基于KNN算法的Iris数据集分类的代码实现

您好！以下是基于KNN算法的Iris数据集分类的代码实现：

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

def euclidean_distance(a, b):
    return np.linalg.norm(a - b)

class KNNClassifier:
    def __init__(self, k=5):
        self.k = k
        self.X_train = None
        self.y_train = None

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

    def predict(self, X_test):
        y_pred = []
        for i in range(X_test.shape[0]):
            distance = []
            for j in range(self.X_train.shape[0]):
                dist = euclidean_distance(X_test[i], self.X_train[j])
                distance.append((dist, self.y_train[j]))
            distance.sort()
            top_k = [x[1] for x in distance[:self.k]]
            counts = np.bincount(top_k)
            y_pred.append(np.argmax(counts))
        return y_pred

iris = load_iris()
X, y = shuffle(iris.data, iris.target, random_state=13)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=13)

knn = KNNClassifier()
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

希望对您有所帮助！如果您有其它问题，可以继续提问。祝您生活愉快！