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Commit45f2de1

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committed
np.round() update
1 parent7946a61 commit45f2de1

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2 files changed

+42
-24
lines changed

2 files changed

+42
-24
lines changed

‎README.md‎

Lines changed: 6 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -130,13 +130,17 @@ This is an example for if you've had a difficult time with the requirements.txt
130130
mySwarm= swarm(LB,UB,TARGETS,TOL,MAXIT,
131131
func_F, constr_F,
132132
opt_df,
133-
parent=parent)
133+
parent=parent,
134+
evaluate_threshold=False,obj_threshold=None,
135+
decimal_limit=4)
134136

135137
# arguments should take the form:
136138
# swarm([[float, float, ...]], [[float, float, ...]], [[float, ...]], float, int,
137139
# func, func,
138140
# dataFrame,
139-
# class obj)
141+
# class obj,
142+
# bool, [int, int, ...],
143+
# int)
140144
#
141145
# opt_df contains class-specific tuning parameters
142146
# boundary: int. 1 = random, 2 = reflecting, 3 = absorbing, 4 = invisible

‎src/chicken_swarm.py‎

Lines changed: 36 additions & 22 deletions
Original file line numberDiff line numberDiff line change
@@ -6,7 +6,7 @@
66
# A basic chicken swarm optimization class.
77
#
88
# Author(s): Lauren Linkous, Jonathan Lundquist
9-
# Last update:March 13, 2025
9+
# Last update:june 19, 2025
1010
##--------------------------------------------------------------------\
1111

1212

@@ -22,7 +22,8 @@ class swarm:
2222
# func, func,
2323
# dataFrame,
2424
# class obj,
25-
# bool, [int, int, ...])
25+
# bool, [int, int, ...],
26+
# int)
2627
#
2728
# opt_df contains class-specific tuning parameters
2829
# boundary: int. 1 = random, 2 = reflecting, 3 = absorbing, 4 = invisible
@@ -37,11 +38,17 @@ def __init__(self, lbound, ubound, targets,E_TOL, maxit,
3738
obj_func,constr_func,
3839
opt_df,
3940
parent=None,
40-
evaluate_threshold=False,obj_threshold=None):
41+
evaluate_threshold=False,obj_threshold=None,
42+
decimal_limit=4):
4143

4244
# Optional parent class func call to write out values that trigger constraint issues
4345
self.parent=parent
4446

47+
48+
self.number_decimals=int(decimal_limit)# limit the number of decimals
49+
# used in cases where real life has limitations on resolution
50+
51+
4552
#evaluation method for targets
4653
# True: Evaluate as true targets
4754
# False: Evaluate as thesholds based on information in obj_threshold
@@ -159,10 +166,14 @@ def __init__(self, lbound, ubound, targets,E_TOL, maxit,
159166
self.chicken_info=np.array([0,0,-1])
160167

161168
#randomly initialize the positions
162-
self.M=np.vstack(np.multiply(self.rng.random((np.max([heightl,
163-
widthl]),1)),
164-
variation)+
165-
lbound)
169+
# self.M = np.vstack(np.multiply(self.rng.random((np.max([heightl,
170+
# widthl]),1)),
171+
# variation) +
172+
# lbound)
173+
174+
self.M=np.round(np.array(np.multiply(self.rng.random((1,np.max([heightl,widthl]))),variation)+lbound),self.number_decimals)
175+
176+
166177

167178

168179
ifNO_OF_PARTICLES>1:
@@ -187,10 +198,10 @@ def __init__(self, lbound, ubound, targets,E_TOL, maxit,
187198
foriinrange(2,int(NO_OF_PARTICLES)+1):
188199
# set initial location
189200
self.M= \
190-
np.vstack([self.M,
201+
np.round(np.vstack([self.M,
191202
np.multiply(self.rng.random((1,np.max([heightl,widthl]))),
192203
variation)
193-
+lbound])
204+
+lbound]),self.number_decimals)
194205
ifclassList[i-1]==0:#rooster
195206
# assign to the next group (i-1), and done.
196207
self.chicken_info= \
@@ -382,7 +393,7 @@ def move_rooster(self, particle):
382393

383394

384395
#update new location based on random()
385-
self.M[particle]=self.M[particle]*(1+self.rng.normal(0,sig_squared))
396+
self.M[particle]=np.round(self.M[particle]*(1+self.rng.normal(0,sig_squared)),self.number_decimals)
386397

387398

388399
defmove_hen(self,particle):
@@ -426,9 +437,9 @@ def move_hen(self, particle):
426437
# these clipped bounds are effectively are zero and inf
427438
# term_1 = S1*self.rng.uniform(0,1)*(rooster_loc-self.M[particle])
428439
# still dealing with overflow issues. apply cap to S1
429-
S1=np.clip(S1,-10e50,10e50)
440+
S1=np.clip(S1,-10e30,10e30)
430441

431-
clipped_term1=np.clip((S1*self.rng.uniform(0,1)*(rooster_loc-self.M[particle])),-10e50,10e10)
442+
clipped_term1=np.clip((S1*self.rng.uniform(0,1)*(rooster_loc-self.M[particle])),-10e30,10e10)
432443
term_1=clipped_term1
433444

434445
#S2 = np.exp(float(fitness_random_chicken-fitness_this_chicken))
@@ -443,13 +454,13 @@ def move_hen(self, particle):
443454
#term_2 = S2*self.rng.uniform(0,1)*(random_chicken_loc-self.M[particle])
444455
# This still causes overflow:
445456
# clipped_term2 = np.clip((S2*self.rng.uniform(0,1)*(random_chicken_loc-self.M[particle])), -10e50, 10e10)
446-
S2=np.clip(S2,-10e50,10e50)
457+
S2=np.clip(S2,-10e30,10e30)
447458

448-
clipped_term2=np.clip((S2*self.rng.uniform(0,1)*(random_chicken_loc-self.M[particle])),-10e50,10e10)
459+
clipped_term2=np.clip((S2*self.rng.uniform(0,1)*(random_chicken_loc-self.M[particle])),-10e30,10e10)
449460
term_2=clipped_term2
450461

451462
# new_loc = old_loc + term_1 + term_2
452-
self.M[particle]=self.M[particle]+term_1+term_2
463+
self.M[particle]=np.round(self.M[particle]+term_1+term_2,self.number_decimals)
453464

454465

455466
defmove_chick(self,particle):
@@ -459,7 +470,7 @@ def move_chick(self, particle):
459470

460471
mother_idx=int(self.chicken_info[particle][2])# the the idx of the mother chicken
461472
mother_loc=self.M[mother_idx]
462-
self.M[particle]=self.M[particle]+self.rng.choice([0,2])*(mother_loc-self.M[particle])
473+
self.M[particle]=np.round(self.M[particle]+self.rng.choice([0,2])*(mother_loc-self.M[particle]),self.number_decimals)
463474

464475
defreorganize_swarm(self):
465476
# rank the chickens' fitness vals and establish hierarchial order
@@ -559,12 +570,15 @@ def random_bound(self, particle):
559570
# and may cause a buffer overflow with large exponents (a bug that was found experimentally)
560571
update=self.check_bounds(particle)ornotself.constr_func(self.M[particle])
561572
ifupdate>0:
562-
while(self.check_bounds(particle)>0)or (self.constr_func(self.M[particle])==False):
563-
variation=self.ubound-self.lbound
564-
self.M[particle]= \
565-
np.squeeze(self.rng.random()*
566-
np.multiply(np.ones((1,np.shape(self.M)[1])),
567-
variation)+self.lbound)
573+
while (self.check_bounds(particle)>0)or (self.constr_func(self.M[particle])==False):
574+
variation=self.ubound-self.lbound
575+
self.M[particle]=np.round(
576+
np.squeeze(
577+
self.rng.random()*
578+
np.multiply(np.ones((1,np.shape(self.M)[1])),variation)+
579+
self.lbound
580+
),self.number_decimals)
581+
568582

569583
defreflecting_bound(self,particle):
570584
update=self.check_bounds(particle)

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