DenseTensorBLAS

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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6. final def eq(arg0: AnyRef): Boolean

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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9. def gemm[T](transa: Char, transb: Char, m: Int, n: Int, k: Int, alpha: T, a: Array[T], aOffset: Int, lda: Int, b: Array[T], bOffset: Int, ldb: Int, beta: T, c: Array[T], cOffset: Int, ldc: Int)(implicit ev: TensorNumeric[T]): Unit

   

   The gemm routines compute a scalar-matrix-matrix product and add the result to a scalar-matrix product, with general matrices.

   The gemm routines compute a scalar-matrix-matrix product and add the result to a scalar-matrix product, with general matrices. C := alpha*op(A)*op(B) + beta*C, where: op(X) is one of op(X) = X, or op(X) = XT, alpha and beta are scalars, A, B and C are matrices: op(A) is an m-by-k matrix, op(B) is a k-by-n matrix, C is an m-by-n matrix.

   this interface treat the input array as column-major array.

   transa

   Specifies the form of op(A) used in the matrix multiplication: if transa=CblasNoTrans, then op(A) = A; if transa=CblasTrans, then op(A) = AT;

   transb

   Specifies the form of op(B) used in the matrix multiplication: if transb=CblasNoTrans, then op(B) = B; if transb=CblasTrans, then op(B) = BT;

   m

   Specifies the number of rows of the matrix op(A) and of the matrix C. The value of m must be at least zero.

   n

   Specifies the number of columns of the matrix op(B) and the number of columns of the matrix C. The value of n must be at least zero.

   k

   Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B). The value of k must be at least zero.

   alpha

   Specifies the scalar alpha.

   a

   Array. if transa=CblasNoTrans, size lda*k. if transa=CblasTrans, size lda*m.

   aOffset

   a offset

   lda

   Specifies the leading dimension of a as declared in the calling (sub)program. if transa=CblasNoTrans, lda must be at least max(1, m). if transa=CblasTrans, lda must be at least max(1, k).

   b

   Array. if transb=CblasNoTrans, size ldb by n. if transb=CblasTrans, size ldb by k.

   bOffset

   b offset

   ldb

   Specifies the leading dimension of b as declared in the calling (sub)program. if transb=CblasNoTrans, ldb must be at least max(1, m). if transb=CblasTrans, ldb must be at least max(1, k).

   beta

   Specifies the scalar beta. When beta is equal to zero, then c need not be set on input.

   c

   Array, size ldc by n. Before entry, the leading m-by-n part of the array c must contain the matrix C, except when beta is equal to zero, in which case c need not be set on entry.

   cOffset

   c offset

   ldc

   ldc must be at least max(1, m).

10. def gemv[T](alpha: T, matrix: Tensor[T], vector: Tensor[T], beta: T, r: Tensor[T])(implicit ev: TensorNumeric[T]): Unit

    

    to be fixed: this interface treat the input tensor as row-major array.

11. def gemv[T](trans: Char, m: Int, n: Int, alpha: T, a: Array[T], aOffset: Int, lda: Int, x: Array[T], xOffset: Int, incx: Int, beta: T, y: Array[T], yOffset: Int, incy: Int)(implicit ev: TensorNumeric[T]): Unit

    

    The gemv routines perform a matrix-vector operation defined as y := alpha*A*x + beta*y, or y := alpha*A'*x + beta*y, where: alpha and beta are scalars, x and y are vectors, A is an m-by-n matrix.

    The gemv routines perform a matrix-vector operation defined as y := alpha*A*x + beta*y, or y := alpha*A'*x + beta*y, where: alpha and beta are scalars, x and y are vectors, A is an m-by-n matrix.

    this interface treat the input array as column-major array.

    trans

    Specifies the operation: if trans=CblasNoTrans, then y := alpha*A*x + beta*y; if trans=CblasTrans, then y := alpha*A'*x + beta*y;

    m

    Specifies the number of rows of the matrix A. The value of m must be at least zero.

    n

    Specifies the number of columns of the matrix A. The value of n must be at least zero.

    alpha

    Specifies the scalar alpha.

    a

    Array, size lda* n. Before entry, the leading m-by-n part of the array a must contain the matrix A.

    aOffset

    a offset

    lda

    Specifies the leading dimension of a as declared in the calling (sub)program. the value of lda must be at least max(1, m).

    x

    Array, size at least (1+(n-1)*abs(incx)). Before entry, the incremented array x must contain the vector x.

    xOffset

    x offset

    incx

    Specifies the increment for the elements of x. The value of incx must not be zero.

    beta

    Specifies the scalar beta. When beta is set to zero, then y need not be set on input.

    y

    Array, size at least (1 +(m - 1)*abs(incy)). Before entry with non-zero beta, the incremented array y must contain the vector y.

    yOffset

    y offset

    incy

    Specifies the increment for the elements of y. The value of incy must not be zero.

12. def ger[T](m: Int, n: Int, alpha: T, x: Array[T], xOffset: Int, incx: Int, y: Array[T], yOffset: Int, incy: Int, a: Array[T], aOffset: Int, lda: Int)(implicit ev: TensorNumeric[T]): Unit

    

    The ger routines perform a matrix-vector operation defined as A := alpha*x*y'+ A, where: alpha is a scalar, x is an m-element vector, y is an n-element vector, A is an m-by-n general matrix.

    The ger routines perform a matrix-vector operation defined as A := alpha*x*y'+ A, where: alpha is a scalar, x is an m-element vector, y is an n-element vector, A is an m-by-n general matrix.

    this interface treat the input array as column-major array.

    m

    Specifies the number of rows of the matrix A. The value of m must be at least zero.

    n

    Specifies the number of columns of the matrix A. The value of n must be at least zero.

    alpha

    Specifies the scalar alpha.

    x

    Array, size at least (1 + (m - 1)*abs(incx)). Before entry, the incremented array x must contain the m-element vector x.

    xOffset

    x offset

    incx

    Specifies the increment for the elements of x. The value of incx must not be zero.

    y

    Array, size at least (1 + (n - 1)*abs(incy)). Before entry, the incremented array y must contain the n-element vector y.

    yOffset

    y offset

    incy

    Specifies the increment for the elements of y. The value of incy must not be zero.

    a

    Array, size lda * n. Before entry, the leading m-by-n part of the array a must contain the matrix A.

    aOffset

    a offset

    lda

    Specifies the leading dimension of a as declared in the calling (sub)program. the value of lda must be at least max(1, m).

13. final def getClass(): Class[_]

    

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14. def hashCode(): Int

    

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15. final def isInstanceOf[T0]: Boolean

    

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16. final def ne(arg0: AnyRef): Boolean

    

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17. final def notify(): Unit

    

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18. final def notifyAll(): Unit

    

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19. final def synchronized[T0](arg0: ⇒ T0): T0

    

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20. var time: Long

    

21. def toString(): String

    

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22. final def wait(): Unit

    

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23. final def wait(arg0: Long, arg1: Int): Unit

    

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24. final def wait(arg0: Long): Unit

    

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