莺尾花的分类问题KNN（K-近邻）分类算法代码，python

以下是莺尾花分类问题的KNN分类算法Python代码示例：

from sklearn.datasets import load_iris  # 导入莺尾花数据集
from sklearn.model_selection import train_test_split  # 划分数据集
from sklearn.neighbors import KNeighborsClassifier  # KNN分类器

# 加载莺尾花数据集
iris = load_iris()

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

# 创建KNN分类器，K值为3
clf = KNeighborsClassifier(n_neighbors=3)

# 训练模型
clf.fit(X_train, y_train)

# 预测测试集的类别
y_pred = clf.predict(X_test)

# 计算模型的准确率
accuracy = clf.score(X_test, y_test)
print("模型的准确率为：{:.2f}%".format(accuracy * 100))

在以上代码中，首先导入莺尾花数据集并划分训练集和测试集。然后创建KNN分类器，K值为3，使用训练集来训练模型。接着，使用测试集来预测新莺尾花的类别，并计算模型的准确率。最后输出模型的准确率。

相关问题

DPC-KNN-PCA算法python完整代码

以下是DPC-KNN-PCA算法的Python完整代码，包括数据预处理、DPC-KNN-PCA算法实现和结果可视化：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 数据预处理
data = pd.read_csv('data.csv')
X = data.values[:, :-1]
y = data.values[:, -1]

# DPC-KNN-PCA算法实现
def DPC_KNN_PCA(X, k, alpha, beta, gamma):
    n, m = X.shape
    D = np.zeros((n, n))
    for i in range(n):
        for j in range(n):
            D[i, j] = np.linalg.norm(X[i] - X[j])
    D_sort = np.sort(D, axis=1)
    idx_sort = np.argsort(D, axis=1)
    K = np.zeros((n, k))
    for i in range(n):
        for j in range(k):
            K[i, j] = idx_sort[i, j+1]
    W = np.zeros((n, n))
    for i in range(n):
        for j in range(k):
            W[int(K[i, j]), i] = 1
    W = np.maximum(W, W.T)
    D_bar = np.diag(np.sum(W, axis=1))
    L = D_bar - W
    M = np.dot(X.T, L).dot(X)
    [U, S, V] = np.linalg.svd(M)
    U_pca = U[:, :2]
    Z = np.dot(X, U_pca)
    L_pca = np.dot(U_pca.T, M).dot(U_pca)
    D_pca = np.diag(np.sum(L_pca, axis=1))
    L_norm = np.linalg.inv(np.sqrt(D_pca)).dot(L_pca).dot(np.linalg.inv(np.sqrt(D_pca)))
    W_norm = np.exp(-alpha*L_norm) - np.eye(n)
    D_norm = np.diag(np.sum(W_norm, axis=1))
    L1_norm = D_norm - W_norm
    L2_norm = np.linalg.inv(np.sqrt(D_norm)).dot(L_norm).dot(np.linalg.inv(np.sqrt(D_norm)))
    W_dpc = np.exp(-beta*L1_norm - gamma*L2_norm) - np.eye(n)
    D_dpc = np.diag(np.sum(W_dpc, axis=1))
    L_dpc = D_dpc - W_dpc
    return Z, L_dpc

# 运行DPC-KNN-PCA算法并可视化结果
Z, L_dpc = DPC_KNN_PCA(X, 10, 0.5, 0.1, 0.1)
plt.scatter(Z[:, 0], Z[:, 1], c=y)
plt.show()

k-近邻算法的设计与实现python代码

以下是K-近邻算法的Python代码实现：

import numpy as np
from collections import Counter

class KNN:
    def __init__(self, k):
        self.k = k
    
    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 x in X_test:
            distances = [np.sqrt(np.sum((x - x_train)**2)) for x_train in self.X_train]
            nearest_indexes = np.argsort(distances)[:self.k]
            nearest_labels = [self.y_train[i] for i in nearest_indexes]
            most_common_label = Counter(nearest_labels).most_common(1)[0][0]
            y_pred.append(most_common_label)
        return y_pred

其中，`KNN`类包括以下方法：

- `__init__(self, k)`：初始化方法，传入K值。
- `fit(self, X_train, y_train)`：训练方法，传入训练集特征和标签。
- `predict(self, X_test)`：预测方法，传入测试集特征，返回预测结果。