中国mooc北京理工大学机器学习第二周（一）：分类

一、K近邻方法（KNeighborsClassifier）

使用方法同kmeans方法，先构造分类器，再进行拟合。区别是Kmeans聚类是无监督学习，KNN是监督学习，因此需要划分出训练集和测试集。

直接贴代码。

X=[0,1,2,3]#样本
Y=[0,0,1,1]#标签
from  sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)#选择周围的三个点作为近邻分析
neigh.fit(X,Y)
neigh.predict(1.1)

K-NN可以看成：有那么一堆你已经知道分类的数据，然后当一个新数据进入的时候，就开始跟训练数据里的每个点求距离，然后挑离这个训练数据最近的K个点看看这几个点属于什么类型，然后用少数服从多数的原则，给新数据归类。

2、决策树（DessionTreeClassifer）

from sklearn.datasets import load_iris
form sklearn.tree import DessionTreeClassifier
from sklearn.model_selection import cross_val_score#交叉验证
clf = DessionTreeClassifier()#默认参数构造的话，基于基尼系数
iris = iris_load()
cross_val_score(clf,iris.data,iris.target,cv=10)#cv=10代表10折验证

from sklearn import tree
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)

clf.predict([[1.2, 1.2]])

三、朴素贝叶斯（naive_bayes.GaussianNB）

对于给定的数据，首先基于特征的条件独立性假设，学习输入输出的联合分布概率，然后基于此模型对给定的输入x，应用贝叶斯定力，测试其后验概率。

在sklearn中实现了高斯朴素贝叶斯，多项式朴素贝叶斯，多元伯努利朴素贝叶斯。

import numpy as np
from sklearn.naive_bayes import GaussianNB
X=np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]])
Y=np.array([1,1,1,2,2,2])
clf=GaussianNB()
clf.fit(X,Y)
print(clf.predict([[-0.8,-1]]))

三、人体运动状态信息评级

    import pandas as pd
    import numpy as np  

    from sklearn.preprocessing import Imputer
    from sklearn.cross_validation import train_test_split
    from sklearn.metrics import classification_report

    from sklearn.neighbors import KNeighborsClassifier#K近邻
    from sklearn.tree import DecisionTreeClassifier#决策树
    from sklearn.naive_bayes import GaussianNB#朴素贝叶斯

    def load_datasets(feature_paths, label_paths):
        feature = np.ndarray(shape=(0,41))
        label = np.ndarray(shape=(0,1))
        for file in feature_paths:
            df = pd.read_table(file, delimiter=‘,‘, na_values=‘?‘, header=None)
            imp = Imputer(missing_values=‘NaN‘, strategy=‘mean‘, axis=0)
            imp.fit(df)
            df = imp.transform(df)
            feature = np.concatenate((feature, df))

        for file in label_paths:
            df = pd.read_table(file, header=None)
            label = np.concatenate((label, df))

        label = np.ravel(label)
        return feature, label

    if __name__ == ‘__main__‘:
        ‘‘‘ 数据路径 ‘‘‘
        featurePaths = [‘A.feature‘,‘B.feature‘,‘C.feature‘,‘D.feature‘,‘E.feature‘]
        labelPaths = [‘A.label‘,‘B.label‘,‘C.label‘,‘D.label‘,‘E.label‘]
        ‘‘‘ 读入数据  ‘‘‘
        x_train,y_train = load_datasets(featurePaths[:4],labelPaths[:4])
        x_test,y_test = load_datasets(featurePaths[4:],labelPaths[4:])
        x_train, x_, y_train, y_ = train_test_split(x_train, y_train, test_size = 0.0)

        print(‘Start training knn‘)
        knn = KNeighborsClassifier().fit(x_train, y_train)
        print(‘Training done‘)
        answer_knn = knn.predict(x_test)
        print(‘Prediction done‘)

        print(‘Start training DT‘)
        dt = DecisionTreeClassifier().fit(x_train, y_train)
        print(‘Training done‘)
        answer_dt = dt.predict(x_test)
        print(‘Prediction done‘)

        print(‘Start training Bayes‘)
        gnb = GaussianNB().fit(x_train, y_train)
        print(‘Training done‘)
        answer_gnb = gnb.predict(x_test)
        print(‘Prediction done‘)

        print(‘\n\nThe classification report for knn:‘)
        print(classification_report(y_test, answer_knn))
        print(‘\n\nThe classification report for DT:‘)
        print(classification_report(y_test, answer_dt))
        print(‘\n\nThe classification report for Bayes:‘)
        print(classification_report(y_test, answer_gnb))

四、支持向量机（SVM）

    from sklearn import svm  

    X = [[0, 0], [1, 1], [1, 0]]  # training samples
    y = [0, 1, 1]  # training target
    clf = svm.SVC()  # class
    clf.fit(X, y)  # training the svc model  

    result = clf.predict([[2, 2]]) # predict the target of testing samples
    print(result)  # target   

    print(clf.support_vectors_)  #support vectors  

    print(clf.support_)  # indeices of support vectors  

    print(clf.n_support_)  # number of support vectors for each class 

以上。

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