TensorEval2 Namespace Reference

Functions
def	ComputeAFunctional
	idea s = np.ones((16, 16, 4)) indx = np.transpose(s.nonzero()) def f1(data, item): data[item[0], item[1], item[2]]= data[item[0], item[1], item[2]]**2 [f1(s, itemX) for itemX in indx]
def	ComputeTensorFunctional
def	ComputeTensorKFunctional
def	ComputeTensorPFunctional
def	EvaluateTensorK0
def	EvaluateTensorK1
def	EvaluateTensorP0
def	EvaluateTensorX0
def	EvaluateTensorX1
def	EvaluateWM0
Variables
tuple	logger = logging.getLogger(__name__)

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Function Documentation

def TensorEval2::ComputeAFunctional	(		A,
			b,
			G,
			k
	)

idea s = np.ones((16, 16, 4)) indx = np.transpose(s.nonzero()) def f1(data, item): data[item[0], item[1], item[2]]= data[item[0], item[1], item[2]]**2 [f1(s, itemX) for itemX in indx]

def TensorEval2::ComputeTensorFunctional	(		data,
			xT,
			yT,
			lT,
			ET,
			A,
			k
	)

def TensorEval2::ComputeTensorKFunctional	(		tens,
			shp,
			xT,
			yT,
			lT,
			ET,
			k
	)

def TensorEval2::ComputeTensorPFunctional	(		y,
			xT,
			yT,
			lT,
			ET,
			A
	)

def TensorEval2::EvaluateTensorK0	(		ten,
			shape
	)

def TensorEval2::EvaluateTensorK1	(		ten,
			shape,
			wmI = empty(0)
	)

def TensorEval2::EvaluateTensorP0	(		data,
			G,
			b
	)

def TensorEval2::EvaluateTensorX0	(		data,
			G,
			b
	)

def TensorEval2::EvaluateTensorX1	(		data,
			G,
			b,
			wmI = empty(0)
	)

def TensorEval2::EvaluateWM0	(		data,
			baseline = 0,
			wmMin = 0,
			wmMax = 1000
	)

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Variable Documentation

tuple TensorEval2::logger = logging.getLogger(__name__)