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0722dd26ff
Committing on behalf of Steve Canon again. We'll hold off on innovating till we have a more complete story here. Swift SVN r28385
735 lines
23 KiB
Swift
735 lines
23 KiB
Swift
//===----------------------------------------------------------*- swift -*-===//
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//
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// This source file is part of the Swift.org open source project
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//
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// Copyright (c) 2014 - 2015 Apple Inc. and the Swift project authors
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// Licensed under Apache License v2.0 with Runtime Library Exception
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//
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// See http://swift.org/LICENSE.txt for license information
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// See http://swift.org/CONTRIBUTORS.txt for the list of Swift project authors
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//
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//===----------------------------------------------------------------------===//
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// simd.h overlays for Swift
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//===----------------------------------------------------------------------===//
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import Darwin
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import simd
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% component = ['x','y','z','w']
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% scalar_types = ['Float','Double','Int32']
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% ctype = { 'Float':'float', 'Double':'double', 'Int32':'int' }
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% floating_types = ['Float','Double']
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% cardinal = { 2:'two', 3:'three', 4:'four'}
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% hash_scales = ['1', '3', '5', '11']
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% one_minus_ulp = { 'Float':'0x1.fffffep-1', 'Double':'0x1.fffffffffffffp-1' }
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% for type in scalar_types:
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% for size in [2, 3, 4]:
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// Workaround <rdar://problem/18900352>
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% vectype = ctype[type] + str(size)
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% vecsize = (8 if type == 'Double' else 4)*(2 if size == 2 else 4)
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/// A vector of ${cardinal[size]} `${type}`. This corresponds to the C and
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/// Obj-C type `vector_${vectype}` and the C++ type `simd::${vectype}`.
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@_alignment(${vecsize})
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public struct ${vectype} :
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ArrayLiteralConvertible, CustomDebugStringConvertible {
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public var ${', '.join(component[:size])}: ${type}
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% if size == 3:
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/// Three-element vectors require padding so that their size is the same as
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/// the size of the corresponding C, Obj-C, and C++ types.
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internal let _padding: ${type} = 0
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% end
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/// Initialize to the zero vector.
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public init() { self.init(0) }
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/// Initialize a vector with the specified elements.
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public init(${', '.join(map(lambda c: c + ': ' + type, component[:size]))}) {
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% for c in component[:size]:
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self.${c} = ${c}
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% end
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}
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/// Initialize to a vector with all elements equal to `scalar`.
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public init(_ scalar: ${type}) {
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self.init(${', '.join(map(lambda c: c + ': scalar', component[:size]))})
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}
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/// Initialize to a vector with elements taken from `array`.
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///
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/// - Precondition: `array` must have exactly ${cardinal[size]} elements.
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public init(_ array: [${type}]) {
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_precondition(array.count == ${size},
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"${vectype} requires a ${cardinal[size]}-element array")
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self.init(${', '.join(map(lambda i:
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component[i] + ': array[' + str(i) + ']',
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range(size)))})
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}
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/// Initialize using `arrayLiteral`.
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///
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/// - precondition: the array literal must exactly ${cardinal[size]} elements.
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public init(arrayLiteral elements: ${type}...) { self.init(elements) }
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/// Access individual elements of the vector via subscript.
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public subscript(index: Int) -> ${type} {
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get {
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switch index {
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% for i in range(size):
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case ${i}: return ${component[i]}
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% end
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default: _preconditionFailure("Vector index out of range")
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}
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}
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set(value) {
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switch index {
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% for i in range(size):
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case ${i}: ${component[i]} = value
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% end
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default: _preconditionFailure("Vector index out of range")
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}
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}
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}
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/// Debug string representation
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public var debugDescription: String {
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return "${vectype}(\(self._descriptionAsArray))"
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}
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/// Helper function for vector and matrix debug representations
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internal var _descriptionAsArray: String {
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get {
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return "[${', '.join(map(lambda c:
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'\\(' + c + ')',
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component[:size]))}]"
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}
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}
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}
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/// Vector sum of `lhs` and `rhs`.
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@inline(__always)
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public func +(lhs: ${vectype}, rhs: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ':lhs.' + c + '+rhs.' + c,
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component[:size]))})
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}
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/// Vector difference of `lhs` and `rhs`.
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@inline(__always)
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public func -(lhs: ${vectype}, rhs: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ':lhs.' + c + '-rhs.' + c,
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component[:size]))})
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}
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/// Negation of `rhs`.
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@inline(__always)
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public prefix func -(rhs: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ':-rhs.' + c,
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component[:size]))})
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}
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/// Elementwise product of `lhs` and `rhs`. A.k.a. the Hadamard or Schur
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/// product of the two vectors.
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@inline(__always)
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public func *(lhs: ${vectype}, rhs: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ':lhs.' + c + '*rhs.' + c,
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component[:size]))})
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}
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/// Elementwise quotient of `lhs` and `rhs`. This is the inverse operation
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/// of the elementwise product.
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@inline(__always)
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public func /(lhs: ${vectype}, rhs: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ':lhs.' + c + '/rhs.' + c,
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component[:size]))})
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}
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/// Add `rhs` to `lhs`.
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@inline(__always)
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public func +=(inout lhs: ${vectype}, rhs: ${vectype}) -> Void {
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lhs = lhs + rhs
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}
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/// Subtract `rhs` from `lhs`.
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@inline(__always)
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public func -=(inout lhs: ${vectype}, rhs: ${vectype}) -> Void {
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lhs = lhs - rhs
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}
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/// Multiply `lhs` by `rhs` (elementwise).
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@inline(__always)
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public func *=(inout lhs: ${vectype}, rhs: ${vectype}) -> Void {
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lhs = lhs * rhs
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}
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/// Divide `lhs` by `rhs` (elementwise).
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@inline(__always)
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public func /=(inout lhs: ${vectype}, rhs: ${vectype}) -> Void {
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lhs = lhs / rhs
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}
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/// Scalar-Vector product.
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@inline(__always)
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public func *(lhs: ${type}, rhs: ${vectype}) -> ${vectype} {
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return ${vectype}(lhs) * rhs
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}
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/// Scalar-Vector product.
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@inline(__always)
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public func *(lhs: ${vectype}, rhs: ${type}) -> ${vectype} {
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return lhs * ${vectype}(rhs)
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}
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/// Scales `lhs` by `rhs`.
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@inline(__always)
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public func *=(inout lhs: ${vectype}, rhs: ${type}) -> Void {
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lhs = lhs * rhs
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}
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/// Elementwise absolute value of a vector. The result is a vector of the same
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/// length with all elements positive.
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@inline(__always)
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public func abs(x: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': abs(x.' + c + ')', \
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component[:size]))})
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}
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/// Elementwise minimum of two vectors. Each component of the result is the
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/// smaller of the corresponding component of the inputs.
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@inline(__always)
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public func min(x: ${vectype}, _ y: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': min(x.' + c + ',y.' + c + ')', \
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component[:size]))})
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}
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/// Elementwise maximum of two vectors. Each component of the result is the
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/// larger of the corresponding component of the inputs.
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@inline(__always)
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public func max(x: ${vectype}, _ y: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': max(x.' + c + ',y.' + c + ')', \
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component[:size]))})
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}
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/// Vector-scalar minimum. Each component of the result is the minimum of the
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/// corresponding element of the input vector and the scalar.
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@inline(__always)
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public func min(vector: ${vectype}, _ scalar: ${type}) -> ${vectype} {
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return min(vector, ${vectype}(scalar))
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}
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/// Vector-scalar maximum. Each component of the result is the maximum of the
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/// corresponding element of the input vector and the scalar.
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@inline(__always)
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public func max(vector: ${vectype}, _ scalar: ${type}) -> ${vectype} {
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return max(vector, ${vectype}(scalar))
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}
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/// Each component of the result is the corresponding element of `x` clamped to
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/// the range formed by the corresponding elements of `min` and `max`. Any
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/// lanes of `x` that contain NaN will end up with the `min` value.
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@inline(__always)
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public func clamp(x: ${vectype},
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min: ${vectype},
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max: ${vectype})
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-> ${vectype} {
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return simd.min(simd.max(x, min), max)
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}
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/// Clamp each element of `x` to the range [`min`, max]. If any lane of `x` is
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/// NaN, the corresponding lane of the result is `min`.
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@inline(__always)
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public func clamp(x: ${vectype},
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min: ${type},
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max: ${type})
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-> ${vectype} {
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return simd.min(simd.max(x, min), max)
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}
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/// Sum of the elements of the vector.
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@inline(__always)
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public func reduce_add(x: ${vectype}) -> ${type} {
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return ${' + '.join(map(lambda x:'x.'+x, component[:size]))}
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}
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/// Minimum element of the vector.
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@inline(__always)
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public func reduce_min(x: ${vectype}) -> ${type} {
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return min(${', '.join(map(lambda x:'x.'+x, component[:size]))})
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}
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/// Maximum element of the vector.
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@inline(__always)
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public func reduce_max(x: ${vectype}) -> ${type} {
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return max(${', '.join(map(lambda x:'x.'+x, component[:size]))})
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}
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% if type in floating_types:
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/// Sign of a vector. Each lane contains -1 if the corresponding lane of `x`
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/// is less than zero, +1 if the corresponding lane of `x` is greater than
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/// zero, and 0 otherwise.
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@inline(__always)
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public func sign(x: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': sign(x.' + c + ')', \
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component[:size]))})
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}
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/// Linear interpolation between `x` (at `t=0`) and `y` (at `t=1`). May be
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/// used with `t` outside of [0, 1] as well.
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@inline(__always)
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public func mix(x: ${vectype}, _ y: ${vectype}, t: ${vectype}) -> ${vectype} {
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return x + t*(y-x)
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}
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/// Linear interpolation between `x` (at `t=0`) and `y` (at `t=1`). May be
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/// used with `t` outside of [0, 1] as well.
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@inline(__always)
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public func mix(x: ${vectype}, _ y: ${vectype}, t: ${type}) -> ${vectype} {
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return x + t*(y-x)
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}
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/// Elementwise reciprocal.
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@inline(__always)
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public func recip(x: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': recip(x.' + c + ')', \
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component[:size]))})
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}
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/// Elementwise reciprocal square root.
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@inline(__always)
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public func rsqrt(x: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': rsqrt(x.' + c + ')', \
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component[:size]))})
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}
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/// Alternate name for minimum of two floating-point vectors.
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@inline(__always)
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public func fmin(x: ${vectype}, _ y: ${vectype}) -> ${vectype} {
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return min(x, y)
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}
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/// Alternate name for maximum of two floating-point vectors.
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@inline(__always)
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public func fmax(x: ${vectype}, _ y: ${vectype}) -> ${vectype} {
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return max(x, y)
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}
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/// Each element of the result is the smallest integral value greater than or
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/// equal to the corresponding element of the input.
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@inline(__always)
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public func ceil(x: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': ceil(x.' + c + ')', \
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component[:size]))})
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}
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/// Each element of the result is the largest integral value less than or equal
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/// to the corresponding element of the input.
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@inline(__always)
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public func floor(x: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': floor(x.' + c + ')', \
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component[:size]))})
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}
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/// Each element of the result is the closest integral value with magnitude
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/// less than or equal to that of the corresponding element of the input.
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@inline(__always)
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public func trunc(x: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': trunc(x.' + c + ')', \
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component[:size]))})
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}
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/// `x - floor(x)`, clamped to lie in the range [0,1). Without this clamp step,
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/// the result would be 1.0 when `x` is a very small negative number, which may
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/// result in out-of-bounds table accesses in common usage.
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@inline(__always)
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public func fract(x: ${vectype}) -> ${vectype} {
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return fmin(x - floor(x), ${vectype}(${one_minus_ulp[type]}))
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}
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/// 0.0 if `x < edge`, and 1.0 otherwise.
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@inline(__always)
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public func step(x: ${vectype}, edge: ${vectype}) -> ${vectype} {
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return ${vectype}(${', '.join(map(lambda c: \
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c + ': step(x.' + c + ', edge: edge.' + c + ')', \
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component[:size]))})
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}
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/// 0.0 if `x < edge0`, 1.0 if `x > edge1`, and cubic interpolation between
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/// 0 and 1 in the interval [edge0, edge1].
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@inline(__always)
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public func smoothstep(x: ${vectype},
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edge0: ${vectype},
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edge1: ${vectype})
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-> ${vectype} {
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let t = clamp((x-edge0)/(edge1-edge0), min: 0, max: 1)
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return t*t*(${vectype}(3) - 2*t)
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}
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/// Dot product of `x` and `y`.
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@inline(__always)
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public func dot(x: ${vectype}, _ y: ${vectype}) -> ${type} {
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return reduce_add(x*y)
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}
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/// Projection of `x` onto `y`.
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@inline(__always)
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public func project(x: ${vectype}, _ y: ${vectype}) -> ${vectype} {
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return dot(x,y)/dot(y,y)*y
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}
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/// Length of `x`, squared. This is more efficient to compute than the length,
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/// so you should use it if you only need to compare lengths to each other.
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/// I.e. instead of writing:
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///
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/// if (length(x) < length(y)) { ... }
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///
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/// use:
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///
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/// if (length_squared(x) < length_squared(y)) { ... }
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///
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/// Doing it this way avoids one or two square roots, which is a fairly costly
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/// operation.
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@inline(__always)
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public func length_squared(x: ${vectype}) -> ${type} {
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return dot(x,x)
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}
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/// Length (two-norm or "Euclidean norm") of `x`.
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@inline(__always)
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public func length(x: ${vectype}) -> ${type} {
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return sqrt(length_squared(x))
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}
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/// The one-norm (or "taxicab norm") of `x`.
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@inline(__always)
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public func norm_one(x: ${vectype}) -> ${type} {
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return reduce_add(abs(x))
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}
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/// The infinity-norm (or "sup norm") of `x`.
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@inline(__always)
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public func norm_inf(x: ${vectype}) -> ${type} {
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return reduce_max(abs(x))
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}
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/// Distance between `x` and `y`, squared.
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@inline(__always)
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public func distance_squared(x: ${vectype}, y: ${vectype}) -> ${type} {
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return length_squared(x - y)
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}
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/// Distance between `x` and `y`.
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@inline(__always)
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public func distance(x: ${vectype}, y: ${vectype}) -> ${type} {
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return length(x - y)
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}
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/// Unit vector pointing in the same direction as `x`. normalize(0) is 0.
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@inline(__always)
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public func normalize(x: ${vectype}) -> ${vectype} {
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return x * rsqrt(length_squared(x))
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}
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/// `x` reflected through the hyperplane with unit normal vector `n`, passing
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/// through the origin. E.g. if `x` is [1,2,3] and `n` is [0,0,1], the result
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/// is [1,2,-3].
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@inline(__always)
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public func reflect(x: ${vectype}, n: ${vectype}) -> ${vectype} {
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return x - 2*dot(x,n)*n
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}
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/// The refraction direction given unit incident vector `x`, unit surface
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/// normal `n`, and index of refraction `eta`. If the angle between the
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/// incident vector and the surface is so small that total internal reflection
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/// occurs, zero is returned.
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@inline(__always)
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public func refract(x: ${vectype},
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n: ${vectype},
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eta: ${type})
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-> ${vectype} {
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let k = 1 - eta*eta*(1 - dot(x,n)*dot(x,n))
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if k >= 0 { return eta*x - (eta*dot(x,n) + sqrt(k))*n }
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return ${vectype}(0)
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}
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% end # if type in floating_types
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% end # for size in [2, 3, 4]
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% if type in floating_types:
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// Scalar versions of common operations:
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/// Returns -1 if `x < 0`, +1 if `x > 0`, and 0 otherwise (`sign(NaN)` is 0).
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@inline(__always)
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public func sign(x: ${type}) -> ${type} {
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return x < 0 ? -1 : (x > 0 ? 1 : 0)
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}
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/// Reciprocal.
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@inline(__always)
|
|
public func recip(x: ${type}) -> ${type} { return 1/x }
|
|
|
|
/// Reciprocal square root.
|
|
@inline(__always)
|
|
public func rsqrt(x: ${type}) -> ${type} { return 1/sqrt(x) }
|
|
|
|
/// Returns 0.0 if `x < edge`, and 1.0 otherwise.
|
|
@inline(__always)
|
|
public func step(x: ${type}, edge: ${type}) -> ${type} {
|
|
return x < edge ? 0.0 : 1.0
|
|
}
|
|
|
|
/// Interprets two two-dimensional vectors as three-dimensional vectors in the
|
|
/// xy-plane and computes their cross product, which lies along the z-axis.
|
|
@inline(__always)
|
|
public func cross(x: ${ctype[type]}2,
|
|
_ y: ${ctype[type]}2)
|
|
-> ${ctype[type]}3 {
|
|
return ${ctype[type]}3(x: 0, y: 0, z: x.x*y.y - x.y*y.x)
|
|
}
|
|
|
|
/// Cross-product of two three-dimensional vectors. The resulting vector is
|
|
/// perpendicular to the plane determined by `x` and `y`, with length equal to
|
|
/// the oriented area of the parallelogram they determine.
|
|
@inline(__always)
|
|
public func cross(x: ${ctype[type]}3,
|
|
_ y: ${ctype[type]}3)
|
|
-> ${ctype[type]}3 {
|
|
return ${ctype[type]}3(x: x.y*y.z - x.z*y.y,
|
|
y: x.z*y.x - x.x*y.z,
|
|
z: x.x*y.y - x.y*y.x)
|
|
}
|
|
% end # type in floating_types
|
|
% end # for type in scalar_types
|
|
|
|
% for type in floating_types:
|
|
% for rows in [2,3,4]:
|
|
// Workaround <rdar://problem/18900352>
|
|
% for cols in [2,3,4]:
|
|
// Workaround <rdar://problem/18900352>
|
|
% mattype = ctype[type] + str(cols) + 'x' + str(rows)
|
|
% diagsize = rows if rows < cols else cols
|
|
% coltype = ctype[type] + str(rows)
|
|
% rowtype = ctype[type] + str(cols)
|
|
% diagtype = ctype[type] + str(diagsize)
|
|
% transtype = ctype[type] + str(rows) + 'x' + str(cols)
|
|
% cmatrix = 'matrix_' + mattype
|
|
|
|
public struct ${mattype} : CustomDebugStringConvertible {
|
|
|
|
internal var _columns: (${', '.join([coltype]*cols)})
|
|
|
|
/// Initialize matrix to zero.
|
|
public init() {
|
|
% for i in range(cols):
|
|
_columns.${i} = ${coltype}()
|
|
% end
|
|
}
|
|
|
|
/// Initialize matrix to have `scalar` on main diagonal, zeros elsewhere.
|
|
public init(_ scalar: ${type}) {
|
|
self.init(diagonal: ${diagtype}(scalar))
|
|
}
|
|
|
|
/// Initialize matrix to have specified `diagonal`, and zeros elsewhere.
|
|
public init(diagonal: ${diagtype}) {
|
|
% for i in range(cols):
|
|
self._columns.${i} = ${coltype}()
|
|
% end
|
|
% for i in range(diagsize):
|
|
self._columns.${i}.${component[i]} = diagonal.${component[i]}
|
|
% end
|
|
}
|
|
|
|
/// Initialize matrix to have specified `columns`.
|
|
public init(_ columns: [${coltype}]) {
|
|
_precondition(columns.count == ${cols}, "Requires array of ${cols} vectors")
|
|
% for i in range(cols):
|
|
self._columns.${i} = columns[${i}]
|
|
% end
|
|
}
|
|
|
|
/// Initialize matrix to have specified `rows`.
|
|
public init(rows: [${rowtype}]) {
|
|
_precondition(rows.count == ${rows}, "Requires array of ${rows} vectors")
|
|
% for i in range(cols):
|
|
self._columns.${i} = [${', '.join(map(lambda j:
|
|
'rows[' + str(j) + '].' + component[i],
|
|
range(rows)))}]
|
|
% end
|
|
}
|
|
|
|
/// Initialize matrix to have specified `columns`.
|
|
internal init(${', '.join(map(lambda i:
|
|
'_ col' + str(i) + ': ' + coltype,
|
|
range(cols)))}) {
|
|
% for i in range(cols):
|
|
self._columns.${i} = col${i}
|
|
% end
|
|
}
|
|
|
|
/// Initialize matrix from corresponding C matrix type.
|
|
public init(_ cmatrix: ${cmatrix}) {
|
|
self = unsafeBitCast(cmatrix, ${mattype}.self)
|
|
}
|
|
|
|
/// Get the matrix as the corresponding C matrix type.
|
|
public var cmatrix: ${cmatrix} {
|
|
get { return unsafeBitCast(self, ${cmatrix}.self) }
|
|
}
|
|
|
|
/// Access to individual columns.
|
|
public subscript(column: Int) -> ${coltype} {
|
|
get {
|
|
switch(column) {
|
|
% for i in range(cols):
|
|
case ${i}: return _columns.${i}
|
|
% end
|
|
default: _preconditionFailure("Column index out of range")
|
|
}
|
|
}
|
|
set (value) {
|
|
switch(column) {
|
|
% for i in range(cols):
|
|
case ${i}: _columns.${i} = value
|
|
% end
|
|
default: _preconditionFailure("Column index out of range")
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Access to individual elements.
|
|
public subscript(column: Int, row: Int) -> ${type} {
|
|
get { return self[column][row] }
|
|
set (value) { self[column][row] = value }
|
|
}
|
|
|
|
public var debugDescription: String {
|
|
return "${mattype}([${', '.join(map(lambda i: \
|
|
'\(_columns.' + str(i) + '._descriptionAsArray)',
|
|
range(cols)))}])"
|
|
}
|
|
|
|
/// Transpose of the matrix.
|
|
public var transpose: ${transtype} {
|
|
get {
|
|
return ${transtype}([
|
|
% for i in range(rows):
|
|
[${', '.join(map(lambda j: \
|
|
'self[' + str(j) + ',' + str(i) + ']', \
|
|
range(cols)))}],
|
|
% end # for i in range(rows)
|
|
])
|
|
}
|
|
}
|
|
|
|
% if rows == cols:
|
|
/// Inverse of the matrix if it exists, otherwise the contents of the
|
|
/// resulting matrix are undefined.
|
|
public var inverse: ${mattype} {
|
|
get {
|
|
% inverse_func = '__invert_' + ('f' if type == 'Float' else 'd') + str(cols)
|
|
return ${mattype}(${inverse_func}(self.cmatrix))
|
|
}
|
|
}
|
|
% end
|
|
}
|
|
|
|
/// Sum of two matrices.
|
|
public func +(lhs: ${mattype}, rhs: ${mattype}) -> ${mattype} {
|
|
return ${mattype}(${', '.join(map(lambda i: \
|
|
'lhs._columns.'+str(i)+' + rhs._columns.'+str(i), \
|
|
range(cols)))})
|
|
}
|
|
|
|
/// Negation of a matrix.
|
|
public prefix func -(rhs: ${mattype}) -> ${mattype} {
|
|
return ${mattype}(${', '.join(map(lambda i: \
|
|
'-rhs._columns.'+str(i), \
|
|
range(cols)))})
|
|
}
|
|
|
|
/// Difference of two matrices.
|
|
public func -(lhs: ${mattype}, rhs: ${mattype}) -> ${mattype} {
|
|
return ${mattype}(${', '.join(map(lambda i: \
|
|
'lhs._columns.'+str(i)+' - rhs._columns.'+str(i), \
|
|
range(cols)))})
|
|
}
|
|
|
|
public func +=(inout lhs: ${mattype}, rhs: ${mattype}) -> Void {
|
|
lhs = lhs + rhs
|
|
}
|
|
|
|
public func -=(inout lhs: ${mattype}, rhs: ${mattype}) -> Void {
|
|
lhs = lhs - rhs;
|
|
}
|
|
|
|
/// Scalar-Matrix multiplication.
|
|
public func *(lhs: ${type}, rhs: ${mattype}) -> ${mattype} {
|
|
return ${mattype}(${', '.join(map(lambda i: \
|
|
'lhs*rhs._columns.'+str(i), \
|
|
range(cols)))})
|
|
}
|
|
|
|
/// Matrix-Scalar multiplication.
|
|
public func *(lhs: ${mattype}, rhs: ${type}) -> ${mattype} {
|
|
return rhs*lhs
|
|
}
|
|
|
|
public func *=(inout lhs: ${mattype}, rhs: ${type}) -> Void {
|
|
lhs = lhs*rhs
|
|
}
|
|
|
|
/// Matrix-Vector multiplication. Keep in mind that matrix types are named
|
|
/// `${type}NxM` where `N` is the number of *columns* and `M` is the number of
|
|
/// *rows*, so we multiply a `${type}3x2 * ${type}3` to get a `${type}2`, for
|
|
/// example.
|
|
public func *(lhs: ${mattype}, rhs: ${rowtype}) -> ${coltype} {
|
|
return ${' + '.join(map(lambda i: \
|
|
'lhs._columns.'+str(i)+'*rhs.'+component[i], \
|
|
range(cols)))}
|
|
}
|
|
|
|
/// Vector-Matrix multiplication.
|
|
public func *(lhs: ${coltype}, rhs: ${mattype}) -> ${rowtype} {
|
|
return ${rowtype}(${', '.join(map(lambda i: \
|
|
component[i]+': dot(lhs, rhs._columns.'+str(i)+')', \
|
|
range(cols)))})
|
|
}
|
|
|
|
% for k in [2,3,4]:
|
|
/// Matrix multiplication (the "usual" matrix product, not the elementwise
|
|
/// product).
|
|
% lhstype = ctype[type] + str(k) + 'x' + str(rows)
|
|
% rhstype = ctype[type] + str(cols) + 'x' + str(k)
|
|
public func *(lhs: ${lhstype}, rhs: ${rhstype}) -> ${mattype} {
|
|
return ${mattype}(${', '.join(map(lambda i: \
|
|
'lhs*rhs._columns.'+str(i), \
|
|
range(cols)))})
|
|
}
|
|
|
|
% end # for k in [2,3,4]
|
|
|
|
% rhstype = ctype[type] + str(cols) + 'x' + str(cols)
|
|
/// Matrix multiplication (the "usual" matrix product, not the elementwise
|
|
/// product).
|
|
public func *=(inout lhs: ${mattype}, rhs: ${rhstype}) -> Void {
|
|
lhs = lhs*rhs
|
|
}
|
|
|
|
% end # for cols in [2,3,4]
|
|
% end # for rows in [2,3,4]
|
|
% end # for type in floating_types
|