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‎blog/ml/10.k-means聚类.md

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```
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#### 测试函数
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1. 测试一下以上的基础函数是否可以如预期运行, 请看: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/10.kmeans/kMeans.py>
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2. 测试一下 kMeans 函数是否可以如预期运行, 请看: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/10.kmeans/kMeans.py>
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1. 测试一下以上的基础函数是否可以如预期运行, 请看: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/10.kmeans/kMeans.py>
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2. 测试一下 kMeans 函数是否可以如预期运行, 请看: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/10.kmeans/kMeans.py>
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参考运行结果如下:
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![K-Means 运行结果1](/img/ml/10.KMeans/apachecn-k-means-run-result-1.jpg)
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#### 测试二分 KMeans 聚类算法
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* 测试一下二分 KMeans 聚类算法，请看: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/10.kmeans/kMeans.py>
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* 测试一下二分 KMeans 聚类算法，请看: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/10.kmeans/kMeans.py>
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上述函数可以运行多次，聚类会收敛到全局最小值，而原始的 kMeans() 函数偶尔会陷入局部最小值。
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运行参考结果如下:

‎blog/ml/11.使用Apriori算法进行关联分析.md

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完整代码地址: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/11.Apriori/apriori.py>
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完整代码地址: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/11.Apriori/apriori.py>
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### 组织完整的 Apriori 算法
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到这一步，我们就找出我们所需要的 `频繁项集` 和他们的 `支持度` 了，接下来再找出关联规则即可！
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完整代码地址: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/11.Apriori/apriori.py>
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完整代码地址: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/11.Apriori/apriori.py>
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## 从频繁项集中挖掘关联规则
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‎blog/ml/12.使用FP-growth算法来高效发现频繁项集.md

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## FP-growth 代码讲解
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完整代码地址: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/12.FrequentPattemTree/fpGrowth.py>
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完整代码地址: <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/12.FrequentPattemTree/fpGrowth.py>
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main 方法大致步骤:
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```python

‎blog/ml/13.利用PCA来简化数据.md

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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/13.PCA/pca.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/13.PCA/pca.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/13.PCA/pca.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/13.PCA/pca.py>
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### 要点补充
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‎blog/ml/14.利用SVD简化数据.md

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> 测试 和 项目调用，可直接参考我们的代码
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/14.SVD/svdRecommend.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/14.SVD/svdRecommend.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/14.SVD/svdRecommend.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/14.SVD/svdRecommend.py>
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#### 要点补充
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/14.SVD/svdRecommend.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/14.SVD/svdRecommend.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/14.SVD/svdRecommend.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/14.SVD/svdRecommend.py>
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‎blog/ml/15.大数据与MapReduce.md

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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/15.BigData_MapReduce/pegasos.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/15.BigData_MapReduce/pegasos.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/15.BigData_MapReduce/pegasos.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/15.BigData_MapReduce/pegasos.py>
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运行方式：`python /opt/git/MachineLearning/src/python/15.BigData_MapReduce/mrSVM.py < input/15.BigData_MapReduce/inputFile.txt`
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[MR版本的代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/15.BigData_MapReduce/mrSVM.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/15.BigData_MapReduce/mrSVM.py>
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[MR版本的代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/15.BigData_MapReduce/mrSVM.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/15.BigData_MapReduce/mrSVM.py>
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‎blog/ml/2.k-近邻算法.md

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### 项目案例1: 优化约会网站的配对效果
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[完整代码地址](../src/python/2.KNN/kNN.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/2.KNN/kNN.py>
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[完整代码地址](/src/py2.x/ml/2.KNN/kNN.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/2.KNN/kNN.py>
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#### 项目概述
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### 项目案例2: 手写数字识别系统
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[完整代码地址](../src/python/2.KNN/kNN.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/2.KNN/kNN.py>
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[完整代码地址](/src/py2.x/ml/2.KNN/kNN.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/2.KNN/kNN.py>
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#### 项目概述
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‎blog/ml/3.决策树.md

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#### 开发流程
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[完整代码地址](../src/python/3.DecisionTree/DecisionTree.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/3.DecisionTree/DecisionTree.py>
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[完整代码地址](/src/py2.x/ml/3.DecisionTree/DecisionTree.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/3.DecisionTree/DecisionTree.py>
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```
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[完整代码地址](../src/python/3.DecisionTree/DecisionTree.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/3.DecisionTree/DecisionTree.py>
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[完整代码地址](/src/py2.x/ml/3.DecisionTree/DecisionTree.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/3.DecisionTree/DecisionTree.py>
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#### 项目概述
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‎blog/ml/4.朴素贝叶斯.md

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### 项目案例1: 屏蔽社区留言板的侮辱性言论
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[完整代码地址](../src/python/4.NaiveBayes/bayes.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/4.NaiveBayes/bayes.py>
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[完整代码地址](/src/py2.x/ml/4.NaiveBayes/bayes.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/4.NaiveBayes/bayes.py>
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[完整代码地址](../src/python/4.NaiveBayes/bayes.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/4.NaiveBayes/bayes.py>
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[完整代码地址](/src/py2.x/ml/4.NaiveBayes/bayes.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/4.NaiveBayes/bayes.py>
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[完整代码地址](../src/python/4.NaiveBayes/bayes.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/4.NaiveBayes/bayes.py>
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[完整代码地址](/src/py2.x/ml/4.NaiveBayes/bayes.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/4.NaiveBayes/bayes.py>
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‎blog/ml/5.Logistic回归.md

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[完整代码地址](../src/python/5.Logistic/logistic.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/5.Logistic/logistic.py>
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[完整代码地址](/src/py2.x/ml/5.Logistic/logistic.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/5.Logistic/logistic.py>
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[完整代码地址](../src/python/5.Logistic/logistic.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/5.Logistic/logistic.py>
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[完整代码地址](/src/py2.x/ml/5.Logistic/logistic.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/5.Logistic/logistic.py>
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‎blog/ml/6.支持向量机.md

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[完整代码地址：SVM简化版，应用简化版SMO算法处理小规模数据集](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/6.SVM/svm-simple.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/6.SVM/svm-simple.py>
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[完整代码地址：SVM简化版，应用简化版SMO算法处理小规模数据集](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/6.SVM/svm-simple.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/6.SVM/svm-simple.py>
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[完整代码地址：SVM完整版，使用完整 Platt SMO算法加速优化，优化点：选择alpha的方式不同](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/6.SVM/svm-complete_Non-Kernel.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/6.SVM/svm-complete_Non-Kernel.py>
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[完整代码地址：SVM完整版，使用完整 Platt SMO算法加速优化，优化点：选择alpha的方式不同](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/6.SVM/svm-complete_Non-Kernel.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/6.SVM/svm-complete_Non-Kernel.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/6.SVM/svm-complete.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/6.SVM/svm-complete.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/6.SVM/svm-complete.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/6.SVM/svm-complete.py>
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‎blog/ml/7.集成方法-随机森林和AdaBoost.md

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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/7.RandomForest/randomForest.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/7.RandomForest/randomForest.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/7.RandomForest/randomForest.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/7.RandomForest/randomForest.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/7.AdaBoost/adaboost.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/7.AdaBoost/adaboost.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/7.AdaBoost/adaboost.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/7.AdaBoost/adaboost.py>
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‎blog/ml/8.回归.md

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[完整代码地址](../src/python/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/8.Regression/regression.py>
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[完整代码地址](/src/py2.x/ml/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/8.Regression/regression.py>
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[完整代码地址](../src/python/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/8.Regression/regression.py>
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[完整代码地址](/src/py2.x/ml/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/8.Regression/regression.py>
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[完整代码地址](../src/python/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/8.Regression/regression.py>
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[完整代码地址](/src/py2.x/ml/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/8.Regression/regression.py>
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到此为止，我们已经介绍了找出最佳拟合直线的两种方法，下面我们用这些技术来预测鲍鱼的年龄。
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[完整代码地址](../src/python/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/8.Regression/regression.py>
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[完整代码地址](/src/py2.x/ml/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/8.Regression/regression.py>
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[完整代码地址](../src/python/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/8.Regression/regression.py>
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[完整代码地址](/src/py2.x/ml/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/8.Regression/regression.py>
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```python
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def stageWise(xArr,yArr,eps=0.01,numIt=100):
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#### 项目案例1: 预测乐高玩具套装的价格
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[完整代码地址](../src/python/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/8.Regression/regression.py>
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[完整代码地址](/src/py2.x/ml/8.Regression/regression.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/8.Regression/regression.py>
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##### 项目概述
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‎blog/ml/9.树回归.md

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retTree['right'] = createTree(rSet, leafType, errType, ops)
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return retTree
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```
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/regTrees.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/regTrees.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/regTrees.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/regTrees.py>
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> 测试算法：使用测试数据上的R^2值来分析模型的效果
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else:
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return tree
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```
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/regTrees.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/regTrees.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/regTrees.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/regTrees.py>
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## 3、模型树
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ws = xTx.I * (X.T * Y)
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return ws, X, Y
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```
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/regTrees.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/regTrees.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/regTrees.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/regTrees.py>
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### 3.3、模型树 运行结果
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return yHat
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```
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/regTrees.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/regTrees.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/regTrees.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/regTrees.py>
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> 测试算法：使用测试数据上的R^2值来分析模型的效果
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![取得更好预测效果的GUI示例图](/img/ml/9.TreeRegression/GUI更好的示例图.png "取得更好预测效果的GUI示例图")
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/treeExplore.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ML/9.RegTrees/treeExplore.py>
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[完整代码地址](https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/treeExplore.py): <https://github.com/apachecn/AiLearning/blob/master/src/py2.x/ml/9.RegTrees/treeExplore.py>
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## 6、树回归 小结

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