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
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
extern crate ndarray;
pub type NdArray = ndarray::Array<f32, ndarray::IxDyn>;
pub type NdArrayView<'a> = ndarray::ArrayView<'a, f32, ndarray::IxDyn>;
pub use array_gen::*;
#[inline]
pub fn arr_to_shape(arr: &NdArray) -> Vec<usize>
{
arr.iter().map(|&a| a as usize).collect::<Vec<_>>()
}
#[doc(hidden)]
#[inline]
pub fn expand_dims_view<'a>(x: NdArrayView<'a>, axis: usize) -> NdArrayView<'a>
{
let mut shape = x.shape().to_vec();
shape.insert(axis, 1);
x.into_shape(shape).unwrap()
}
#[doc(hidden)]
#[inline]
pub fn expand_dims(x: NdArray, axis: usize) -> NdArray
{
let mut shape = x.shape().to_vec();
shape.insert(axis, 1);
x.into_shape(shape).unwrap()
}
#[doc(hidden)]
#[inline]
pub fn roll_axis(arg: &mut NdArray, to: ndarray::Axis, from: ndarray::Axis)
{
let i = to.index();
let mut j = from.index();
if j > i {
while i != j {
arg.swap_axes(i, j);
j -= 1;
}
} else {
while i != j {
arg.swap_axes(i, j);
j += 1;
}
}
}
#[inline]
pub fn normalize_negative_axis(axis: isize, ndim: usize) -> usize {
if axis < 0 {
(ndim as isize + axis) as usize
} else {
axis as usize
}
}
#[inline]
pub fn normalize_negative_axes(axes: &NdArray, ndim: usize) -> Vec<usize> {
let mut axes_ret: Vec<usize> = Vec::with_capacity(axes.len());
for &axis in axes.iter() {
let axis = if axis < 0. {
(ndim as f32 + axis) as usize
} else {
axis as usize
};
axes_ret.push(axis);
}
axes_ret
}
#[inline]
pub fn sparse_to_dense(arr: &NdArray) -> Vec<usize> {
let mut axes: Vec<usize> = vec![];
for (i, &a) in arr.iter().enumerate() {
if a == 1. {
axes.push(i as usize);
}
}
axes
}
#[doc(hidden)]
#[inline]
pub fn vec_as_shape(x: &NdArray) -> Vec<usize>
{
let mut target = Vec::with_capacity(x.len());
for &a in x.iter() {
target.push(a as usize);
}
target
}
#[doc(hidden)]
#[inline]
pub fn scalar_shape() -> NdArray
{
NdArray::from_shape_vec(ndarray::IxDyn(&[0]), vec![]).unwrap()
}
#[doc(hidden)]
#[inline]
pub fn is_scalar_shape(shape: &[usize]) -> bool {
shape == &[] || shape == &[0]
}
#[doc(hidden)]
#[inline]
pub fn shape_of(x: &NdArray) -> NdArray
{
let shape = x.shape().iter().map(|&a| a as f32).collect::<Vec<f32>>();
let rank = shape.len();
NdArray::from_shape_vec(ndarray::IxDyn(&[rank]), shape).unwrap()
}
#[doc(hidden)]
#[inline]
pub fn into_mat(x: NdArray) -> ndarray::Array<f32, ndarray::Ix2>
{
let (a, b) = {
let shape = x.shape();
(shape[0], shape[1])
};
x.into_shape(ndarray::Ix2(a, b)).unwrap()
}
pub mod array_gen
{
use super::*;
#[inline]
pub fn zeros(shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
NdArray::from_elem(shape, 0.)
}
#[inline]
pub fn ones(shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
NdArray::from_elem(shape, 1.)
}
#[inline]
pub fn from_scalar(val: f32) -> ndarray::Array<f32, ndarray::IxDyn>
{
NdArray::from_elem(ndarray::IxDyn(&[]), val)
}
#[inline]
pub fn permutation(size: usize) -> ndarray::Array1<usize>
{
ArrRng::default().permutation(size)
}
#[inline]
pub fn random_normal(
shape: &[usize],
mean: f64,
stddev: f64,
) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().random_normal(shape, mean, stddev)
}
#[inline]
pub fn random_uniform(
shape: &[usize],
min: f64,
max: f64,
) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().random_uniform(shape, min, max)
}
#[inline]
pub fn standard_normal(shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().standard_normal(shape)
}
#[inline]
pub fn standard_uniform(shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().standard_uniform(shape)
}
#[inline]
pub fn glorot_normal(shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().glorot_normal(shape)
}
#[inline]
pub fn glorot_uniform(shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().glorot_normal(shape)
}
#[inline]
pub fn bernoulli(shape: &[usize], p: f64) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().bernoulli(shape, p)
}
#[inline]
pub fn exponential(shape: &[usize], lambda: f64) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().exponential(shape, lambda)
}
#[inline]
pub fn log_normal(
shape: &[usize],
mean: f64,
stddev: f64,
) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().log_normal(shape, mean, stddev)
}
#[inline]
pub fn gamma(
shape: &[usize],
shape_param: f64,
scale: f64,
) -> ndarray::Array<f32, ndarray::IxDyn>
{
ArrRng::default().gamma(shape, shape_param, scale)
}
}
use std::cell::RefCell;
use rand::{self, Rng, XorShiftRng};
use rand::distributions::IndependentSample;
pub struct ArrRng<R = XorShiftRng> {
rng: RefCell<R>
}
impl Default for ArrRng<XorShiftRng>
{
fn default() -> Self
{
ArrRng {
rng: RefCell::new(rand::weak_rng())
}
}
}
impl<R> ArrRng<R> {
pub fn new(rng: R) -> Self
{
ArrRng {
rng: RefCell::new(rng)
}
}
}
impl<R: Rng> ArrRng<R>
{
#[inline]
fn gen_rnd_array<T>(&self, shape: &[usize], dist: T) -> NdArray
where
T: IndependentSample<f64>,
{
let mut rng = self.rng.borrow_mut();
NdArray::from_shape_fn(shape, |_| dist.ind_sample(&mut *rng) as f32)
}
#[inline]
fn gen_rand_array_f<T, F>(&self, shape: &[usize], dist: T, f: F) -> NdArray
where
T: IndependentSample<f64>,
F: Fn(f64) -> f64,
{
let mut rng = self.rng.borrow_mut();
NdArray::from_shape_fn(shape, |_| f(dist.ind_sample(&mut *rng)) as f32)
}
#[inline]
pub fn permutation(&mut self, size: usize) -> ndarray::Array1<usize>
{
let mut data: Vec<usize> = (0..size).collect();
let slice = data.as_mut_slice();
let mut rng = self.rng.borrow_mut();
rng.shuffle(slice);
ndarray::Array1::<usize>::from_vec(slice.to_vec())
}
#[inline]
pub fn random_normal(
&self,
shape: &[usize],
mean: f64,
stddev: f64,
) -> ndarray::Array<f32, ndarray::IxDyn>
{
let normal = rand::distributions::Normal::new(mean, stddev);
self.gen_rnd_array(shape, normal)
}
#[inline]
pub fn random_uniform(
&self,
shape: &[usize],
min: f64,
max: f64,
) -> ndarray::Array<f32, ndarray::IxDyn>
{
let range = rand::distributions::Range::new(min, max);
self.gen_rnd_array(shape, range)
}
#[inline]
pub fn standard_normal(&self, shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
let normal = rand::distributions::Normal::new(0., 1.);
self.gen_rnd_array(shape, normal)
}
#[inline]
pub fn standard_uniform(&self, shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
let dist = rand::distributions::Range::new(0., 1.);
self.gen_rnd_array(shape, dist)
}
#[inline]
pub fn glorot_normal(&self, shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
assert_eq!(shape.len(), 2);
let s = 1. / (shape[0] as f64).sqrt();
let normal = rand::distributions::Normal::new(0., s);
self.gen_rnd_array(shape, normal)
}
#[inline]
pub fn glorot_uniform(&self, shape: &[usize]) -> ndarray::Array<f32, ndarray::IxDyn>
{
assert_eq!(shape.len(), 2);
let s = (6. / shape[0] as f64).sqrt();
let uniform = rand::distributions::Range::new(-s, s);
self.gen_rnd_array(shape, uniform)
}
#[inline]
pub fn bernoulli(&self, shape: &[usize], p: f64) -> ndarray::Array<f32, ndarray::IxDyn>
{
let dist = rand::distributions::Range::new(0., 1.);
self.gen_rand_array_f(shape, dist, |a| (a < p) as i64 as f64)
}
#[inline]
pub fn exponential(&self, shape: &[usize], lambda: f64) -> ndarray::Array<f32, ndarray::IxDyn>
{
let dist = rand::distributions::Exp::new(lambda);
self.gen_rnd_array(shape, dist)
}
#[inline]
pub fn log_normal(
&self,
shape: &[usize],
mean: f64,
stddev: f64,
) -> ndarray::Array<f32, ndarray::IxDyn>
{
let dist = rand::distributions::LogNormal::new(mean, stddev);
self.gen_rnd_array(shape, dist)
}
#[inline]
pub fn gamma(
&self,
shape: &[usize],
shape_param: f64,
scale: f64,
) -> ndarray::Array<f32, ndarray::IxDyn>
{
let dist = rand::distributions::Gamma::new(shape_param, scale);
self.gen_rnd_array(shape, dist)
}
}