trait Tensor extends AnyRef
A tensor is an algebraic object that describes a (multilinear) relationship between sets of algebraic objects related to a vector space. Objects that tensors may map between include vectors (which are often, but not always, understood as arrows with length that point in a direction) and scalars (which are often familiar numbers such as the real numbers), and, recursively, even other tensors. Tensors are defined independent of any basis, although they are often referred to by their components in a basis related to a particular coordinate system.
The shape of tensor (the number of dimensions and the size of each dimension) might be only partially known.
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- def ->[B](y: B): (Tensor, B)
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- def ensuring(cond: (Tensor) => Boolean, msg: => Any): Tensor
- def ensuring(cond: (Tensor) => Boolean): Tensor
- def ensuring(cond: Boolean, msg: => Any): Tensor
- def ensuring(cond: Boolean): Tensor
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- def formatted(fmtstr: String): String
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- This member is added by an implicit conversion from Tensor toStringFormat[Tensor] performed by method StringFormat in scala.Predef.
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- Deprecated
(Since version 2.12.16) Use
formatString.format(value)
instead ofvalue.formatted(formatString)
, or use thef""
string interpolator. In Java 15 and later,formatted
resolves to the new method in String which has reversed parameters.
- def →[B](y: B): (Tensor, B)
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- This member is added by an implicit conversion from Tensor toArrowAssoc[Tensor] performed by method ArrowAssoc in scala.Predef.
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- @deprecated
- Deprecated
(Since version 2.13.0) Use
->
instead. If you still wish to display it as one character, consider using a font with programming ligatures such as Fira Code.
Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.
Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.