | } 107 | return ret; 108 | } 109 | }; 110 | ``` -------------------------------------------------------------------------------- /notes/备忘-常用子函数.md: -------------------------------------------------------------------------------- 1 | **RoadMap** 2 | --- 3 | 4 | 5 | - [暴力搜索](#暴力搜索) 6 | - [遍历所有子集](#遍历所有子集) 7 | - [遍历所有正因子](#遍历所有正因子) 8 | - [二分查找](#二分查找) 9 | - [离散模板](#离散模板) 10 | - [连续模板](#连续模板) 11 | - [位运算](#位运算) 12 | - [计算二进制表示中`1`的个数](#计算二进制表示中1的个数) 13 | - [判断第`i`位是否为 1](#判断第i位是否为-1) 14 | - [置第`i`位为 1 且不影响其他位](#置第i位为-1-且不影响其他位) 15 | - [判断奇偶](#判断奇偶) 16 | - [简单快速幂](#简单快速幂) 17 | 18 | 19 | 20 | # 暴力搜索 21 | 22 | ## 遍历所有子集 23 | - 代码 24 | - **imhuay**/**Algorithm_for_Interview**/_utils工具函数/[遍历所有子集.hpp](https://github.com/imhuay/Algorithm_for_Interview-Chinese/blob/master/Algorithm_for_Interview/_utils工具函数/遍历所有子集.hpp) 25 | - 相关问题 26 | 27 | ## 遍历所有正因子 28 | - 代码 29 | - **imhuay**/**Algorithm_for_Interview**/_utils工具函数/[遍历所有正因子.hpp](https://github.com/imhuay/Algorithm_for_Interview-Chinese/blob/master/Algorithm_for_Interview/_utils工具函数/遍历所有正因子.hpp) 30 | - 相关问题 31 | 32 | # 二分查找 33 | 34 | ## 离散模板 35 | 36 | ## 连续模板 37 | 38 | # 位运算 39 | - 代码 40 | - **imhuay**/**Algorithm_for_Interview**/_utils工具函数/[位运算.hpp](https://github.com/imhuay/Algorithm_for_Interview-Chinese/blob/master/Algorithm_for_Interview/_utils工具函数/位运算.hpp) 41 | 42 | ## 计算二进制表示中`1`的个数 43 | - **法 1**：O(m) - m 为 1 的个数 44 | ```C 45 | int numberOfOne(int n) { 46 | int cnt = 0; 47 | while (n > 0) { 48 | cnt++; 49 | } 50 | return cnt; 51 | } 52 | /* 说明： 53 | n-1 后会翻转右数第一个 1 及之后的每一位； 54 | 然后 n&(n-1) 会把该位置的 1 及之后的每一位置零； 55 | 因此每次循环恰好消去一个 1 直到变为 0 56 | n = 100,100 57 | n-1 = 100,011 58 | n = n&(n-1) = 100,000 cnt++ 59 | n-1 = 011,111 60 | n = n&(n-1) = 000,000 cnt++ 61 | */ 62 | ``` 63 | - **法2**：O(n) - n 为二进制的长度 64 | ```C 65 | int numberOfOne(int n) { 66 | int cnt = 0; 67 | while (n > 0) { 68 | if (n & 1) 69 | cnt++; 70 | n >>= 1; 71 | } 72 | return cnt; 73 | } 74 | /* 说明： 75 | 每次移动一位，判断是否为 1 76 | */ 77 | ``` 78 | 79 | ## 判断第`i`位是否为 1 80 | - 构造一个第`i`位是`1`的数与原数做**与运算** 81 | ```C 82 | int i = 4; // 因为 i 从 0 开始，所以这实际上是右起第 5 位 83 | int n = 16; // 10000 84 | // 法 1：移动 1 85 | if (n & (1 << i)) 86 | cout << "true" << endl; 87 | // 法 2：移动 n 88 | if ((n >> i) & 1) 89 | cout << "true" << endl; 90 | ``` 91 | 92 | ## 置第`i`位为 1 且不影响其他位 93 | - 类似[判断第`i`位是否为 1](#判断第i位是否为-1)的思路 94 | ``` 95 | int ret = 3; // 011 96 | int i = 2; 97 | ret ^= 1 << i; // 111 98 | ``` 99 | 100 | ## 判断奇偶 101 | - 判断奇偶，实际上就是判断二进制表示的最后一位是否为 1 102 | ```C 103 | n = 5; 104 | if (n & 1) // 只适用于正整数，但是效率更高 105 | cout << n << " 是奇数" << endl; 106 | 107 | // 常规方法 108 | if (n % 2 != 0) // 适用于正、负数 109 | cout << n << " 是奇数" << endl; 110 | ``` 111 | 112 | ## 简单快速幂 113 | ```C 114 | double quickPow(double base, int p) { 115 | double ret = 1.0; 116 | int q = abs(p); 117 | while (q > 0) { 118 | if (q & 1) 119 | ret *= base; 120 | base *= base; 121 | q >>= 1; 122 | } 123 | return p > 0 ? ret : 1 / ret; 124 | } 125 | ``` 126 | - 用一个例子来说明这段代码更直观 127 | - 求 `3^20 = 9^10 = 81^5 (= 81*81^4) = 81*6561^2 = 81*43046721` 128 | - 循环次数 = `bin(20)`的位数 = `len(10100)` = 5 129 | 130 | -------------------------------------------------------------------------------- /笔试面经/笔试-网易.md: -------------------------------------------------------------------------------- 1 | 笔试-网易 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [网易互娱-0809](#网易互娱-0809) 9 | - [1.](#1) 10 | - [2.](#2) 11 | - [3.](#3) 12 | - [网易互联网-0811](#网易互联网-0811) 13 | - [1. 瞌睡](#1-瞌睡) 14 | - [2. 丰收](#2-丰收) 15 | - [3. 小易的字典](#3-小易的字典) 16 | 17 | 18 | 19 | 20 | ## 网易互娱-0809 21 | 22 | ### 1. 23 | 24 | ### 2. 25 | 26 | ### 3. 27 | 28 | 29 | ## 网易互联网-0811 30 | > [网易2019校招笔试编程题参考代码](https://www.nowcoder.com/discuss/92989)_笔经面经_牛客网 31 | ### 1. 瞌睡 32 | 33 | **Code（未测试）** 34 | ```C++ 35 | #include 36 | using namespace std; 37 |   38 | int main() { 39 | int n, k; 40 | cin >> n >> k; 41 | 42 | vector a(n), t(n); 43 | for (int i = 0; i < n; i++) 44 | cin >> a[i]; 45 | int now = 0; 46 | for (int i = 0; i < n; i++) 47 | cin >> t[i], now += t[i] * a[i]; 48 | int res = now; 49 | for (int i = 0; i < n;) { 50 | now += (!t[i]) * a[i]; 51 | if (++i >= k) { 52 | res = max(res, now); 53 | now -= (!t[i - k]) * a[i - k]; 54 | } 55 | } 56 | cout << res << endl; 57 | return 0; 58 | } 59 | 60 | /* 测试样例 61 | 输入 62 | 6 3 63 | 1 3 5 2 5 4 64 | 1 1 0 1 0 0 65 | 66 | 输出 67 | 16 68 | */ 69 | ``` 70 | 71 | ### 2. 丰收 72 | **题目描述** 73 |
74 | 75 | **思路** 76 | - 维护**前缀和数组**，二分查找答案 77 | 78 | **Code(AC) - C++** 79 | ```C++ 80 | int main() { 81 | 82 | int n; 83 | cin >> n; 84 | vector a; 85 | int tmp; 86 | for (int i = 0; i < n; i++) { 87 | cin >> tmp; 88 | a.push_back(tmp); 89 | } 90 | 91 | vector as; 92 | int sum = 0; 93 | for (auto i : a) { 94 | sum += i; 95 | as.push_back(sum); 96 | } 97 | 98 | int m; 99 | cin >> m; 100 | vector q; 101 | for (int i = 0; i < m; i++) { 102 | cin >> tmp; 103 | q.push_back(tmp); 104 | } 105 | 106 | for (auto i : q) { 107 | auto ret = lower_bound(as.begin(), as.end(), i); 108 | cout << ret - as.begin() + 1 << endl; 109 | } 110 | 111 | //cout << endl; 112 | //system("PAUSE"); 113 | return 0; 114 | } 115 | ``` 116 | 117 | ### 3. 小易的字典 118 |
119 | 120 | **思路** 121 | - 贪心 122 | - DP 123 | 124 | **Code（未测试）** 125 | ```C++ 126 | #include 127 | using namespace std; 128 | const int N = 200 + 5; 129 | const int MX = (int)1e9 + 7; 130 | int comb[N][N]; 131 | void init(){ 132 | for(int i = 0; i < N; i ++) { 133 | comb[i][0] = comb[i][i] = 1; 134 | for(int j = 1; j < i; j ++) { 135 | comb[i][j] = comb[i-1][j] + comb[i-1][j-1]; 136 | if(comb[i][j] >= MX) comb[i][j] = MX; 137 | } 138 | } 139 | } 140 | int main() 141 | { 142 | //freopen("in.txt","r",stdin); 143 | //freopen("out.txt","w",stdout); 144 | int n, m, k; 145 | init(); 146 | while(cin >> n >> m >> k) { 147 | if(k > comb[n + m][n]) { 148 | cout << -1 << endl; 149 | continue; 150 | } 151 | string ans = ""; 152 | while(k > 0 && n > 0) { 153 | if(comb[n + m - 1][n - 1] >= k) { 154 | ans += 'a'; 155 | n--; 156 | } else { 157 | ans += 'z'; 158 | k = k - comb[n + m - 1][n - 1]; 159 | m--; 160 | } 161 | } 162 | while(m--) ans += 'z'; 163 | cout << ans << endl; 164 | } 165 | return 0; 166 | } 167 | ``` --------------------------------------------------------------------------------