Closes Bears-R-Us#3632: refactor LinalgMsg to remove registerND annot…

…ation (Bears-R-Us#3633)

Co-authored-by: Amanda Potts <ajpotts@users.noreply.github.com>
Co-authored-by: drculhane <drculhane@users.noreply.github.com>

ServerModules.cfg

@@ -18,6 +18,7 @@ In1dMsg
 IndexingMsg
 JoinEqWithDTMsg
 KExtremeMsg
+LinalgMsg
 LogMsg
 ManipulationMsg
 OperatorMsg

arkouda/numeric.py

@@ -10,6 +10,7 @@
 from arkouda.client import generic_msg
 from arkouda.dtypes import DTypes, bigint
 from arkouda.dtypes import dtype as akdtype
+from arkouda.dtypes import int64 as akint64
 from arkouda.dtypes import (
  int_scalars,
  isSupportedNumber,
@@ -34,6 +35,7 @@
  "ceil",
  "clip",
  "count_nonzero",
+ "eye",
  "floor",
  "trunc",
  "round",
@@ -48,6 +50,11 @@
  "exp",
  "expm1",
  "square",
+ "matmul",
+ "triu",
+ "tril",
+ "transpose",
+ "vecdot",
  "cumsum",
  "cumprod",
  "sin",
@@ -2248,3 +2255,302 @@ def putmask(pda: pdarray, mask: Union[bool, pdarray], values: pdarray):
  result = result[:-(reduction)]
 
  pda[:] = where(mask, result, pda) # pda[:] = allows us to return modified value
+
+
+def eye(rows: int_scalars, cols: int_scalars, diag: int_scalars = 0, dt: type = akint64):
+ """
+ Return a pdarray with zeros everywhere except along a diagonal, which is all ones.
+ The matrix need not be square.
+
+ Parameters
+ ----------
+ rows : int_scalars
+ cols : int_scalars
+ diag : int_scalars
+ if diag = 0, zeros start at element [0,0] and proceed along diagonal
+ if diag > 0, zeros start at element [0,diag] and proceed along diagonal
+ if diag < 0, zeros start at element [diag,0] and proceed along diagonal
+ etc.
+
+ Returns
+ -------
+ pdarray
+ an array of zeros with ones along the specified diagonal
+
+ Examples
+ --------
+ >>> ak.eye(rows=4,cols=4,diag=0,dt=ak.int64)
+ array([array([1 0 0 0]) array([0 1 0 0]) array([0 0 1 0]) array([0 0 0 1])])
+ >>> ak.eye(rows=3,cols=3,diag=1,dt=ak.float64)
+ array([array([0.00000000000000000 1.00000000000000000 0.00000000000000000])
+ array([0.00000000000000000 0.00000000000000000 1.00000000000000000])
+ array([0.00000000000000000 0.00000000000000000 0.00000000000000000])])
+ >>> ak.eye(rows=4,cols=4,diag=-1,dt=ak.bool_)
+ array([array([False False False False]) array([True False False False])
+ array([False True False False]) array([False False True False])]
+
+ Notes
+ -----
+ if rows = cols and diag = 0, the result is an identity matrix
+ Server returns an error if rank of pda < 2
+
+ """
+
+ cmd = f"eye<{akdtype(dt).name}>"
+ args = {
+ "rows": rows,
+ "cols": cols,
+ "diag": diag,
+ }
+ return create_pdarray(
+ generic_msg(
+ cmd=cmd,
+ args=args,
+ )
+ )
+
+
+def triu(pda: pdarray, diag: int_scalars = 0):
+ """
+ Return a copy of the pda with the lower triangle zeroed out
+
+ Parameters
+ ----------
+ pda : pdarray
+ diag : int_scalars
+ if diag = 0, zeros start just above the main diagonal
+ if diag = 1, zeros start at the main diagonal
+ if diag = 2, zeros start just below the main diagonal
+ etc.
+
+ Returns
+ -------
+ pdarray
+ a copy of pda with zeros in the lower triangle
+
+ Examples
+ --------
+ >>> a = ak.array([[1,2,3,4,5],[2,3,4,5,6],[3,4,5,6,7],[4,5,6,7,8],[5,6,7,8,9]])
+ >>> ak.triu(a,diag=0)
+ array([array([1 2 3 4 5]) array([0 3 4 5 6]) array([0 0 5 6 7])
+ array([0 0 0 7 8]) array([0 0 0 0 9])])
+ >>> ak.triu(a,diag=1)
+ array([array([0 2 3 4 5]) array([0 0 4 5 6]) array([0 0 0 6 7])
+ array([0 0 0 0 8]) array([0 0 0 0 0])])
+ >>> ak.triu(a,diag=2)
+ array([array([0 0 3 4 5]) array([0 0 0 5 6]) array([0 0 0 0 7])
+ array([0 0 0 0 0]) array([0 0 0 0 0])])
+ >>> ak.triu(a,diag=3)
+ array([array([0 0 0 4 5]) array([0 0 0 0 6]) array([0 0 0 0 0])
+ array([0 0 0 0 0]) array([0 0 0 0 0])])
+ >>> ak.triu(a,diag=4)
+ array([array([0 0 0 0 5]) array([0 0 0 0 0]) array([0 0 0 0 0])
+ array([0 0 0 0 0]) array([0 0 0 0 0])])
+
+ Notes
+ -----
+ Server returns an error if rank of pda < 2
+
+ """
+
+ cmd = f"triu<{pda.dtype},{pda.ndim}>"
+ args = {
+ "array": pda,
+ "diag": diag,
+ }
+ return create_pdarray(
+ generic_msg(
+ cmd=cmd,
+ args=args,
+ )
+ )
+
+
+def tril(pda: pdarray, diag: int_scalars = 0):
+ """
+ Return a copy of the pda with the upper triangle zeroed out
+
+ Parameters
+ ----------
+ pda : pdarray
+ diag : int_scalars
+ if diag = 0, zeros start just below the main diagonal
+ if diag = 1, zeros start at the main diagonal
+ if diag = 2, zeros start just above the main diagonal
+ etc.
+
+ Returns
+ -------
+ pdarray
+ a copy of pda with zeros in the upper triangle
+
+ Examples
+ --------
+ >>> a = ak.array([[1,2,3,4,5],[2,3,4,5,6],[3,4,5,6,7],[4,5,6,7,8],[5,6,7,8,9]])
+ >>> ak.tril(a,diag=4)
+ array([array([1 2 3 4 5]) array([2 3 4 5 6]) array([3 4 5 6 7])
+ array([4 5 6 7 8]) array([5 6 7 8 9])])
+ >>> ak.tril(a,diag=3)
+ array([array([1 2 3 4 0]) array([2 3 4 5 6]) array([3 4 5 6 7])
+ array([4 5 6 7 8]) array([5 6 7 8 9])])
+ >>> ak.tril(a,diag=2)
+ array([array([1 2 3 0 0]) array([2 3 4 5 0]) array([3 4 5 6 7])
+ array([4 5 6 7 8]) array([5 6 7 8 9])])
+ >>> ak.tril(a,diag=1)
+ array([array([1 2 0 0 0]) array([2 3 4 0 0]) array([3 4 5 6 0])
+ array([4 5 6 7 8]) array([5 6 7 8 9])])
+ >>> ak.tril(a,diag=0)
+ array([array([1 0 0 0 0]) array([2 3 0 0 0]) array([3 4 5 0 0])
+ array([4 5 6 7 0]) array([5 6 7 8 9])])
+
+ Notes
+ -----
+ Server returns an error if rank of pda < 2
+
+ """
+ cmd = f"tril<{pda.dtype},{pda.ndim}>"
+ args = {
+ "array": pda,
+ "diag": diag,
+ }
+ return create_pdarray(
+ generic_msg(
+ cmd=cmd,
+ args=args,
+ )
+ )
+
+
+def transpose(pda: pdarray):
+ """
+ Compute the transpose of a matrix.
+
+ Parameters
+ ----------
+ pda : pdarray
+
+ Returns
+ -------
+ pdarray
+ the transpose of the input matrix
+
+ Examples
+ --------
+ >>> a = ak.array([[1,2,3,4,5],[1,2,3,4,5]])
+ >>> ak.transpose(a)
+ array([array([1 1]) array([2 2]) array([3 3]) array([4 4]) array([5 5])])
+
+
+ Notes
+ -----
+ Server returns an error if rank of pda < 2
+
+ """
+ cmd = f"transpose<{pda.dtype},{pda.ndim}>"
+ args = {
+ "array": pda,
+ }
+ return create_pdarray(
+ generic_msg(
+ cmd=cmd,
+ args=args,
+ )
+ )
+
+
+def matmul(pdaLeft: pdarray, pdaRight: pdarray):
+ """
+ Compute the product of two matrices.
+
+ Parameters
+ ----------
+ pdaLeft : pdarray
+ pdaRight : pdarray
+
+ Returns
+ -------
+ pdarray
+ the matrix product pdaLeft x pdaRight
+
+ Examples
+ --------
+ >>> a = ak.array([[1,2,3,4,5],[1,2,3,4,5]])
+ >>> b = ak.array(([1,1],[2,2],[3,3],[4,4],[5,5]])
+ >>> ak.matmul(a,b)
+ array([array([30 30]) array([45 45])])
+
+ >>> x = ak.array([[1,2,3],[1.1,2.1,3.1]])
+ >>> y = ak.array([[1,1,1],[0,2,2],[0,0,3]])
+ >>> ak.matmul(x,y)
+ array([array([1.00000000000000000 5.00000000000000000 14.00000000000000000])
+ array([1.1000000000000001 5.3000000000000007 14.600000000000001])])
+
+ Notes
+ -----
+ Server returns an error if shapes of pdaLeft and pdaRight
+ are incompatible with matrix multiplication.
+
+ """
+ if pdaLeft.ndim != pdaRight.ndim:
+ raise ValueError("matmul requires matrices of matching rank.")
+ cmd = f"matmul<{pdaLeft.dtype},{pdaRight.dtype},{pdaLeft.ndim}>"
+ args = {
+ "x1": pdaLeft,
+ "x2": pdaRight,
+ }
+ return create_pdarray(
+ generic_msg(
+ cmd=cmd,
+ args=args,
+ )
+ )
+
+
+def vecdot(x1: pdarray, x2: pdarray):
+ """
+ Compute the generalized dot product of two vectors along the given axis.
+ Assumes that both tensors have already been broadcast to the same shape.
+
+ Parameters
+ ----------
+ x1 : pdarray
+ x2 : pdarray
+
+ Returns
+ -------
+ pdarray
+ x1 vecdot x2
+
+ Examples
+ --------
+ >>> a = ak.array([[1,2,3,4,5],[1,2,3,4,5]])
+ >>> b = ak.array(([2,2,2,2,2],[2,2,2,2,2]])
+ >>> ak.vecdot(a,b)
+ array([5 10 15 20 25])
+ >>> ak.vecdot(b,a)
+ array([5 10 15 20 25])
+
+ Raises
+ ------
+ ValueTypeError
+ Raised if x1 and x2 are not of matching shape or if rank of x1 < 2
+
+ """
+
+ if x1.shape != x2.shape:
+ raise ValueError("vecdot requires matrices of matching rank.")
+ if x1.ndim < 2:
+ raise ValueError("vector requires matrices of rank 2 or more.")
+ cmd = f"vecdot<{x1.dtype},{x2.dtype},{x1.ndim}>"
+ args = {
+ "x1": x1,
+ "x2": x2,
+ "bcShape": tuple(x1.shape),
+ "axis": 0,
+ }
+ return create_pdarray(
+ generic_msg(
+ cmd=cmd,
+ args=args,
+ )
+ )