1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
use libnum;
use itertools::free::enumerate;
use imp_prelude::*;
use numeric_util;
use {
LinalgScalar,
};
impl<A, S> ArrayBase<S, Ix>
where S: Data<Elem=A>,
{
/// Compute the dot product of one-dimensional arrays.
///
/// The dot product is a sum of the elementwise products (no conjugation
/// of complex operands, and thus not their inner product).
///
/// **Panics** if the arrays are not of the same length.
pub fn dot<S2>(&self, rhs: &ArrayBase<S2, Ix>) -> A
where S2: Data<Elem=A>,
A: LinalgScalar,
{
self.dot_impl(rhs)
}
fn dot_generic<S2>(&self, rhs: &ArrayBase<S2, Ix>) -> A
where S2: Data<Elem=A>,
A: LinalgScalar,
{
debug_assert_eq!(self.len(), rhs.len());
assert!(self.len() == rhs.len());
if let Some(self_s) = self.as_slice() {
if let Some(rhs_s) = rhs.as_slice() {
return numeric_util::unrolled_dot(self_s, rhs_s);
}
}
let mut sum = A::zero();
for i in 0..self.len() {
unsafe {
sum = sum.clone() + self.uget(i).clone() * rhs.uget(i).clone();
}
}
sum
}
#[cfg(not(feature="rblas"))]
fn dot_impl<S2>(&self, rhs: &ArrayBase<S2, Ix>) -> A
where S2: Data<Elem=A>,
A: LinalgScalar,
{
self.dot_generic(rhs)
}
#[cfg(feature="rblas")]
fn dot_impl<S2>(&self, rhs: &ArrayBase<S2, Ix>) -> A
where S2: Data<Elem=A>,
A: LinalgScalar,
{
use std::any::{Any, TypeId};
use rblas::vector::ops::Dot;
use linalg::AsBlasAny;
// Read pointer to type `A` as type `B`.
//
// **Panics** if `A` and `B` are not the same type
fn cast_as<A: Any + Copy, B: Any + Copy>(a: &A) -> B {
assert_eq!(TypeId::of::<A>(), TypeId::of::<B>());
unsafe {
::std::ptr::read(a as *const _ as *const B)
}
}
// Use only if the vector is large enough to be worth it
if self.len() >= 32 {
debug_assert_eq!(self.len(), rhs.len());
assert!(self.len() == rhs.len());
if let Ok(self_v) = self.blas_view_as_type::<f32>() {
if let Ok(rhs_v) = rhs.blas_view_as_type::<f32>() {
let f_ret = f32::dot(&self_v, &rhs_v);
return cast_as::<f32, A>(&f_ret);
}
}
if let Ok(self_v) = self.blas_view_as_type::<f64>() {
if let Ok(rhs_v) = rhs.blas_view_as_type::<f64>() {
let f_ret = f64::dot(&self_v, &rhs_v);
return cast_as::<f64, A>(&f_ret);
}
}
}
self.dot_generic(rhs)
}
}
impl<A, S> ArrayBase<S, (Ix, Ix)>
where S: Data<Elem=A>,
{
/// Return an array view of row `index`.
///
/// **Panics** if `index` is out of bounds.
pub fn row(&self, index: Ix) -> ArrayView<A, Ix>
{
self.subview(Axis(0), index)
}
/// Return a mutable array view of row `index`.
///
/// **Panics** if `index` is out of bounds.
pub fn row_mut(&mut self, index: Ix) -> ArrayViewMut<A, Ix>
where S: DataMut
{
self.subview_mut(Axis(0), index)
}
/// Return an array view of column `index`.
///
/// **Panics** if `index` is out of bounds.
pub fn column(&self, index: Ix) -> ArrayView<A, Ix>
{
self.subview(Axis(1), index)
}
/// Return a mutable array view of column `index`.
///
/// **Panics** if `index` is out of bounds.
pub fn column_mut(&mut self, index: Ix) -> ArrayViewMut<A, Ix>
where S: DataMut
{
self.subview_mut(Axis(1), index)
}
/// Perform matrix multiplication of rectangular arrays `self` and `rhs`.
///
/// The array shapes must agree in the way that
/// if `self` is *M* × *N*, then `rhs` is *N* × *K*.
///
/// Return a result array with shape *M* × *K*.
///
/// **Panics** if shapes are incompatible.
///
/// ```
/// use ndarray::arr2;
///
/// let a = arr2(&[[1., 2.],
/// [0., 1.]]);
/// let b = arr2(&[[1., 2.],
/// [2., 3.]]);
///
/// assert!(
/// a.mat_mul(&b) == arr2(&[[5., 8.],
/// [2., 3.]])
/// );
/// ```
///
pub fn mat_mul(&self, rhs: &ArrayBase<S, (Ix, Ix)>) -> OwnedArray<A, (Ix, Ix)>
where A: LinalgScalar,
{
// NOTE: Matrix multiplication only defined for Copy types to
// avoid trouble with panicking + and *, and destructors
let ((m, a), (b, n)) = (self.dim, rhs.dim);
let (self_columns, other_rows) = (a, b);
assert!(self_columns == other_rows);
// Avoid initializing the memory in vec -- set it during iteration
// Panic safe because A: Copy
let mut res_elems = Vec::<A>::with_capacity(m as usize * n as usize);
unsafe {
res_elems.set_len(m as usize * n as usize);
}
let mut i = 0;
let mut j = 0;
for rr in &mut res_elems {
unsafe {
*rr = (0..a).fold(libnum::zero::<A>(),
move |s, k| s + *self.uget((i, k)) * *rhs.uget((k, j))
);
}
j += 1;
if j == n {
j = 0;
i += 1;
}
}
unsafe {
ArrayBase::from_vec_dim_unchecked((m, n), res_elems)
}
}
/// Perform the matrix multiplication of the rectangular array `self` and
/// column vector `rhs`.
///
/// The array shapes must agree in the way that
/// if `self` is *M* × *N*, then `rhs` is *N*.
///
/// Return a result array with shape *M*.
///
/// **Panics** if shapes are incompatible.
pub fn mat_mul_col(&self, rhs: &ArrayBase<S, Ix>) -> OwnedArray<A, Ix>
where A: LinalgScalar,
{
let ((m, a), n) = (self.dim, rhs.dim);
let (self_columns, other_rows) = (a, n);
assert!(self_columns == other_rows);
// Avoid initializing the memory in vec -- set it during iteration
let mut res_elems = Vec::<A>::with_capacity(m as usize);
unsafe {
res_elems.set_len(m as usize);
}
for (i, rr) in enumerate(&mut res_elems) {
unsafe {
*rr = (0..a).fold(libnum::zero::<A>(),
move |s, k| s + *self.uget((i, k)) * *rhs.uget(k)
);
}
}
unsafe {
ArrayBase::from_vec_dim_unchecked(m, res_elems)
}
}
}