DeepLearning,Tensor,SingularValueDecomposition
compute singular value decomposition of a Tensor
Calling Sequence
Parameters
Options
Description
Examples
Compatibility
SingularValueDecomposition(x,opts)
x
-
Tensor
opts
zero or more options as specified below
name=string
The value of option name specifies an optional name for this Tensor, to be displayed in output and when visualizing the dataflow graph.
The SingularValueDecomposition(x,opts) or SVD(x,opts) commands compute a singular value decomposition of one or more matrices in x
with⁡DeepLearning:
t≔Constant⁡LinearAlgebra:-VandermondeMatrix⁡3.,5.,7.
t≔DeepLearning TensorName: Const:0Shape: [3, 3]Data Type: float[8]
svd≔SingularValueDecomposition⁡t
svd≔DeepLearning TensorName: noneShape: undefinedData Type: undefined
value⁡svd
56.46412410674442.187672563859500.129528419652346,0.1661839554963110.7844709268945020.5974849435700300.4514913663923320.478138549997582−0.7533518919332420.876663241506558−0.3949542908533730.274722895164790,0.02646527484549620.3966110835654450.9176051643383420.1574920629634680.904808849092890−0.3956225432236030.987165558217498−0.1549857896590650.0385170828710182
The DeepLearning,Tensor,SingularValueDecomposition command was introduced in Maple 2018.
For more information on Maple 2018 changes, see Updates in Maple 2018.
See Also
DeepLearning Overview
LinearAlgebra[SingularValues]
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