my_ML

a machine learning package based on golang

这是一个基于golang的机器学习库和一些机器学习的数据集,可作为学习的辅助代码,也可以作为小型项目的demo,对于结果的敛散性没有做特殊设计,可根据具体问题具体考虑

基础的工具包:
read_excel: 读取特定格式的excel数据,返回float64或者string
test_data:用于测试算法的微型数据集,内含目录
entropy: 用于计算一些有关熵的内容
math_tool: 一些常用的数学工具(nn全排列,nk全排列,多元积分数值求解)

基础的机器学习算法:
liner_regression:线性回归(包含岭回归)
perception:感知机
logistic_regression: 逻辑回归
naive_bayes: 朴素贝叶斯
gda: 高斯判别分析
pca: 主成分分析
smo: 序列最小优化算法
svm: 支持向量机
knn: k近邻算法
k_means: k平均聚类算法
decision_tree: 决策树(ide3、c4.5)

后续更新会加入集成算法和最新的深度学习代码套件,有问题可以加vx交流:13997171940

This is a machine learning library based on golang and some machine learning data sets. It can be used as auxiliary code for learning or demo of small projects. There is no special design for the convergence and divergence of results, which can be considered according to specific problems

Basic Toolkit:

read_ Excel: read Excel data in a specific format and return float64 or string

test_ Data: a micro data set used to test the algorithm, including a directory

Entropy: used to calculate some information about entropy

math_ Tool: some commonly used mathematical tools (NN Full Permutation, NK Full Permutation, multivariate integral numerical solution)

Fundamentals of machine learning algorithms:

liner_ Region: linear regression (including ridge regression)
Perception: perceptron
logistic_ Logistic regression
naive_ Bayes: Naive Bayes
GDA: Gaussian discriminant analysis
PCA: principal component analysis
Smo: sequence minimum optimization algorithm
SVM: support vector machine
KNN: k-nearest neighbor algorithm
k_ Means: K-means clustering algorithm
decision_ Tree: decision tree (ide3, C4.5)

The following updates will add the integration algorithm and the latest deep learning code suite. If there is a problem, you can add VX communication: 13997171940

GitHub

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