本文整理汇总了Python中pysgpp.DataVector.sqr方法的典型用法代码示例。如果您正苦于以下问题:Python DataVector.sqr方法的具体用法?Python DataVector.sqr怎么用?Python DataVector.sqr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.sqr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: testOps
# 需要导入模块: from pysgpp import DataVector [as 别名]
# 或者: from pysgpp.DataVector import sqr [as 别名]
def testOps(self):
from pysgpp import DataVector
# sum
self.assertAlmostEqual(self.d_rand.sum(), sum(self.l_rand_total))
# sqr
d = DataVector(self.d_rand)
d.sqr()
for i in xrange(self.N):
self.assertEqual(self.d_rand[i]**2, d[i])
# abs
d = DataVector(self.d_rand)
d.abs()
for i in xrange(self.N):
self.assertEqual(abs(self.d_rand[i]), d[i])
# componentwise_mult
d = DataVector(self.d_rand)
# d2 = DataVector(self.nrows, self.ncols)
d2 = DataVector(self.N)
for i in xrange(self.N):
d2[i] = i
d.componentwise_mult(d2)
for i in xrange(self.N):
self.assertEqual(self.d_rand[i]*i, d[i])
# componentwise_div
d = DataVector(self.d_rand)
for i in xrange(self.N):
d2[i] = i+1
d.componentwise_div(d2)
for i in xrange(self.N):
self.assertEqual(self.d_rand[i]/(i+1), d[i])
示例2: calc_indicator_value
# 需要导入模块: from pysgpp import DataVector [as 别名]
# 或者: from pysgpp.DataVector import sqr [as 别名]
def calc_indicator_value(self, index):
numData = self.trainData.getNrows()
numCoeff = self.grid.getSize()
seq = self.grid.getStorage().seq(index)
num = 0
denom = 0
tmp = DataVector(numCoeff)
self.multEval.multTranspose(self.errors, tmp)
num = tmp.__getitem__(seq)
num **= 2
alpha = DataVector(numCoeff)
col = DataVector(numData)
alpha.__setitem__(seq, 1.0)
self.multEval.mult(alpha, col)
col.sqr()
denom = col.sum()
if denom == 0:
print "Denominator is zero"
value = 0
else:
value = num/denom
return value
示例3: Regressor
# 需要导入模块: from pysgpp import DataVector [as 别名]
# 或者: from pysgpp.DataVector import sqr [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()) )