esp32-freertos-sdk 工具包 See the Getting Started guide links above for a detailed setup guide. This is a quick reference for common commands when working with ESP-IDF projects: Setup Build Environment (See Getting Started guide for a full list of required steps with details.) Install host build dependencies mentioned in Getting Started guide. Add tools/ directory to the PATH Run python -m pip install -r requirements.txt to install Python dependencies Configuring the Project idf.py menuconfig Opens a text-based configuration menu for the project. Use up & down arrow keys to navigate the menu. Use Enter key to go into a submenu, Escape key to go out or to exit. Type ? to see a help screen. Enter key exits the help screen. Use Space key, or Y and N keys to enable (Yes) and disable (No) configuration items with checkboxes "[*]" Pressing ? while highlighting a configuration item displays help about that item. Type / to search the configuration items. Once done configuring, press Escape multiple times to exit and say "Yes" to save the new configuration when prompted. Compiling the Project idf.py build ... will compile app, bootloader and generate a partition table based on the config. Flashing the Project When the build finishes, it will print a command line to use esptool.py to flash the chip. However you can also do this automatically by running: idf.py -p PORT flash Replace PORT with the name of your serial port (like COM3 on Windows, /dev/ttyUSB0 on Linux, or /dev/cu.usbserial-X on MacOS. If the -p option is left out, idf.py flash will try to flash the first available serial port. This will flash the entire project (app, bootloader and partition table) to a new chip. The settings for serial port flashing can be configured with idf.py menuconfig. You don't need to run idf.py build before running idf.py flash, idf.py flash will automatically rebuild anything which needs it. Viewing Serial Output The idf.py monitor target uses the idf_monitor tool to display se

采用tf-idf算法计算携程评论中的关键词，并输出前500个关键词，该算法不同于市面上的其他算法，保证了o（n）的时间复杂度，执行速度更快，同时具有更好的移植性和健壮性

计算TF-IDF的程序，使用java编写，能计算出输入文档的TF-idf值

这是一个使用python实现TF-IDF算法的代码，具体介绍见本人博客

通过python代码实现TF-IDF算法，并对文本提取关键词，可以自己添加词库以及停用词表。

算法思想：提取文档的TF/IDF权重，然后用余弦定理计算两个多维向量的距离来计算两篇文档的相似度，用标准的k-means算法就可以实现文本聚类。源码为java实现

其于原有20万带IDF权重的词典，经过去重，增加，合并后成了120万； 线上系统正在使用中，非常不错； 后面我会将常用度量的也加上。最终形成超全的词库

用来测试tf-idf的4个新闻,用来测试tf-idf的4个新闻,用来测试tf-idf的4个新闻

该资源属于代码类，用C语言和Python实现了TF-IDF算法，适用于文本分类等特征权重抽取

在Hadoop集群中，用MapReduce分布式计算TFIDF