NumPy 使用方法
所有的算法都重载了
- fit(train_x, train_y), predict(test_x)接口,使用方法类似sklearn。
- 例如:
- from mlearn.knn import KNeighborClassifierimport numpy as npclf = KNeighborClassifier(n_neighbors=3)train_x = np.array([[1, 1], [0.1, 0.1], [0.5, 0.7], [10, 10], [10, 11]])train_y = np.array(['A', 'A', 'A', 'B', 'B'])test_x = np.array([[11, 12], [12, 13], [11, 13], [0.05, 0.1]])clf.fit(train_x, train_y)print(clf.predict(test_x))# 输出['B' 'B' 'B' 'A']
- from mlearn.naive_bayes import NaiveBayesClassifiertrain_x = [["1", "S"], ["1", "M"], ["1", "M"], ["1", "S"], ["1", "S"], ["2", "S"], ["2", "M"], ["2", "M"], ["2", "L"], ["2", "L"], ["3", "L"], ["3", "M"], ["3", "M"], ["3", "L"], ["3", "L"]]train_y = ["-1", "-1", "1", "1", "-1", "-1", "-1", "1", "1", "1", "1", "1", "1", "1", "-1"]clf = NaiveBayesClassifier()clf.fit(train_x, train_y)print(clf.predict([["2", "S"]]))# 输出['-1']
- 内容
- K近邻算法
- 感知机
- 朴素贝叶斯分类器
- 决策树(Decision Tree)
- 随机森林(Random Forests)
- 支持向量机(Support Vector Machine)
- 线性回归(Linear Regression)
- 逻辑斯蒂回归(Logistic Regression)
- Bagging算法
- 神经网络(BP算法)
- 隐马尔科夫模型(HMM)
- K-Means聚类算法
- LVQ聚类算法
- 主成份分析法(PCA)
- 说明
- 本项目中的算法是作者在学习机器学习的过程中所做的练习,其中不免有些错误,如果有发现的错误,欢迎批评指正。
来源: http://www.92to.com/bangong/2017/02-27/17760236.html