svm多分类python代码
时间: 2023-09-07 12:14:58 浏览: 40
下面是一个简单的使用Python中的sklearn库实现SVM多分类的代码示例:
```python
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# 加载Iris数据集
iris = datasets.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=0.3, random_state=0)
# 创建SVM分类器
clf = SVC(kernel='linear')
# 训练分类器
clf.fit(X_train, y_train)
# 预测测试集
y_pred = clf.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print('准确率:', accuracy)
```
在这个例子中,我们使用Iris数据集进行多分类的SVM。首先,我们加载数据集并将其分为训练集和测试集。然后,我们创建一个SVM分类器,并使用训练数据拟合分类器。最后,我们使用测试数据进行预测,并计算预测结果的准确率。
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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:
```python
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:
```python
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。