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operators_broadcasted.nim
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# Copyright 2017 the Arraymancer contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ./data_structure,
./higher_order_applymap,
./shapeshifting,
./math
import complex except Complex64, Complex32
# #########################################################
# # Broadcasting Tensor-Tensor
# # And element-wise multiplication (Hadamard) and division
proc `.+`*[T: SomeNumber|Complex[float32]|Complex[float64]](a, b: Tensor[T]): Tensor[T] {.noInit,inline.} =
## Broadcasted addition for tensors of incompatible but broadcastable shape.
let (tmp_a, tmp_b) = broadcast2(a, b)
result = tmp_a + tmp_b
proc `.-`*[T: SomeNumber|Complex[float32]|Complex[float64]](a, b: Tensor[T]): Tensor[T] {.noInit,inline.} =
## Broadcasted addition for tensors of incompatible but broadcastable shape.
let (tmp_a, tmp_b) = broadcast2(a, b)
result = tmp_a - tmp_b
proc `.*`*[T: SomeNumber|Complex[float32]|Complex[float64]](a, b: Tensor[T]): Tensor[T] {.noInit.} =
## Element-wise multiplication (Hadamard product).
##
## And broadcasted element-wise multiplication.
let (tmp_a, tmp_b) = broadcast2(a, b)
result = map2_inline(tmp_a, tmp_b, x * y)
proc `./`*[T: SomeInteger](a, b: Tensor[T]): Tensor[T] {.noInit.} =
## Tensor element-wise division for integer numbers.
##
## And broadcasted element-wise division.
let (tmp_a, tmp_b) = broadcast2(a, b)
result = map2_inline(tmp_a, tmp_b, x div y)
proc `./`*[T: SomeFloat|Complex[float32]|Complex[float64]](a, b: Tensor[T]): Tensor[T] {.noInit.} =
## Tensor element-wise division for real numbers.
##
## And broadcasted element-wise division.
let (tmp_a, tmp_b) = broadcast2(a, b)
result = map2_inline(tmp_a, tmp_b, x / y )
# ##############################################
# # Broadcasting in-place Tensor-Tensor
proc `.+=`*[T: SomeNumber|Complex[float32]|Complex[float64]](a: var Tensor[T], b: Tensor[T]) =
## Tensor broadcasted in-place addition.
##
## Only the right hand side tensor can be broadcasted.
# shape check done in apply2 proc
let tmp_b = b.broadcast(a.shape)
apply2_inline(a, tmp_b, x + y)
proc `.-=`*[T: SomeNumber|Complex[float32]|Complex[float64]](a: var Tensor[T], b: Tensor[T]) =
## Tensor broadcasted in-place substraction.
##
## Only the right hand side tensor can be broadcasted.
# shape check done in apply2 proc
let tmp_b = b.broadcast(a.shape)
apply2_inline(a, tmp_b, x - y)
proc `.*=`*[T: SomeNumber|Complex[float32]|Complex[float64]](a: var Tensor[T], b: Tensor[T]) =
## Tensor broadcasted in-place multiplication (Hadamard product)
##
## Only the right hand side tensor can be broadcasted
# shape check done in apply2 proc
let tmp_b = b.broadcast(a.shape)
apply2_inline(a, tmp_b, x * y)
proc `./=`*[T: SomeInteger](a: var Tensor[T], b: Tensor[T]) =
## Tensor broadcasted in-place integer division.
##
## Only the right hand side tensor can be broadcasted.
# shape check done in apply2 proc
let tmp_b = b.broadcast(a.shape)
apply2_inline(a, tmp_b, x div y)
proc `./=`*[T: SomeFloat|Complex[float32]|Complex[float64]](a: var Tensor[T], b: Tensor[T]) =
## Tensor broadcasted in-place float division.
##
## Only the right hand side tensor can be broadcasted.
# shape check done in apply2 proc
let tmp_b = b.broadcast(a.shape)
apply2_inline(a, tmp_b, x / y)
# ##############################################
# # Broadcasting Tensor-Scalar and Scalar-Tensor
proc `.+`*[T: SomeNumber|Complex[float32]|Complex[float64]](val: T, t: Tensor[T]): Tensor[T] {.noInit.} =
## Broadcasted addition for tensor + scalar.
result = t.map_inline(x + val)
proc `.+`*[T: SomeNumber|Complex[float32]|Complex[float64]](t: Tensor[T], val: T): Tensor[T] {.noInit.} =
## Broadcasted addition for scalar + tensor.
result = t.map_inline(x + val)
proc `.-`*[T: SomeNumber|Complex[float32]|Complex[float64]](val: T, t: Tensor[T]): Tensor[T] {.noInit.} =
## Broadcasted substraction for tensor - scalar.
result = t.map_inline(val - x)
proc `.-`*[T: SomeNumber|Complex[float32]|Complex[float64]](t: Tensor[T], val: T): Tensor[T] {.noInit.} =
## Broadcasted substraction for scalar - tensor.
result = t.map_inline(x - val)
proc `./`*[T: SomeInteger](val: T, t: Tensor[T]): Tensor[T] {.noInit.} =
## Broadcasted division of an integer by a tensor of integers.
result = t.map_inline(val div x)
proc `./`*[T: SomeFloat|Complex[float32]|Complex[float64]](val: T, t: Tensor[T]): Tensor[T] {.noInit.} =
## Broadcasted division of a float by a tensor of floats.
result = t.map_inline(val / x)
proc `./`*[T: SomeInteger](t: Tensor[T], val: T): Tensor[T] {.noInit.} =
## Broadcasted division of tensor of integers by an integer.
result = t.map_inline(x div val)
proc `./`*[T: SomeFloat|Complex[float32]|Complex[float64]](t: Tensor[T], val: T): Tensor[T] {.noInit.} =
## Broadcasted division of a tensor of floats by a float.
result = t.map_inline(x / val)
proc `.^`*[T: SomeFloat|Complex[float32]|Complex[float64]](t: Tensor[T], exponent: T): Tensor[T] {.noInit.} =
## Compute element-wise exponentiation
result = t.map_inline pow(x, exponent)
# #####################################
# # Broadcasting in-place Tensor-Scalar
proc `.+=`*[T: SomeNumber|Complex[float32]|Complex[float64]](t: var Tensor[T], val: T) =
## Tensor in-place addition with a broadcasted scalar.
t.apply_inline(x + val)
proc `.-=`*[T: SomeNumber|Complex[float32]|Complex[float64]](t: var Tensor[T], val: T) =
## Tensor in-place substraction with a broadcasted scalar.
t.apply_inline(x - val)
proc `.^=`*[T: SomeFloat|Complex[float32]|Complex[float64]](t: var Tensor[T], exponent: T) =
## Compute in-place element-wise exponentiation
t.apply_inline pow(x, exponent)
proc `.*=`*[T: SomeNumber|Complex[float32]|Complex[float64]](t: var Tensor[T], val: T) =
## Tensor in-place multiplication with a broadcasted scalar.
t.apply_inline(x * val)
proc `./=`*[T: SomeNumber|Complex[float32]|Complex[float64]](t: var Tensor[T], val: T) =
## Tensor in-place division with a broadcasted scalar.
t.apply_inline(x / val)