X-Git-Url: http://git.tomasm.cz/imago.git/blobdiff_plain/16a9526d24fbdad7bbc544aeae9fd7ba067b3d91..841662dc25b40dce151a0bf7a024e55682028f18:/imago_pack/cs.py?ds=sidebyside

diff --git a/imago_pack/cs.py b/imago_pack/cs.py
deleted file mode 100644
index 09d5280..0000000
--- a/imago_pack/cs.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""Cuckoo search optimization"""
-
-import random
-import lhs
-from math import sin, gamma, pi
-
-class Space(object):
-    def __init__(self, dimension, bound, d_function, n_nest):
-        self.pa = 0.25 #parameter
-        self.dimension = dimension
-        self.bound = bound
-        self.d_function = d_function
-        self.nests = [(d_function(*p), p) for p in lhs.latin_hypercube(dimension, bound, n_nest)]
-        self.best_value, self.best = max(self.nests)
-
-def new_nest(space):
-    position = [2 * space.bound * random.random() 
-                - space.bound for _ in xrange(space.dimension)]
-    value = space.d_function(*position)
-    return (value, position)
-
-def get_cuckoos(space):
-    beta = 1.5
-    sigma = (gamma(1. + beta) * sin(pi * beta / 2.) / (gamma((1. + beta) / 2.) *
-             beta * 2. ** ((beta - 1.) / 2))) ** (1. / beta)
-    u_a = [[random.gauss(0, 1) * sigma for _ in xrange(space.dimension)] for _ in
-         xrange(len(space.nests))]
-    v_a = [[random.gauss(0, 1) for _ in xrange(space.dimension)] for _ in
-         xrange(len(space.nests))]
-    r_a = [[random.gauss(0, 1) for _ in xrange(space.dimension)] for _ in
-         xrange(len(space.nests))]
-    step = [[u / abs(v) ** (1. / beta) for (u, v) in zip(u_l, v_l)]
-            for (u_l, v_l) in zip(u_a, v_a)]
-    stepsize = [[0.01 * st * (n_e - be) for (st, n_e, be) 
-                in zip(step_l, n_l, space.best)]
-                for (step_l, (_, n_l)) in zip(step, space.nests)]
-    s = [[n + st * r for (n, st, r) in zip(n_l, st_l, r_l)]
-         for ((_, n_l), st_l, r_l) in zip(space.nests, stepsize, r_a)]
-    cuckoos = [[min(max(st, - space.bound), space.bound) for st in st_l]
-         for st_l in s]
-    return [(space.d_function(*c), c) for c in cuckoos]
-
-def get_empty(space):
-    r = random.random()
-    r_arr = [[random.random() for _ in xrange(space.dimension)] for _ in
-             xrange(len(space.nests))]
-    perm1 = [n for (_, n) in space.nests]
-    random.shuffle(perm1)
-    perm2 = [n for (_, n) in space.nests]
-    random.shuffle(perm2)
-    stepsize = [[p1 - p2 for (p1, p2) in zip (p1l, p2l)] for (p1l, p2l) in
-                zip(perm1, perm2)]
-    step = [[(r * p * (1 if random.random() > space.pa else 0)) for p in n] for n in stepsize]
-    empty = [[(p + s) for (p, s) in zip(sl, n)] 
-            for (sl, (_, n)) in zip(step, space.nests)]
-    empty = [[min(max(st, - space.bound), space.bound) for st in st_l]
-         for st_l in empty]
-    return [(space.d_function(*e), e) for e in empty]
-
-def next_turn(space):
-    cuckoos = get_cuckoos(space)
-    space.nests = [max(n, m) for (n, m) in zip(space.nests, cuckoos)]
-    nests = get_empty(space)
-    space.nests = [max(n, m) for (n, m) in zip(space.nests, nests)]
-    space.best_value, space.best = max(space.nests)
-
-def optimize(dimension, boundary, function_d, n_nest, n_turns, reset=1):
-    best_list = []
-    for i in xrange(reset):
-        space = Space(dimension, boundary, function_d, n_nest)
-        for _ in xrange(n_turns / reset):
-            next_turn(space)
-        best_list.append((space.best_value, space.best))
-        # print space.best_value
-
-    return max(best_list)[1]