使用sklearn中鸢尾花数据集手写knn算法，得出数据结论

好的，我们可以使用sklearn中的鸢尾花数据集，手动实现KNN算法，并得出数据结论。下面是一个示例代码：

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

# 定义KNN算法类
class KNN:
    def __init__(self, n_neighbors=3):
        self.n_neighbors = n_neighbors

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

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

    def predict(self, X_test):
        y_pred = []
        for test_sample in X_test:
            distances = [self.euclidean_distance(test_sample, x) for x in self.X]
            nearest_indices = np.argsort(distances)[:self.n_neighbors]
            nearest_labels = self.y[nearest_indices]
            unique_labels, counts = np.unique(nearest_labels, return_counts=True)
            predicted_label = unique_labels[np.argmax(counts)]
            y_pred.append(predicted_label)
        return np.array(y_pred)

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

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 实例化KNN算法类
knn = KNN(n_neighbors=3)

# 拟合模型
knn.fit(X_train, y_train)

# 预测测试集
y_pred = knn.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("准确率:", accuracy)

这段代码会将鸢尾花数据集划分为训练集和测试集，然后使用手动实现的KNN算法进行训练和预测。最后，计算预测结果与真实标签之间的准确率。

希望这对你有帮助！