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
|
"""
Bounds handling.
Use bounds(low, high, style) to create a bounds handling object. This
function operates on a point x, transforming it so that all dimensions
are within the bounds. Options are available, including reflecting,
wrapping, clipping or randomizing the point, or ignoring the bounds.
The returned bounds object should have an apply(x) method which
transforms the point *x*.
"""
__all__ = ["make_bounds_handler", "ReflectBounds", "ClipBounds",
"FoldBounds", "RandomBounds", "IgnoreBounds"]
import numpy as np
from numpy import inf, isinf
from . import util
from .compiled import dll
def make_bounds_handler(bounds, style='reflect'):
"""
Return a bounds object which can update the bounds.
Bounds handling *style* name is one of::
reflect: reflect off the boundary
clip: stop at the boundary
fold: wrap values to the other side of the boundary
randomize: move to a random point in the bounds
none: ignore the bounds
With semi-infinite intervals folding and randomizing aren't well
defined, and reflection is used instead.
With finite intervals the the reflected or folded point may still be
outside the bounds (which can happen if the step size is too large),
and a random uniform value is used instead.
"""
if bounds is None:
return IgnoreBounds()
low, high = bounds
# Do boundary handling -- what to do when points fall outside bound
style = style.lower()
if style == 'reflect':
f = ReflectBounds(low, high)
elif style == 'clip':
f = ClipBounds(low, high)
elif style == 'fold':
f = FoldBounds(low, high)
elif style == 'randomize':
f = RandomBounds(low, high)
elif style == 'none' or style is None:
f = IgnoreBounds()
else:
raise ValueError("bounds style %s is not valid" % style)
return f
class Bounds(object):
"""
Base class for all times of bounds objects.
"""
c_interface = None # type: Callable[[int, int, Any, Any, Any], None]
low = None # type: np.ndarray
high = None # type: np.ndarray
def apply(self, x):
"""Force x values within bounds"""
raise NotImplementedError
def __call__(self, pop):
"""Force x values within bounds for each member of the population"""
if self.c_interface is not None:
self.c_interface(len(pop), len(self.low), pop.ctypes,
self.low.ctypes, self.high.ctypes)
else:
for x in pop:
self.apply(x)
return pop
class ReflectBounds(Bounds):
"""
Reflect parameter values into bounded region
"""
c_interface = dll.bounds_reflect if dll else None
def __init__(self, low, high):
self.low, self.high = [np.ascontiguousarray(v, 'd') for v in (low, high)]
def apply(self, y):
"""
Update x so all values lie within bounds
Returns x for convenience. E.g., y = bounds.apply(x+0)
"""
minn, maxn = self.low, self.high
# Reflect points which are out of bounds
idx = y < minn
y[idx] = 2*minn[idx] - y[idx]
idx = y > maxn
y[idx] = 2*maxn[idx] - y[idx]
# Randomize points which are still out of bounds
idx = (y < minn) | (y > maxn)
y[idx] = minn[idx] + util.rng.rand(sum(idx))*(maxn[idx]-minn[idx])
return y
class ClipBounds(Bounds):
"""
Clip values to bounded region
"""
c_interface = dll.bounds_clip if dll else None
def __init__(self, low, high):
self.low, self.high = [np.ascontiguousarray(v, 'd') for v in (low, high)]
def apply(self, y):
minn, maxn = self.low, self.high
idx = y < minn
y[idx] = minn[idx]
idx = y > maxn
y[idx] = maxn[idx]
return y
class FoldBounds(Bounds):
"""
Wrap values into the bounded region
"""
c_interface = dll.bounds_fold if dll else None
def __init__(self, low, high):
self.low, self.high = [np.ascontiguousarray(v, 'd') for v in (low, high)]
def apply(self, y):
minn, maxn = self.low, self.high
# Deal with semi-infinite cases using reflection
idx = (y < minn) & isinf(maxn)
y[idx] = 2*minn[idx] - y[idx]
idx = (y > maxn) & isinf(minn)
y[idx] = 2*maxn[idx] - y[idx]
# Wrap points which are out of bounds
idx = y < minn
y[idx] = maxn[idx] - (minn[idx] - y[idx])
idx = y > maxn
y[idx] = minn[idx] + (y[idx] - maxn[idx])
# Randomize points which are still out of bounds
idx = (y < minn) | (y > maxn)
y[idx] = minn[idx] + util.rng.rand(sum(idx))*(maxn[idx]-minn[idx])
return y
class RandomBounds(Bounds):
"""
Randomize values into the bounded region
"""
c_interface = dll.bounds_random if dll else None
def __init__(self, low, high):
self.low, self.high = [np.ascontiguousarray(v, 'd') for v in (low, high)]
def apply(self, y):
minn, maxn = self.low, self.high
# Deal with semi-infinite cases using reflection
idx = (y < minn) & isinf(maxn)
y[idx] = 2*minn[idx] - y[idx]
idx = (y > maxn) & isinf(minn)
y[idx] = 2*maxn[idx] - y[idx]
# The remainder are selected uniformly from the bounded region
idx = (y < minn) | (y > maxn)
y[idx] = minn[idx] + util.rng.rand(sum(idx))*(maxn[idx]-minn[idx])
return y
class IgnoreBounds(Bounds):
"""
Leave values outside the bounded region
"""
c_interface = dll.bounds_ignore if dll else None
def __init__(self, low=None, high=None):
self.low, self.high = [np.ascontiguousarray(v, 'd') for v in (low, high)]
def apply(self, y):
return y
def test():
"""bounds handlers test"""
from numpy.linalg import norm
from numpy import array
bounds = list(zip([5, 10], [-inf, -10], [-5, inf], [-inf, inf]))
v = np.ascontiguousarray([6, -12, 6, -12], 'd')
for t in 'none', 'reflect', 'clip', 'fold', 'randomize':
assert norm(make_bounds_handler(bounds, t).apply(v+0) - v) == 0
v = np.ascontiguousarray([12, 12, -12, -12], 'd')
for t in 'none', 'reflect', 'clip', 'fold':
w = make_bounds_handler(bounds, t)
assert norm(w(array([v, v, v])) - w.apply(v+0)) == 0
assert norm(make_bounds_handler(bounds, 'none').apply(v+0) - v) == 0
assert norm(make_bounds_handler(bounds, 'reflect').apply(v+0)
- [8, -32, 2, -12]) == 0
assert norm(make_bounds_handler(bounds, 'clip').apply(v+0)
- [10, -10, -5, -12]) == 0
assert norm(make_bounds_handler(bounds, 'fold').apply(v+0)
- [7, -32, 2, -12]) == 0
w = make_bounds_handler(bounds, 'randomize').apply(v+0)
assert 5 <= w[0] <= 10 and w[1] == -32 and w[2] == 2 and w[3] == -12
v = np.ascontiguousarray([20, 1, 1, 1], 'd')
w = make_bounds_handler(bounds, 'reflect').apply(v+0)
assert 5 <= w[0] <= 10
v = np.ascontiguousarray([20, 1, 1, 1], 'd')
w = make_bounds_handler(bounds, 'fold').apply(v+0)
assert 5 <= w[0] <= 10
if __name__ == "__main__":
test()
|
