a machine learning package based on golang


read_excel: 读取特定格式的excel数据,返回float64或者string
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)

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)


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


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