python实现knn算法鸢尾花源代码

以下是使用Python实现knn算法对鸢尾花进行分类的代码：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 定义knn算法函数
def knn(X_train, y_train, X_test, k):
    distances = []
    targets = []

    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)

    for i in range(k):
        index = distances[i][1]
        targets.append(y_train[index])

    return np.bincount(targets).argmax()

# 对测试集进行预测
predictions = []

for i in range(len(X_test)):
    predictions.append(knn(X_train, y_train, X_test[i, :], 3))

# 计算预测准确率
accuracy = np.sum(np.array(predictions) == y_test) / len(y_test)

print("预测结果：", predictions)
print("真实结果：", y_test)
print("预测准确率：", accuracy)

输出结果：

预测结果： [2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 2, 2, 2, 2, 0, 2, 2, 0, 0, 2]
真实结果： [2 0 2 0 0 1 2 2 2 0 1 1 1 0 0 1 1 1 0 0 2 2 2 2 0 2 2 0 0 2]
预测准确率： 0.9333333333333333