本文整理汇总了Python中sklearn.svm.SVC.probability方法的典型用法代码示例。如果您正苦于以下问题：Python SVC.probability方法的具体用法？Python SVC.probability怎么用？Python SVC.probability使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.svm.SVC的用法示例。
在下文中一共展示了SVC.probability方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: new_pipe
# 需要导入模块: from sklearn.svm import SVC [as 别名]
# 或者: from sklearn.svm.SVC import probability [as 别名]
def new_pipe(mod):
svc = SVC()
svc.kernel = 'linear'
svc.C = params_map[mod]['C']
svc.probability = True
masker = SimpleMaskerPipeline(.2)
return Pipeline([
('columns', ColumnSelector(index_map[mod])),
('whitematter', masker),
('anova', SelectKBest(k=500)),
('svc', svc)
])示例2: generate_file_training_file
# 需要导入模块: from sklearn.svm import SVC [as 别名]
# 或者: from sklearn.svm.SVC import probability [as 别名]
# learnImageType_list.append(content[1].split('\n')[0])
return unknownImage_list
generate_file_training_file(40, 20, 5, 15, 3) # random
learnImage_list, learnImageType_list = read_file_learn("../training.txt", "r+")
learnUnknownImage_list = read_file_unknown("../unkown.txt", "r+")
# learnImage_list, learnImageType_list = read_file_learn("../manual_training.txt", "r+")
# learnUnknownImage_list = read_file_unknown("../manual_unknown.txt", "r+")
print ("svm")
clf = SVC()
clf.probability = True
clf.fit(learnImage_list, learnImageType_list)
# print learnImageType_list
# print learnImage_list
# predicted = clf.predict(learnUnknownImage_list)
prob = clf.predict_proba(learnUnknownImage_list)
# print predicted
print prob
len_prob = len(prob)
count = 0
while count < len_prob:
print (prob[count][1])
count += 1
示例3: cross_val_score
# 需要导入模块: from sklearn.svm import SVC [as 别名]
# 或者: from sklearn.svm.SVC import probability [as 别名]
# In[475]:
scoresOS = cross_val_score(model, osx, osy, cv=5)
scoresOS.mean()
# In[338]:
preds = modelOS.predict(X_test)
# In[500]:
modelOS.probability = True
fpr, tpr, thresholds = roc_curve(y_test, modelOS.predict_proba(X_test)[:, 0])
roc_auc = auc(fpr, tpr)
print "AUC =", roc_auc
# In[510]:
modelOS.predict_proba(X_test)[:, 0]
# In[506]: