PSO-logistic多分类预测并输出参数python代码
时间: 2024-01-16 13:05:39 浏览: 28
这里是一个简单的PSO-logistic多分类预测并输出参数的Python代码示例,使用了scikit-learn和pyswarms库:
```python
import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from pyswarms.single.global_best import GlobalBestPSO
# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
# 定义PSO-logistic分类器
def pso_logistic(X, y, params):
# 解压缩参数
w, b = params
# 计算logistic回归的预测结果
clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000, tol=1e-4, fit_intercept=False, C=1e6)
clf.coef_ = w.reshape(3, -1)
clf.intercept_ = b.reshape(3,)
y_pred = clf.predict(X)
# 计算分类准确率
accuracy = accuracy_score(y, y_pred)
# 返回分类准确率作为适应度
return 1.0 - accuracy
# 定义PSO算法的参数范围和适应度函数
bounds = (np.array([-10.0]*12), np.array([10.0]*12))
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
# 运行PSO算法进行优化
optimizer = GlobalBestPSO(n_particles=20, dimensions=12, options=options, bounds=bounds)
best_params, best_cost = optimizer.optimize(pso_logistic, iters=100)
# 输出最优参数和分类准确率
w, b = best_params
clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000, tol=1e-4, fit_intercept=False, C=1e6)
clf.coef_ = w.reshape(3, -1)
clf.intercept_ = b.reshape(3,)
y_pred = clf.predict(X)
accuracy = accuracy_score(y, y_pred)
print("Best parameters:", best_params)
print("Best accuracy:", accuracy)
# 输出模型参数
print("Coefficients:", clf.coef_)
print("Intercepts:", clf.intercept_)
```
这个代码示例使用PSO算法进行优化,尝试找到logistic回归的系数和截距,使得多分类模型的分类准确率最大。最后输出了最优参数、最优准确率以及模型的系数和截距。这个示例只是一个简单的实现,实际应用中可能需要更复杂的PSO-logistic模型和更严格的性能评估。