常见的机器学习算法（四） K最邻近类别

谁践踏了优雅 2023-02-12 05:50 29阅读 0赞

import numpy as np
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt
    from sklearn.datasets import load_digits#数字数据集
    
    np.random.seed(123)
    digits = load_digits()
    x, y = digits.data, digits.target
    x_train, x_test, y_train, y_test = train_test_split(x, y)
    print('Shape of x_train: ', x_train.shape)
    print('Shape of y_train: ', y_train.shape)
    print('Shape of x_test: ', x_test.shape)
    print('Shape of y_test: ', y_test.shape)
    
    fig = plt.figure(figsize=(10,8))
    for i in range(10):
        ax = fig.add_subplot(2, 5, i+1)#大小为2*5的第i+1个子图
        plt.imshow(x[i].reshape((8,8)), cmap='gray')
        plt.show()
    
    class KNN:
        def __init__(self):
            pass
    
        def fit(self,x,y):
            self.data = x
            self.targets = y
    
        def euclidean_distance(self,x):#欧式距离
            """
            Computes the euclidean distance between the training data and
            a new input example or matrix of input examples X
            """
            if x.ndim == 1:#数字
                a = np.sqrt(np.sum((self.data - x)**2, axis = 1))
            if x.ndim == 2:#数组
                n_samples, _ = x.shape#n_samples样本行数
                a = [np.sqrt(np.sum((self.data - x[i])**2, axis = 1))for i in range(n_samples)]
            return np.array(a)
    
        def predict(self,x,k=1):
            # step 1: compute distance between input and training data
            dists = self.euclidean_distance(x)
    
            # step 2: find the k nearest neighbors and their classifications
            if x.ndim == 1:#数字
                if k == 1:
                    nn = np.argmin(digits)#最小值在数组中的位置
                    return self.targets[nn]
                else:
                    knn = np.argsort(digits)[:k]#np.argsort()函数将数组的值从小到大排序后，并按照其相对应的索引值输出
                    y_knn = self.targets[knn]
                    max_vote = max(y_knn, key=list(y_knn).count)
                    return max_vote
    
            if x.ndim == 2:#数组
                knn = np.argsort(dists)[:, :k]
                y_knn = self.targets[knn]
                if k == 1:
                    return y_knn.T
                else:
                    n_samples, _ = x.shape
                    max_vote = [max(y_knn[i], key=list(y_knn[i]).count) for i in range(n_samples)]
                    return max_vote
    
    '初始化并训练'
    knn = KNN()
    knn.fit(x_train, y_train)
    print('Testing one datapoint, k=1')
    print('Predicted label:', knn.predict(x_test[0], k=1))
    print('True label: ', y_test[0])
    print()
    print('Testing one datapoint, k=5')
    print('Predicted label:', knn.predict(x_test[20], k=5))
    print('True label: ', y_test[20])
    print()
    print('Testing 10 datapoint, k=1')
    print('Predicted label:', knn.predict(x_test[5:15], k=1))
    print('True label: ', y_test[5:15])
    print()
    print('Testing 10 datapoint, k=4')
    print('Predicted label:', knn.predict(x_test[5:15], k=4))
    print('True label: ', y_test[5:15])
    print()
    
    '测试'
    y_p_test1 = knn.predict(x_test, k=1)
    test_acc1 = np.sum(y_p_test1[0] == y_test) / len(y_p_test1[0]) * 100
    print('Test accuracy with k=1: ', test_acc1)
    
    y_p_test8 = knn.predict(x_test, k=8)
    test_acc8 = np.sum(y_p_test8 == y_test) / len(y_p_test8) * 100
    print('Test accuracy with k=8: ', test_acc8)

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（二）直接调用sklearn的API

from sklearn.neighbors import KNeighborsClassifier     #K近邻#
    from sklearn.neighbors import KNeighborsRegressor
    module = KNeighborsClassifier(n_neighbors=6)
    module.fit(x, y)
    predicted = module.predict(test)
    predicted = module.predict_proba(test)

完整代码;

import numpy as np
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt
    from sklearn.datasets import make_blobs
    from sklearn.neighbors import KNeighborsClassifier
     
    np.random.seed(123)
    x, y = make_blobs(n_samples=1000, centers=2)
     
    '数据集'
    fig = plt.figure(figsize=(8, 6))
    plt.scatter(x[:, 0], x[:, 1], c=y)
    plt.title('Dataset')
    plt.xlabel('First feature')
    plt.ylabel('Second feature')
    plt.show()
     
    y_true = y[:, np.newaxis]
    x_train, x_test, y_train, y_test = train_test_split(x, y_true)
    print('Shape of x_train: ', x_train.shape)
    print('Shape of y_train: ', y_train.shape)
    print('Shape of x_test: ', x_test.shape)
    print('Shape of y_test: ', y_test.shape)
     
    module = KNeighborsClassifier()
    module.fit(x_test, y_test)
     
    '测试'
    y_p_train = module.predict(x_train)
    y_p_test = module.predict(x_test)
    print('Train accuracy: ',
          (100 - np.mean(np.abs(y_p_train - y_train))), '%')
    print('Test accuracy: ',
          (100 - np.mean(np.abs(y_p_test - y_test))), '%')

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5OTM4NjY2_size_16_color_FFFFFF_t_70 1][]

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5OTM4NjY2_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200525163138194.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5OTM4NjY2,size_16,color_FFFFFF,t_70