利用iris数据集进行原理代码的验证并且可视化显示

以下是利用iris数据集进行原理代码的验证并且可视化显示的示例代码：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

# 加载iris数据集
iris = load_iris()
X = iris.data
y = iris.target

# 将数据集划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=0)

# 定义KNN分类器
class KNN_classifier():
    def __init__(self, k=3):
        self.k = k
    
    def euclidean_distance(self, x1, x2):
        return np.sqrt(np.sum((x1-x2)**2))
    
    def predict(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((dist, y_train[i]))
        
        distances = sorted(distances)
        topk = distances[:self.k]
        
        labels = [i[1] for i in topk]
        prediction = max(set(labels), key=labels.count)
        
        return prediction

# 训练模型
knn = KNN_classifier(k=3)
y_pred = []
for i in range(len(X_test)):
    y_pred.append(knn.predict(X_train, y_train, X_test[i]))

# 计算模型准确率
from sklearn.metrics import accuracy_score

accuracy = accuracy_score(y_test, y_pred)
print("模型准确率：", accuracy)

# 可视化结果
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111)

ax.scatter(X_test[:, 0], X_test[:, 1], c=y_pred, cmap='viridis')
ax.set_xlabel('Sepal length')
ax.set_ylabel('Sepal width')
ax.set_title('KNN分类器结果')

plt.show()

运行以上代码可以得到如下的可视化结果：


从图中可以看出，在Sepal length和Sepal width这两个特征下，KNN分类器对iris数据集进行了分类，不同颜色的点代表不同的类别。