本文整理汇总了Python中pysgpp.DataVector.sub方法的典型用法代码示例。如果您正苦于以下问题：Python DataVector.sub方法的具体用法？Python DataVector.sub怎么用？Python DataVector.sub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pysgpp.DataVector的用法示例。
在下文中一共展示了DataVector.sub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: __computeRanking
# 需要导入模块: from pysgpp import DataVector [as 别名]
# 或者: from pysgpp.DataVector import sub [as 别名]
def __computeRanking(self, v, A, b):
"""
Compute ranking for variance estimation
\argmax_{i \in \A} | v (2 Av - vb) |
@param v: DataVector, coefficients of known grid points
@param A: DataMatrix, stiffness matrix
@param b: DataVector, squared expectation value contribution
@return: numpy array, contains the ranking for the given samples
"""
# update the ranking
av = DataVector(A.getNrows())
av.setAll(0.0)
# = Av
for i in xrange(A.getNrows()):
for j in xrange(A.getNcols()):
av[i] += A.get(i, j) * v[j]
av.mult(2.) # = 2 * Av
b.componentwise_mult(v) # = v * b
av.sub(b) # = 2 * Av - v * b
w = DataVector(v)
w.componentwise_mult(av) # = v * (2 * Av - v * b)
w.abs() # = | v * (2 * Av - v * b) |
return w.array()示例2: Regressor
# 需要导入模块: from pysgpp import DataVector [as 别名]
# 或者: from pysgpp.DataVector import sub [as 别名]
class Regressor(Learner):
## Errors per basis function
errors = None
## Error vector
error = None
##constructor
def __init__(self):
super(Regressor,self).__init__()
##calculate L2-norm of error
# @return: last L2-norm of error
def getL2NormError(self):
return sqrt(self.error.sum())
## calculate max error
# @return: max error
def getMaxError(self):
return sqrt(self.error.max())
## calculate min error
# @return: min error
def getMinError(self):
return sqrt(self.error.min())
## Evaluate regression MSE
#
# @param data: DataContainer dataset
# @param alpha: DataVector alpha-vector
# @return: mean square error
def evalError(self, data, alpha):
size = data.getPoints().getNrows()
if size == 0: return 0
self.error = DataVector(size)
self.specification.getBOperator(data.getName()).mult(alpha, self.error)
self.error.sub(data.getValues()) # error vector
self.error.sqr() # entries squared
errorsum = self.error.sum()
mse = errorsum / size # MSE
# calculate error per basis function
self.errors = DataVector(len(alpha))
self.specification.getBOperator(data.getName()).multTranspose(self.error, self.errors)
self.errors.componentwise_mult(alpha)
return mse
##Update different statistics about training progress
# @param alpha: DataVector alpha-vector
# @param trainSubset: DataContainer with training data
# @param testSubset: DataContainer with validation data, default value: None
def updateResults(self, alpha, trainSubset, testSubset = None):
self.knowledge.update(alpha)
#eval Error for training data and append it to other in this iteration
self.trainAccuracy.append(self.evalError(trainSubset, alpha))
i = float(len(self.trainAccuracy))
#eval error for test data and append it to other in this iteration
if testSubset != None:
self.testAccuracy.append(self.evalError(testSubset, alpha))
self.testingOverall.append(sum(self.testAccuracy)/i)
self.trainingOverall.append(sum(self.trainAccuracy)/i)
self.numberPoints.append(self.grid.getSize())
##Refines grid with the number of points as specified in corresponding TrainingSpecification object
def refineGrid(self):
self.notifyEventControllers(LearnerEvents.REFINING_GRID)
pointsNum = self.specification.getNumOfPointsToRefine( self.grid.createGridGenerator().getNumberOfRefinablePoints() )
self.grid.createGridGenerator().refine( SurplusRefinementFunctor(self.errors, pointsNum, self.specification.getAdaptThreshold()) )