本文整理汇总了Python中pysgpp.DataMatrix.array方法的典型用法代码示例。如果您正苦于以下问题:Python DataMatrix.array方法的具体用法?Python DataMatrix.array怎么用?Python DataMatrix.array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.DataMatrix
的用法示例。
在下文中一共展示了DataMatrix.array方法的6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: ppf
# 需要导入模块: from pysgpp import DataMatrix [as 别名]
# 或者: from pysgpp.DataMatrix import array [as 别名]
def ppf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
x = DataVector([x])
elif isMatrix(x):
x = DataMatrix(x)
if isinstance(x, DataMatrix):
A = x
B = DataMatrix(A.getNrows(), A.getNcols())
B.setAll(0.0)
elif isinstance(x, DataVector):
A = DataMatrix(1, len(x))
A.setRow(0, x)
B = DataMatrix(1, len(x))
B.setAll(0)
# do the transformation
opInvRosen = createOperationInverseRosenblattTransformationKDE(self.dist)
opInvRosen.doTransformation(A, B)
# transform the outcome
if isNumerical(x) or isinstance(x, DataVector):
return B.get(0, 0)
elif isinstance(x, DataMatrix):
return B.array()
示例2: cdf
# 需要导入模块: from pysgpp import DataMatrix [as 别名]
# 或者: from pysgpp.DataMatrix import array [as 别名]
def cdf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
x = DataVector([x])
elif isMatrix(x):
x = DataMatrix(x)
if isinstance(x, DataMatrix):
A = x
B = DataMatrix(A.getNrows(), A.getNcols())
B.setAll(0.0)
elif isinstance(x, DataVector):
A = DataMatrix(1, len(x))
A.setRow(0, x)
B = DataMatrix(1, len(x))
B.setAll(0)
# do the transformation
self.dist.cdf(A, B)
# transform the outcome
if isNumerical(x) or isinstance(x, DataVector):
return B.get(0, 0)
elif isinstance(x, DataMatrix):
return B.array()
示例3: estimateDiscreteL2Error
# 需要导入模块: from pysgpp import DataMatrix [as 别名]
# 或者: from pysgpp.DataMatrix import array [as 别名]
def estimateDiscreteL2Error(grid, alpha, f, n=1000):
gs = grid.getStorage()
# create control samples
samples = DataMatrix(np.random.rand(n, gs.dim()))
nodalValues = evalSGFunctionMulti(grid, alpha, samples)
fvalues = DataVector(samples.getNrows())
for i, sample in enumerate(samples.array()):
fvalues[i] = f(sample)
# compute the difference
nodalValues.sub(fvalues)
return nodalValues.l2Norm()
示例4: ppf
# 需要导入模块: from pysgpp import DataMatrix [as 别名]
# 或者: from pysgpp.DataMatrix import array [as 别名]
def ppf(self, x):
# convert the parameter to the right format
if isList(x):
x = DataVector(x)
elif isNumerical(x):
x = DataVector([x])
# do the transformation
if self.grid.getStorage().dim() == 1:
op = createOperationInverseRosenblattTransformation1D(self.grid)
ans = np.ndarray(len(x))
for i, xi in enumerate(x.array()):
ans[i] = op.doTransformation1D(self.alpha, xi)
if len(ans) == 1:
return ans[0]
else:
return ans
else:
if isinstance(x, DataMatrix):
A = x
B = DataMatrix(A.getNrows(), A.getNcols())
B.setAll(0.0)
elif isinstance(x, DataVector):
A = DataMatrix(1, len(x))
A.setRow(0, x)
B = DataMatrix(1, len(x))
B.setAll(0)
# do the transformation
op = createOperationInverseRosenblattTransformation(self.grid)
op.doTransformation(self.alpha, A, B)
# extract the outcome
if isNumerical(x) or isinstance(x, DataVector):
return B.get(0, 0)
elif isinstance(x, DataMatrix):
return B.array()
示例5: corrcoef
# 需要导入模块: from pysgpp import DataMatrix [as 别名]
# 或者: from pysgpp.DataMatrix import array [as 别名]
def corrcoef(self):
corrMatrix = DataMatrix(np.zeros((self.dim, self.dim)))
self.dist.corrcoef(corrMatrix)
return corrMatrix.array()
示例6: LibAGFDist
# 需要导入模块: from pysgpp import DataMatrix [as 别名]
# 或者: from pysgpp.DataMatrix import array [as 别名]
class LibAGFDist(Dist):
"""
The Sparse Grid Density Estimation (SGDE) distribution
"""
def __init__(self,
trainData,
samples=None,
testData=None,
bandwidths=None,
transformation=None,
surfaceFile=None):
super(LibAGFDist, self).__init__()
self.trainData = DataMatrix(trainData)
self.testData = testData
self.bounds = [[0, 1] for _ in xrange(trainData.shape[1])]
if len(self.bounds) == 1:
self.bounds = self.bounds[0]
if transformation is not None:
self.bounds = [trans.getBounds()
for trans in transformation.getTransformations()]
self.dim = trainData.shape[1]
self.samples = samples
self.transformation = transformation
self.bandwidths = None
if bandwidths is not None:
self.bandwidths = bandwidths
else:
op = createOperationInverseRosenblattTransformationKDE(self.trainData)
self.bandwidths = DataVector(self.dim)
op.getOptKDEbdwth(self.bandwidths)
self.surfaceFile = surfaceFile
@classmethod
def byConfig(cls, config):
if config is not None and os.path.exists(config):
# init density function
traindatafile, samplefile, testFile, testOutFile, bandwidthFile, surfaceFile = \
cls.computeDensity(config)
return cls.byFiles(traindatafile, samplefile,
testFile, testOutFile,
bandwidthFile, surfaceFile)
@classmethod
def byFiles(cls, trainDataFile,
samplesFile=None,
testFile=None,
testOutFile=None,
bandwidthFile=None,
surfaceFile=None):
# load training file
if os.path.exists(trainDataFile):
trainData = np.loadtxt(trainDataFile)
if len(trainData.shape) == 1:
trainData = np.array([trainData]).transpose()
else:
raise Exception('The training data file "%s" does not exist' % trainDataFile)
# load samples for quadrature
samples = None
if samplesFile is not None:
if os.path.exists(samplesFile):
samples = np.loadtxt(samplesFile)
# if the data is just one dimensional -> transform to
# matrix with one column
if len(samples.shape) == 1:
samples = np.array([samples]).transpose()
# load test file for evaluating pdf values
testData = None
if testFile is not None:
if os.path.exists(testFile):
testData = np.loadtxt(testFile)
# if the data is just one dimensional -> transform to
# matrix with one column
if len(testData.shape) == 1:
testData = np.array([testData]).transpose()
# load bandwidths file for evaluating pdf values
bandwidths = None
if bandwidthFile is not None:
if os.path.exists(bandwidthFile):
bandwidths = np.loadtxt(bandwidthFile)
# load pdf values for testSamples if available
if testOutFile is not None:
if os.path.exists(testOutFile):
testLikelihood = np.loadtxt(testOutFile)
# store the results in a hash map
if testData is not None:
testDataEval = {}
for i, sample in enumerate(testData):
testDataEval[tuple(sample)] = testLikelihood[i]
if surfaceFile is not None and not os.path.exists(surfaceFile):
surfaceFile = None
return cls(trainData,
#.........这里部分代码省略.........