python_arima

Kaggle competition final submission score 0.985(Kaggle competition final submission score 0.985 fsaaddaewfew5sadf4asf)

本文给出使用Python做小波包特征提取的一个实例，并附有MATLAB编写的实现源码。 数据来源：自己采集得到的模拟电路故障响应数据，存在SumData文件夹中，其中文件夹共有9个excel文件，每一个excel文件存的是某种故障状态下采集得到的100组故障响应数据。(In this paper, an example of using Python to extract the features of wavelet packet is given, and the source code written by MATLAB is attached. Data Source: The fault response data of analog circuits collected by ourselves are stored in the SumData folder. There are nine Excel files in the folder. Each excel file contains 100 sets of fault response data collected under some fault condition.)

说明：  简单的电池建模(戴维南模型)以及卡尔曼滤波器，用于估算模拟锂电池的充电状态（SoC）估计。可模拟CC / CV充电和脉冲放电。 然后显示图形，显示仿真结果。 可以在Python / main.py中使用容量和Thevenin模型值。 充电/放电协议也可以更改，值可在Python / protocol.py中找到。(Simple battery modeling (the Thevenin model) and the Kalman filter are used to estimate the state of charge (SoC) of an analog lithium battery. Can simulate CC / CV charging and pulse discharge. Then display the graph and display the simulation results. The capacity and Thevenin model values can be used in Python / main.py. The charge/discharge protocol can also be changed and the values can be found in Python / protocol.py.)

说明：  ABAQUS Python二次开发攻略-配书资源code(ABAQUS Python secondary development strategy - book resource code)

关于各种GAN的代码实列，包括ｃｇａｎ，ｉｎｆｏｇａｎ，ｗｇａｎ等。(Code listings for various GANs, including cgan, infogan, wgan, etc.)

说明：  训练一个深度学习的循环神经网络，可实现获取公开数据集并进行训练，框架为Pytorch(Training a deep learning circular neural network can achieve the acquisition and training of open data sets, and the framework is Pytorch)

本书提供如下资源：  • 本书源代码； • 书中习题答案。 要获得以上配套资源。注意：为保证购书读者的权益，该操作会给出相关提示，要求输 入提取码进行验证。 如果您是教师，希望获得教学配套资源，请在社区本书页面中直接联系本书的责任编辑。  

说明：  卷积神经网络和循环神经网络被联合起来解决文本分类的问题(Convolutional neural network and cyclic neural network are combined to solve the problem of text classification)

说明：  基于hopfield神经网络的数字识别Python程序，本人对现有的程序进行了扩充修改，只需要添加训练样本图片就可以增加训练样本（注意样本像素要一致），这是我的课程设计，只做了数字0-5的识别，如有需要，稍微修改程序即可实现0-9的识别。(Based on the number recognition Python program of Hopfield neural network, I have expanded and modified the existing program. I only need to add the training sample image to increase the training sample (note that the sample pixels should be consistent). This is my curriculum design, only do the number 0-5 recognition, if necessary, slightly modify the program to achieve 0-9 recognition.)

用python进行forensic，经典书籍，黑客必备！！！！！！！！(Master the art of digital forensics and analysis with Python)