trait Scalar extends Tensor
Scalar: rank-0 tensor.
Linear Supertypes
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- Scalar
- Tensor
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- by any2stringadd
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Abstract Value Members
Concrete Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- def *(y: Matrix): Matrix
- def *(y: Vector): Vector
- def *(y: Scalar): Scalar
- def **(y: Scalar): Scalar
- def +(y: Scalar): Scalar
- def -(y: Scalar): Scalar
- def ->[B](y: B): (Scalar, B)
- def /(y: Scalar): Scalar
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- def apply(env: (String, Tensor)*): Scalar
Applies the expression.
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- def d(dx: Var*): Vector
Returns the gradient vector.
- def ensuring(cond: (Scalar) => Boolean, msg: => Any): Scalar
- def ensuring(cond: (Scalar) => Boolean): Scalar
- def ensuring(cond: Boolean, msg: => Any): Scalar
- def ensuring(cond: Boolean): Scalar
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable])
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- def rank: Option[Int]
The rank of tensor, i.e.
- def shape: Option[Array[IntScalar]]
The shape of tensor, i.e the size of each dimension.
- def simplify: Scalar
Simplify the expression.
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- AnyRef → Any
- def unary_+: Scalar
- def unary_-: Scalar
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
Shadowed Implicit Value Members
- def +(other: String): String
- Implicit
- This member is added by an implicit conversion from Scalar toany2stringadd[Scalar] performed by method any2stringadd in scala.Predef.
- Shadowing
- This implicitly inherited member is shadowed by one or more members in this class.
To access this member you can use a type ascription:(scalar: any2stringadd[Scalar]).+(other)
- Definition Classes
- any2stringadd
Deprecated Value Members
- def formatted(fmtstr: String): String
- Implicit
- This member is added by an implicit conversion from Scalar toStringFormat[Scalar] performed by method StringFormat in scala.Predef.
- Definition Classes
- StringFormat
- Annotations
- @deprecated @inline()
- Deprecated
(Since version 2.12.16) Use
formatString.format(value)instead ofvalue.formatted(formatString), or use thef""string interpolator. In Java 15 and later,formattedresolves to the new method in String which has reversed parameters.
- def →[B](y: B): (Scalar, B)
- Implicit
- This member is added by an implicit conversion from Scalar toArrowAssoc[Scalar] performed by method ArrowAssoc in scala.Predef.
- Definition Classes
- ArrowAssoc
- Annotations
- @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.