?

from sklearn import svm, model_selection import pandas as pd import numpy as np #支持向量機(jī)主要調(diào)節(jié)的參數(shù)有：C、kernel、degree、gamma、coef0。 #C 默認(rèn) = 1.0正則化參數(shù)。 #kernel {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}, default =’rbf’ #degreeint, default = 3 多項(xiàng)式核函數(shù) ('poly') 的次數(shù) 浮點(diǎn)數(shù) #gamma{‘scale’, ‘a(chǎn)uto’} or float, default=’scale’ #coef0 default=0.0 核函數(shù)中的獨(dú)立項(xiàng)。它僅在“poly”和“sigmoid”中有意義。 from sklearn.model_selection import GridSearchCV# 設(shè)置隨機(jī)種子 random_state_model = 40 feature_number = 45 filepath = r"xls" ExcelData = pd.read_excel(filepath) # reading file ExcelData = np.array(ExcelData) classifypointdata = ExcelData[:, 0:feature_number] classifypointlabel = ExcelData[:, -1] # 切分?jǐn)?shù)據(jù)，固定隨機(jī)種子（random_state）時(shí)，同樣的代碼，得到的訓(xùn)練集數(shù)據(jù)相同。 # 此處運(yùn)行完的結(jié)果可能是string類型之后用于計(jì)算時(shí)記得類型轉(zhuǎn)換 train_data, test_data, train_label, test_label = model_selection.train_test_split(classifypointdata, classifypointlabel, random_state=random_state_model,train_size=0.8) SVM_classifier = svm.SVC() #kernel核心函數(shù) # SVM_params = [{'kernel': ['linear'], 'C': [1, 10, 100]}, # {'kernel': ['poly'], 'C': [1,10], 'degree': [1,3,5]}, # {'kernel': ['rbf'], 'C': [1, 10], 'gamma': [0.01,0.00001]}] SVM_params = {'kernel': ['linear'], 'C': [1, 10]}SVM_GSC = GridSearchCV(SVM_classifier, SVM_params, cv=3) SVM_GSC.fit(train_data,train_label) #獲取網(wǎng)格搜索每個(gè)參數(shù)組合 SVM_GSC.cv_results_['params'] #獲取網(wǎng)格搜索每個(gè)參數(shù)組合所對(duì)應(yīng)的平均測(cè)試分值 SVM_GSC.cv_results_['mean_test_score'] #獲取最好的參數(shù) print(SVM_GSC.best_params_) print(SVM_GSC.best_score_) print(SVM_GSC.best_estimator_)

總結(jié)

以上是生活随笔為你收集整理的网格搜索支持向量机运行结束不了的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。