svm多分类python代码

以下是 SVM 多分类的 Python 代码示例：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

# 载入数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

# 使用 SVM 进行多分类
model = SVC(kernel='linear', C=1, decision_function_shape='ovr')
model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 输出预测结果和真实标签
print("预测结果：", y_pred)
print("真实标签：", y_test)

注意：本代码只是示例，实际应用中需要根据具体问题进行调参和优化。

相关问题

pso-svm多分类python代码

以下是使用PSO-SVM进行多分类的Python代码示例：

import numpy as np
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 生成多分类数据集
X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_classes=3, random_state=42)

# 定义PSO-SVM分类器
class PSOSVM:
    def __init__(self, n_particles=10, max_iter=100, c1=2, c2=2, w=0.7):
        self.n_particles = n_particles
        self.max_iter = max_iter
        self.c1 = c1
        self.c2 = c2
        self.w = w

    def fit(self, X, y):
        # 数据标准化
        scaler = StandardScaler()
        X_scaled = scaler.fit_transform(X)

        # 初始化粒子群
        particles = np.random.uniform(low=-1, high=1, size=(self.n_particles, X.shape\[1\] + 1))
        velocities = np.zeros_like(particles)
        best_positions = particles.copy()
        best_scores = np.zeros(self.n_particles)

        # 迭代更新粒子群
        for _ in range(self.max_iter):
            for i in range(self.n_particles):
                # 计算粒子的适应度得分
                svm = SVC(C=10 ** particles\[i\]\[-1\])
                svm.fit(X_scaled, y)
                y_pred = svm.predict(X_scaled)
                score = accuracy_score(y, y_pred)

                # 更新粒子的最佳位置和最佳得分
                if score > best_scores\[i\]:
                    best_positions\[i\] = particles\[i\].copy()
                    best_scores\[i\] = score

            # 更新粒子的速度和位置
            for i in range(self.n_particles):
                r1 = np.random.rand(X.shape\[1\] + 1)
                r2 = np.random.rand(X.shape\[1\] + 1)
                velocities\[i\] = self.w * velocities\[i\] + self.c1 * r1 * (best_positions\[i\] - particles\[i\]) + self.c2 * r2 * (best_positions.mean(axis=0) - particles\[i\])
                particles\[i\] += velocities\[i\]

        # 找到最佳粒子的位置
        best_particle = particles\[np.argmax(best_scores)\]

        # 根据最佳粒子的位置训练最终的SVM分类器
        svm = SVC(C=10 ** best_particle\[-1\])
        svm.fit(X_scaled, y)

        self.svm = svm
        self.scaler = scaler

    def predict(self, X):
        X_scaled = self.scaler.transform(X)
        return self.svm.predict(X_scaled)

# 使用PSO-SVM进行多分类
pso_svm = PSOSVM(n_particles=10, max_iter=100, c1=2, c2=2, w=0.7)
pso_svm.fit(X, y)
y_pred = pso_svm.predict(X)

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

请注意，这只是一个示例代码，具体的PSO-SVM实现可能会有所不同。你可以根据自己的需求进行调整和修改。
#### 引用[.reference_title]
- *1* *2* *3* [基于Python的SVM算法深入研究](https://blog.csdn.net/weixin_45137708/article/details/106340493)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^koosearch_v1,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]

ga-svm多分类python代码

以下是一个使用遗传算法优化SVM多分类的Python示例代码：

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
import numpy as np
import random

# 准备数据集
X, y = load_data()  # 加载数据集

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 定义SVM模型
clf = SVC()

# 定义GA算法的适应度函数
def fitness(population):
    accuracies = []
    for chromosome in population:
        clf.set_params(C=chromosome[0], gamma=chromosome[1])
        clf.fit(X_train, y_train)
        y_pred = clf.predict(X_test)
        accuracy = accuracy_score(y_test, y_pred)
        accuracies.append(accuracy)
    return accuracies

# 定义GA算法的染色体编码
def chromosome_encoding(population_size):
    population = []
    for i in range(population_size):
        chromosome = [random.uniform(0.1, 10), random.uniform(0.001, 1)]
        population.append(chromosome)
    return population

# 定义GA算法的交叉操作
def crossover(parent1, parent2):
    child1 = [parent1[0], parent2[1]]
    child2 = [parent2[0], parent1[1]]
    return child1, child2

# 定义GA算法的变异操作
def mutation(chromosome):
    mutation_prob = 0.1
    if random.random() < mutation_prob:
        chromosome[random.randint(0, 1)] = random.uniform(0.1, 10)
    return chromosome

# 实现GA算法
population_size = 50
num_generations = 100
population = chromosome_encoding(population_size)
for i in range(num_generations):
    fitness_values = fitness(population)
    sorted_indices = np.argsort(fitness_values)[::-1]
    population = [population[i] for i in sorted_indices]
    next_generation = [population[0]]
    for j in range(1, population_size):
        parent1 = population[random.randint(0, population_size // 2)]
        parent2 = population[random.randint(0, population_size // 2)]
        child1, child2 = crossover(parent1, parent2)
        child1 = mutation(child1)
        child2 = mutation(child2)
        next_generation.extend([child1, child2])
    population = next_generation[:population_size]

# 测试模型
best_chromosome = population[0]
clf.set_params(C=best_chromosome[0], gamma=best_chromosome[1])
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy * 100))

在上述代码中，我们首先加载数据集并划分训练集和测试集。然后定义SVM模型、适应度函数、染色体编码、交叉操作和变异操作。接着使用遗传算法进行训练，并测试模型的准确率。最后输出模型的准确率。需要注意的是，在实际应用中，可以进行交叉验证等技术来提高模型的泛化能力。