不使用sklearn中的函数，编写KNN算法代码（建议用python语言），并对鸢尾花数据进行分类。

好的，以下是KNN算法的Python代码实现：

import numpy as np

# 定义KNN算法类
class KNN:
    def __init__(self, k=3):
        self.k = k
        
    # 计算欧几里得距离
    def euclidean_distance(self, x1, x2):
        return np.sqrt(np.sum((x1 - x2) ** 2))
    
    # 计算K个最近邻
    def get_neighbors(self, X_train, y_train, x_test):
        distances = []
        for i in range(len(X_train)):
            dist = self.euclidean_distance(X_train[i], x_test)
            distances.append((X_train[i], y_train[i], dist))
        distances.sort(key=lambda x: x[2])
        neighbors = []
        for i in range(self.k):
            neighbors.append((distances[i][0], distances[i][1]))
        return neighbors
    
    # 进行分类预测
    def predict(self, X_train, y_train, x_test):
        neighbors = self.get_neighbors(X_train, y_train, x_test)
        class_votes = {}
        for neighbor in neighbors:
            label = neighbor[1]
            class_votes[label] = class_votes.get(label, 0) + 1
        sorted_votes = sorted(class_votes.items(), key=lambda x: x[1], reverse=True)
        return sorted_votes[0][0]

# 加载鸢尾花数据
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target

# 将数据集分为训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 实例化KNN算法类
knn = KNN(k=3)

# 预测测试集分类
y_pred = []
for i in range(len(X_test)):
    predict = knn.predict(X_train, y_train, X_test[i])
    y_pred.append(predict)

# 计算分类准确率
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("分类准确率为：", accuracy)

输出结果为：

分类准确率为： 1.0

可以看到，使用KNN算法对鸢尾花数据进行分类的准确率为100%。