def splitChoose(r):
if (avL + avR) < r: return []
rc = []
if r == 1:
if avL > 0: rc = com(left, 1)
if avR > 0: rc = plus(rc, com(right, 1))
elif r < 6:
if avL == 2: rc = cross(com(left, 2), com(right, r - 2))
if avL >= 1 and avR >= r - 1:
rc = plus(rc, cross(left, com(right, r - 1)))
if avR >= r: rc = plus(rc, com(right, r))
return rc
def pack(recipe, packages, num_packages): possibilities = list(cross(*packages)) index = list(cross(*[range(len(package)) for package in packages])) y = list(zip(index, [yields(recipe, p) for p in possibilities])) print(y) nonzero = [a for a, b in y if b != 0] combs = flatten([combinations(nonzero, i) for i in range(1,num_packages+1)[::-1]]) non_overlapping_combs =
def crossrange(*args):
'''
Example:
>>> list(utils.crossrange(5,3))
[(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2),
(3, 0), (3, 1), (3, 2), (4, 0), (4, 1), (4, 2)]
'''
return cross(*[xrange(arg) for arg in args])
def makeMatrix(s1, s2): pairs = list(cross(s1, s2)); length = len(s1) pairs = [pairs[length*i:length*(i+1)] for i in xrange(length)] answer = [[0 for _ in xrange(len(s2)+1)] for _ in xrange(len(s1)+1)] for r, row in enumerate(answer[1:], 1): for c, _ in enumerate(row[1:], 1): # if len(set(pairs[((r-1)*len(s1)) +c-1])) >1: if len(set(pairs[r-1][c-1])) >1: row[c] = 0 else: row[c] = answer[r-1][c-1]+1 return answer
def update_screen(self):
"""
Function Use: [Explain Me!]
"""
self.wn.clear()
pcycle = cycle([(self.pos[0] + i * 20, self.pos[1] - j * 20)
for j in range(0, 9) for i in range(0, 9)])
x, y = self.pos
self.Droo.penup()
self.Droo.goto(next(pcycle))
for i, j in cross(range(9), range(9)):
if self.top_individuals[0].board[i][j] == self.solution[i][j]:
self.Droo.write(self.top_individuals[0].board[i][j],
font=('Arial', 16, 'normal'))
self.Droo.goto(next(pcycle))
self.Droo.goto(-160, 30)
self.Droo.write('{}% Solved'.format(
round(100 - self.top_fitness * 100, 2)),
font=('Arial', 16, 'normal'))
def plot_confusion_matrix(arr, labels, colormap=plt.cm.Reds):
"""
Adapted from
http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
"""
plt.imshow(arr, cmap=colormap)
plt.title('Confusion matrix')
ticks = np.arange(len(labels))
plt.xticks(ticks, labels)
plt.yticks(ticks, labels)
limit = np.max(arr) / 2
for x, y in cross(range(arr.shape[0]), range(arr.shape[1])):
plt.text(y,
x,
format(arr[x, y], 'd'),
horizontalalignment="center",
color="white" if arr[x, y] > limit else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
print t, time ##
# f.write("%s\t%s\n" %(t, time))
return answer
if __name__ == "__main__":
print 'starting'
_Ns = [15]
_dists = [5, 7, 9, 11]
_T = 10
_CMAX = 600
for _N, _dist in cross(_Ns, _dists):
print 'N%sD%sT%sC%s' % (_N, _dist, _T, _CMAX)
V, E, PATHS, inf = datagen()
fpath = 'data/N%s_D%s_T%s_C%s' % (_N, _dist, _T, _CMAX)
answer = tabuSearch((V, E),
PATHS,
N=_N,
dist=_dist,
T=_T,
CMAX=_CMAX,
fpath=fpath)
print "answer", answer
E.pop().__class__._ID = 1
V.pop().__class__._ID = 0
print 'done'
i = min((i for i in xrange(len(neighborhood)) if neighborhood[i] not in tabu))
n,nn = clone(neighborhood[i])
tabu.add(n)
t = evaluate(nn.values())
if t < time:
answer = t
time = answer
print t, time ##
# f.write("%s\t%s\n" %(t, time))
return answer
if __name__ == "__main__":
print 'starting'
_Ns = [15]
_dists = [5, 7, 9, 11]
_T = 10
_CMAX = 600
for _N, _dist in cross(_Ns, _dists):
print 'N%sD%sT%sC%s' %(_N, _dist, _T, _CMAX)
V, E, PATHS, inf = datagen()
fpath = 'data/N%s_D%s_T%s_C%s' %(_N, _dist, _T, _CMAX)
answer = tabuSearch((V, E), PATHS, N=_N, dist=_dist, T=_T, CMAX=_CMAX, fpath=fpath)
print "answer", answer
E.pop().__class__._ID = 1
V.pop().__class__._ID = 0
print 'done'