knn算法对鸢尾花进行分类，用欧氏距离计算的python代码

好的，以下是KNN算法对鸢尾花进行分类，使用欧氏距离计算的Python代码：

首先，导入必要的库：

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

接着，载入鸢尾花数据集：

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, random_state=42)

定义欧氏距离计算函数：

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

定义KNN分类器：

class KNN:
    def __init__(self, k=3):
        self.k = k

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

    def predict(self, X):
        y_pred = [self._predict(x) for x in X]
        return np.array(y_pred)

    def _predict(self, x):
        distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
        k_indices = np.argsort(distances)[:self.k]
        k_nearest_labels = [self.y_train[i] for i in k_indices]
        most_common = Counter(k_nearest_labels).most_common(1)
        return most_common[0][0]

最后，训练模型并进行预测：

knn = KNN(k=3)
knn.fit(X_train, y_train)
predictions = knn.predict(X_test)

完整代码如下：

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

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

class KNN:
    def __init__(self, k=3):
        self.k = k

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

    def predict(self, X):
        y_pred = [self._predict(x) for x in X]
        return np.array(y_pred)

    def _predict(self, x):
        distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
        k_indices = np.argsort(distances)[:self.k]
        k_nearest_labels = [self.y_train[i] for i in k_indices]
        most_common = Counter(k_nearest_labels).most_common(1)
        return most_common[0][0]

iris = load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

knn = KNN(k=3)
knn.fit(X_train, y_train)

predictions = knn.predict(X_test)
print(predictions)

希望对你有所帮助！