差分进化算法优化灰色模型

数据分析(Python language learning data analysis)

说明：  python 通过opencv实现的OCR算法(python enable the OCR by OpenCV)

说明：  python编写，使用sklearn机器学习库里的svm分类器，以MNIST为数据集，手动输入数字分类，MNIST.py为训练代码，训练10000次准确率为93.93%，手动3.py为手动输入并分类的代码。(Written in Python, using the svm classifier in the sklearn machine learning library, using MNIST as the data set, manually input the numeric classification, MNIST.py is the training code, the accuracy of training 10000 times is 93.93%, manual 3.py is manually entered and classified Code.)

说明：  运用拉格朗日插值法给空缺数据进行插值，通过调用scipy中的lagrange实现(The Lagrange interpolation method is used to interpolate the vacant data, which is realized by calling the Lagrange in SciPy)

利用GPflow进行高斯过程回归，kernel有OU过程核等(Gauss Process Regression Using GPflow)

以图像处理和knn算法为基础，利用knn算法，在python开发环境中设计了一个手写数字识别系统，该系统采用28*28的模板对图新分成784块提取手写数字的784个特征，在二值图像数据基础上，通过朴素贝叶斯分类器算法，对0~9这十类手写数字进行学习和分类。(Based on image processing and KNN algorithm, a handwritten digit recognition system is designed in Python development environment by using KNN algorithm. The system uses 28*28 template to divide the graph into 784 blocks to extract 784 features of handwritten digits. On the basis of binary image data, it learns and classifies the ten types of handwritten digits, 0-9, through Naive Bayesian classifier algorithm.)

shows test signal TestDAC1 on pin AUX1 and TestDAC2 on pin AUX2 when the TestDAC1Reg register is programmed with a slope defined by values 00h to 3Fh and the TestDAC2Reg register is programmed with a rectangular signal defined by values 00h and 3Fh.

AP聚类算法用python实现的代码。代码可靠，可以直接运行。代码使用python3写的。(The AP clustering algorithm uses python to implement the code. The code is reliable and can be run directly. The code is written in python3.)

None Coding not anything to do

根据训练的数据来对新图片进行识别的过程