本文整理汇总了Python中sklearn.svm.SVR.set_params方法的典型用法代码示例。如果您正苦于以下问题：Python SVR.set_params方法的具体用法？Python SVR.set_params怎么用？Python SVR.set_params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.SVR的用法示例。
在下文中一共展示了SVR.set_params方法的2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: dict
# 需要导入模块: from sklearn.svm import SVR [as 别名]
# 或者: from sklearn.svm.SVR import set_params [as 别名]
X = scaler.fit_transform(X)
Xt = scaler.transform(Xt)
##############################################################
# stacking result = svr + α * rfr + β * gbr
# tune α and β with cross validation
##############################################################
scores = dict()
skf = cross_validation.StratifiedKFold(Y, n_folds=3)
for train_index, test_index in skf:
X1, X2 = X[train_index], X[test_index]
Y1, Y2 = Y[train_index], Y[test_index]
# predict with SVR
svr = SVR()
svr.set_params(**pickle.load(open("svr.p", "rb" )))
svr.fit(X1, Y1)
Y_svr = svr.predict(X2)
# predict with RF
rfr = RandomForestRegressor(n_estimators = 1000)
rfr.set_params(**pickle.load(open("rfr.p", "rb" )))
rfr.fit(X1, Y1)
Y_rfr = rfr.predict(X2)
# predict with GBT
gbr = GradientBoostingRegressor(n_estimators=3000)
gbr.set_params(**pickle.load(open("gbr.p", "rb" )))
gbr.fit(X1, Y1)
Y_gbr = gbr.predict(X2)
示例2: range
# 需要导入模块: from sklearn.svm import SVR [as 别名]
# 或者: from sklearn.svm.SVR import set_params [as 别名]
y = range(10) # np.random.randn(n_samples)
#X = np.random.randn(n_samples, n_features)
#y = [
# [1, 38],
# [2, 59],
# [3, 14],
#]
y = [1, 0, 0, 1, 1, 1, 1, 0, 0, 0]
X = [
[1, 24],
[3, 48],
[3, 63],
[1, 12],
[1, 27],
[1, 31],
[1, 18],
[3, 50],
[3, 73],
[3, 82],
]
y = [i for i in range(1, 10)]
X = [[i] for i in range(1, 10)]
print y
print X
clf = SVR(kernel='linear')#, C=1.0, epsilon=0.2)
print clf.fit(X, y)
print clf.predict([[i] for i in range(10)])
print clf.set_params(kernel='rbf')