本文整理汇总了Python中sklearn.svm.SVR.fitPrepared方法的典型用法代码示例。如果您正苦于以下问题：Python SVR.fitPrepared方法的具体用法？Python SVR.fitPrepared怎么用？Python SVR.fitPrepared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.fitPrepared方法的1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: kernelize
# 需要导入模块: from sklearn.svm import SVR [as 别名]
# 或者: from sklearn.svm.SVR import fitPrepared [as 别名]
"svr")
sigma = [0]
testimg, testdot, testmapping, testtags = Counter.prepareData(img, dot,
sigma,
normalize =
False, smooth
= True
)
#print "blub", testimg.shape
#print testimg
#print testdot, np.sum(testdot)
boxConstraints = []
#boxConstraints = [(12, img[:,:,:])]
#boxConstraints = [(3, img[0:30,0:30,:])]
#boxConstraints.reshape((-1, boxConstraints.shape[-1]))
success = Counter.fitPrepared(testimg[testmapping,:], testdot[testmapping], testtags, epsilon = 0.000,
boxConstraints = boxConstraints)
#success = Counter.fitPrepared(testimg[indices,:], testdot[indices], testtags[:len(indices)], epsilon = 0.000)
#print Counter.w, Counter.
print "learning finished"
#conversion step
#Q = kernelize(B, method = "gaussian")
##Q = B * B.transpose()
#tags = np.zeros(numVariables,dtype=np.int8)
#tags[0:len(pindices)] = 1
#tags[len(pindices):] = -1
#c = dot[allIndices] * (-tags)+ epsilon
#upperBounds = [None, pMult, lMult]
#success,solution = optimize(tags,Q,c,upperBounds)
## Put model data into dense matrices
#print Counter.b, Counter.w