knn的python代码

以下是一个简单的KNN算法的Python代码：

import numpy as np
from collections import Counter

def knn(X_train, y_train, X_test, k):
    distances = []
    for i in range(len(X_train)):
        distance = np.sqrt(np.sum(np.square(X_test - X_train[i, :])))
        distances.append([distance, i])
    distances = sorted(distances)
    targets = []
    for i in range(k):
        index = distances[i][1]
        targets.append(y_train[index])
    return Counter(targets).most_common(1)[0][0]

其中，X_train是训练集的特征矩阵，y_train是训练集的标签，X_test是测试集的特征矩阵，k是KNN算法中的k值。

相关问题

knn Python代码实现

当然可以！下面是一个简单的knn Python代码实现：

import numpy as np
from collections import Counter

def euclidean_distance(x1, x2):
    return np.sqrt(np.sum((x1 - x2)**2))

class KNN:
    def __init__(self, k=3):
        self.k = k

    def fit(self, X, y):
        self.X_train = X
        self.y_train = y

    def predict(self, X):
        predicted_labels = [self._predict(x) for x in X]
        return np.array(predicted_labels)

    def _predict(self, x):
        distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
        k_indices = np.argsort(distances)[:self.k]
        k_nearest_labels = [self.y_train[i] for i in k_indices]
        most_common = Counter(k_nearest_labels).most_common(1)
        return most_common[0][0]

这个代码实现了一个简单的KNN分类器，其中欧式距离被用于计算相似度。使用方法如下：

X_train = np.array([[1,2], [2,4], [3,2], [3,4]])
y_train = np.array([0, 0, 1, 1])

knn = KNN(k=3)
knn.fit(X_train, y_train)

X_test = np.array([[1,3], [2,1]])
predictions = knn.predict(X_test)

print(predictions)

输出应该是一个包含两个数字的数组，代表对应的测试样本的预测标签。

sklearn knn python代码

以下是使用sklearn库实现KNN算法的Python代码示例：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# 导入数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 切分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

# 创建KNN分类器对象
knn = KNeighborsClassifier(n_neighbors=3)

# 拟合训练集数据
knn.fit(X_train, y_train)

# 预测测试集数据
y_pred = knn.predict(X_test)

# 打印预测结果
print("预测结果：", y_pred)

请注意，这只是一个简单的示例代码，实际使用时可能需要根据具体情况进行适当的调整和优化。