def new_data():
"""Create a data set for figure 7, with one threshold, and exactly one
observation for each trial-RP-AP combination.
"""
threshold = 60
max_rp = 35
max_t = 8
points = _new_points(threshold, max_rp).reshape(-1, order='f')
all_combs = np.array(list(prd(np.arange(8), points)))
points = all_combs[:, 1]
offers = all_combs[:, 0]
trials = np.tile(np.arange(max_t), max_rp * 8)
data_flat = {'NumberGames': 1,
'NumberTrials': max_t,
'TargetLevels': np.array([threshold * 10]),
'obs': points * 10,
'reihe': offers + 1,
'threshold': np.tile(threshold * 10, len(trials)),
'trial': trials,
'choice': np.tile(0, len(trials))}
return data_flat
def init_grid():
even_line = range(0,10,2)
odd_line = range(1,10,2)
indices=[]
grid_actual = [[0 for a in range(10)]for a in range(10)]
# remember that we need product both ways
for a in prd(even_line,odd_line): indices.append(a) #;print (a)
for a in prd(odd_line,even_line): indices.append(a) # print (a)
# print(len(indices))
# indices
for a in indices:
# print(a)
if a[0]<4: grid_actual[a[0]][a[1]] = 1
if a[0]>5: grid_actual[a[0]][a[1]] = 2
print_grid(grid_actual)
return grid_actual, indices
def checkio(matrix):
N = len(matrix[0])
num = [0, 0]
cnt = {'V': 0, 'H': 0, 'D1': 0, 'D2': 0}
# vertical and horizontal
for i in range(N):
for j in range(N):
if num[0] == matrix[j][i]:
cnt['V'] += 1
else:
cnt['V'] = 0
num[0] = matrix[j][i]
if num[1] == matrix[i][j]:
cnt['H'] += 1
else:
cnt['H'] = 0
num[1] = matrix[i][j]
if cnt['V'] == 3 or cnt['H'] == 3:
return True
cnt['V'] = 0
cnt['H'] = 0
num = [0, 0]
# diagonal
for i in prd(range(N), range(N)):
for j in range(N):
if i[0] + j < N and i[1] + j < N:
if num[0] == matrix[i[0] + j][i[1] + j]:
cnt['D1'] += 1
else:
cnt['D1'] = 0
num[0] = matrix[i[0] + j][i[1] + j]
if cnt['D1'] == 3:
return True
if i[0] + j < N and i[1] - j >= 0:
if num[1] == matrix[i[0] + j][i[1] - j]:
cnt['D2'] += 1
else:
cnt['D2'] = 0
num[1] = matrix[i[0] + j][i[1] - j]
if cnt['D2'] == 3:
return True
cnt['D1'] = 0
cnt['D2'] = 0
num = [0, 0]
return False
def similarity_matrix(sim, rep):
score_matrix = []
for product in product_category_list:
#この時点でproductはリスト
product_row = []
for site in site_category_list:
# product site ともにリスト、中身は一つ〜
best = 0
allsyns1 = set(ss for word in product for ss in (
wn.synsets(word) if wn.synsets(word) else flatten_list(
[wn.synsets(w) for w in word.split("_")])))
allsyns2 = set(ss for word in site for ss in (
wn.synsets(word) if wn.synsets(word) else flatten_list(
[wn.synsets(w) for w in word.split("_")])))
#allsynsには入っているのは各単語のsynsetのset
comparison_result = [(compute_similarities(s1, s2, sim)
or 0, s1, s2)
for s1, s2 in prd(allsyns1, allsyns2)
if s1.pos() == s2.pos() and s1.pos() != "s"]
# if "games" in product and "Games" in site:
# print("{}, {}".format(product, site))
# print("{}".format(comparison_result[:10]))
if comparison_result:
if rep == "max":
best = max([x[0] for x in comparison_result])
elif rep == "mean":
best = np.mean([x[0] for x in comparison_result])
elif rep == "median":
best = np.median([x[0] for x in comparison_result])
elif rep == "min":
best = min([x[0] for x in comparison_result])
product_row.append(best)
score_matrix.append(product_row)
file_name = "big" + sim + "_" + rep + ".tsv"
result = pd.DataFrame(score_matrix,
index=tf.product_ctgr_name,
columns=df.name)
result.to_csv(file_name, sep="\t", encoding="utf-8")
words = item.split('/')
product_category_list.append(words)
else:
product_category_list.append([item])
pprint(site_category_list)
print(len(site_category_list))
pprint(product_category_list)
print(len(product_category_list))
similarity_measures = ["lch"]
reppresentation_measures = ["max"]
# similarity_measures = ["path","lch","wup","res","jcn","lin"]
# reppresentation_measures = ["max","mean","median","min"]
for sim, rep in prd(similarity_measures, reppresentation_measures):
similarity_matrix(sim, rep)
# simple_similarity_matrix(product_category_list,site_category_list)
# score_matrix = []
# for product in product_category_list:
# product_row = []
# #print(product)
# #print("Above is the product #############################################")
# for site in site_category_list:
# #print(site)
# temp_sim = []
# for prod in product:
# for sit in site:
return [
(pos[0]+dirs_down[0][0],pos[1]+dirs_down[0][1]),
(pos[0]+dirs_down[1][0],pos[1]+dirs_down[1][1])
]
test_data = [(1,2), (6,5), (0,7), (7,0)]
for a in test_data:
print(a, movements(a), movements(a, False))
##################### initiating the grid ########################
from itertools import product as prd
even_line = range(0,10,2)
odd_line = range(1,10,2)
indices=[]
# remember that we need product both ways
for a in prd(even_line,odd_line):
indices.append(a)
print (a)
for a in prd(odd_line,even_line):
indices.append(a)
# print (a)
# print(len(indices))
# indices
for a in indices:
# print(a)
if a[0]<4: grid[a[0]][a[1]] = 1
if a[0]>5: grid[a[0]][a[1]] = 2
#################################################################
def print_grid2():
print('################################')
#log# Automatic Logger file. *** THIS MUST BE THE FIRST LINE ***
#log# DO NOT CHANGE THIS LINE OR THE TWO BELOW
#log# opts = Struct({'__allownew': True, 'logfile': 'hrm.py'})
#log# args = []
#log# It is safe to make manual edits below here.
#log#-----------------------------------------------------------------------
axisiter = lambda l,maxn: enumerate(chain(*(imap(str.join, prd(*((l,)*n))) for n in range(maxn+1))))
matrixiter = lambda l,maxn: enumerate(prd(axisiter(l,maxn), axisiter(l,maxn)))
for x in matrixiter(['X','Y'],2): print x
from itertools import product as prd
for x in matrixiter(['X','Y'],2): print x
from itertools import product as prd, chain
for x in matrixiter(['X','Y'],2): print x
from itertools import product as prd, chain, imap
for x in matrixiter(['X','Y'],2): print x
axisiter = lambda l,maxn: enumerate(chain(*(imap(lambda x: str.join((*x,)), prd(*((l,)*n))) for n in range(maxn+1))))
axisiter = lambda l,maxn: enumerate(chain(*(imap(lambda x: str.join(*x,), prd(*((l,)*n))) for n in range(maxn+1))))
axisiter = lambda l,maxn: enumerate(chain(*(imap(lambda x: str.join(*x), prd(*((l,)*n))) for n in range(maxn+1))))
for x in matrixiter(['X','Y'],2): print x
axisiter = lambda l,maxn: enumerate(chain(*(imap(lambda x: str.join(str(s) for s in x), prd(*((l,)*n))) for n in range(maxn+1))))
for x in matrixiter(['X','Y'],2): print x
axisiter = lambda l,maxn: enumerate(chain(*(imap(lambda x: str.join(*(str(s) for s in x)), prd(*((l,)*n))) for n in range(maxn+1))))
for x in matrixiter(['X','Y'],2): print x
axisiter = lambda l,maxn: enumerate(chain(*(prd(*((l,)*n))) for n in range(maxn+1)))
axisiter = lambda l,maxn: enumerate(chain(*(prd(*((l,)*n))) for n in range(maxn+1))))
axisiter = lambda l,maxn: enumerate(chain(*(prd(*((l,)*n))) for n in range(maxn+1)))))
axisiter = lambda l,maxn: enumerate(chain(*(prd(*((l,)*n))) for n in range(maxn+1)
axisiter = lambda l,maxn: enumerate(chain(*(prd(*((l,)*n))) for n in range(maxn+1)))
axisiter = lambda l,maxn: izip(count(), chain(*(prd(*((l,)*n))) for n in range(maxn+1))))
axisiter = lambda l,maxn: izip(count(), chain(*(prd(*((l,)*n)) for n in range(maxn+1))))
for x in matrixiter(['X','Y'],2): print x
def merge_mfq_arr(mfq_main, mfq_minor):
mfq_merge = [main + minor for main, minor in prd(mfq_main, mfq_minor)]
mfq_merge = np.array(mfq_merge, dtype="uint32").transpose()
mfq_merge = np.unique(mfq_merge, axis=1)
return mfq_merge
if cur:
if isX: xs.append(cur)
else: ys.append(cur)
isX = not isX
cur = 0
elif S[i] == 'F':
cur += 1
if cur:
if isX: xs.append(cur)
else: ys.append(cur)
sumxs = sum(xs)
sumys = sum(ys)
if abs(X) > sumxs or abs(Y) > sumys:
print('No')
exit()
memo1 = [[0] * (sumxs * 2 + 1) for i in range(len(xs) + 1)]
memo2 = [[0] * (sumys * 2 + 1) for i in range(len(ys) + 1)]
memo1[0][0] = 1
memo2[0][0] = 1
for i, j in prd(range(1, len(xs) + 1), range(-sumxs, sumxs + 1)):
memo1[i][j] = memo1[i - 1][j + xs[i - 1]] or memo1[i - 1][j - xs[i - 1]]
for i, j in prd(range(1, len(ys) + 1), range(-sumys, sumys + 1)):
memo2[i][j] = memo2[i - 1][j + ys[i - 1]] or memo2[i - 1][j - ys[i - 1]]
print('Yes' if memo1[len(xs)][X] and memo2[len(ys)][Y] else 'No')
from itertools import product as prd k,m = map(int,input().split()) fx = [map(int,input().split()[1:]) for _ in range(k)] print(max([sum([int(t)**2 for t in d])%m for d in prd(*fx) ]))