from sklearn.svmimportSVCmodel=SVC(kernel='rbf',probability=True)param_grid={'C':[1e-3,1e-2,1e-1,1,10,100,1000],'gamma':[0.001,0.0001]}grid_search=GridSearchCV(model,param_grid,n_jobs=8,verbose=1)grid_search.fit(train_x,train_y)best_parameters=grid_search.best_estimator_.get_...
AI代码解释 """2. 通过网格搜索寻找最优参数"""parameters={'gamma':np.linspace(0.0001,0.1),'kernel':['linear','poly','rbf','sigmoid'],}model=svm.SVC()grid_model=GridSearchCV(model,parameters,cv=10,return_train_score=True)grid_model.fit(X_train,y_train)# 用测试集做预测 pred_label=gr...
Both of them adopt a objective function which is based on the leave-one-out cross-validation, and the SVM parameters are optimized by using GA (genetic algorithm) and PSO (particle swarm optimization) respectively. From experiment results, it can be concluded that both approaches, especially PSO...
# 它其实是一种贪心算法:拿当前对模型影响最大的参数调优,直到最优化;# 再拿下一个影响最大的参数调优,如此下去,直到所有的参数调整完毕。这个方法的缺点就是可能会调到局部最优而不是全局最优parameters={'svc__C':[1,5,10,50]}grid=GridSearchCV(model,param_grid=parameters,cv=3)%timegrid.fit(Xtrain...
```pythonfrom sklearn.model_selection import GridSearchCVparam_grid = {'C': [0.1, 1, 10, 100],'gamma': ['scale', 'auto'],'kernel': ['rbf']}grid_search = GridSearchCV(svm.SVC(), param_grid, cv=5)grid_search.fit(X_train, y_train)print("Best parameters:", grid_search....
parameters={ 'gamma':np.linspace(0.0001,0.1), 'kernel': ['linear','poly','rbf','sigmoid'], } model=svm.SVC() grid_model=GridSearchCV(model,parameters,cv=10,return_train_score=True) grid_model.fit(X_train,y_train) # 用测试集做预测 ...
Select optimal machine learning hyperparameters using Bayesian optimizationww2.mathworks.cn/help/releases/R2021a/stats/bayesopt.html 实现流程 1. 数据集(以3类二维高斯分布数据为例) % 生成3类样本(二维高斯分布)sigma=[0.60;00.6];numData=100;mu=[65];X_1=mvnrnd(mu,sigma,numData);label_1=ones...
Chaos Optimization Method of SVM Parameters Selection for Chaotic Time Series Forecasting For support vector regression(SVR),the setting of key parameters is very important,which determines the regression accuracy and generalization performance ... Y Hu,H Zhang - 《Physics Procedia》 被引量: 13发表:...
def __init__(self,dataMatIn, classLabels, C, toler, kTup): # Initialize the structure with the parameters self.X = dataMatIn self.labelMat = classLabels self.C = C self.tol = toler self.m = shape(dataMatIn)[0] self.alphas = mat(zeros((self.m,1))) ...
Parameters: [5x1 double] nr_class: 2 totalSV: 259%支持向量的数目 rho: 0.0514%b Label: [2x1 double]%classification中标签的个数 ProbA: [] ProbB: [] nSV: [2x1 double]%每类支持向量的个数 sv_coef: [259x1 double]%支持向量对应的Wi SVs: [259x13 double]%装的是259个支持向量 model.Pa...