def edit_ad(url):
room = Room.query.filter_by(urlname=url).first()
if not room and room.user == g.user:
abort(404)
if room.dead or get_days_ago(room.created_at) > OLD_DAYS.days:
abort(404)
occupied = Occupied.query.filter_by(space=room.occupieds).order_by("created_at").first()
if occupied and get_days_ago(occupied.created_at) > OCCUPIED_DAYS.days:
abort(404)
if room.is_available:
form = AvailableAdForm()
else:
form = WantedAdForm()
form.starting.flags.is_date = True
if request.method == "GET":
form.process(obj=room)
if room.is_available:
form.room_pref.data = factorize(room.room_pref)
else:
form.room_type.data = factorize(room.room_type)
form.email.data = g.user.email
elif form.validate_on_submit():
if room.is_available:
form.room_pref.data = product(form.room_pref.data)
else:
form.room_type.data = product(form.room_type.data)
form.populate_obj(room)
db.session.commit()
return redirect(url_for("view_ad", url=room.urlname))
return render_template("autoform.html", form=form, title="Edit advertisement", submit="Save")
def is_sq_free(n, k):
"""Checks whether C(n, k) is square free)"""
from collections import Counter
c = Counter()
for i in range(1, k + 1):
c += Counter(dict(factorize(n - i + 1)))
c -= Counter(dict(factorize(i)))
return all(v == 1 for v in c.values())
def __init__(self, stims, psth_data,tres=15):
self.stims = stims
self.psth_data = psth_data
self.tres = tres
self.STA = reverse_correlation(stims,psth_data,tres,normalize="pseudo_inverse",smooth=1)
self.Sfact, self.Tfact = utils.factorize(self.STA)
self.channels, self.sres = self.Sfact.shape
def number_of_factors(n):
prime_factors = factorize(n)
grouped_factors = [list(g) for k, g in groupby(prime_factors)]
count = 1
for factor_group in grouped_factors:
count *= len(factor_group) + 1
return count
def M(n):
fs = [p**pp for p, pp in factorize(n)]
f2 = [n // f * modinv(n // f, f) % n for f in fs]
ans = 0
for i in range(2**len(fs)):
cur = sum(f2[j] for j in range(len(fs)) if i & (1 << j))
ans = max(ans, cur % n)
return ans
from utils import factorize
def product(seq):
return reduce(lambda x, y: x * y, seq, 1)
limit = 10**5
pos = 10**4
radicals = []
for i in range(1, limit + 1):
radical = product(p for p, _ in factorize(i))
radicals.append((radical, i))
radicals.sort()
print radicals[pos - 1][1]
from utils import factorize, product
limit = 10**6
count = 0
for n in range(8, limit + 1, 4):
k = n // 4
l = product(p + 1 for _, p in factorize(k)) // 2
if l <= 10:
count += 1
print count
from utils import factorize, chinese
from itertools import product
n = 91
def roots(n):
res = []
for i in range(n):
if i**3 % n == 1:
res.append(i)
return res
rems = []
mods = []
for p, pow in factorize(n):
rems.append(roots(p**pow))
mods.append(p**pow)
ans = set()
for p in product(*rems):
ans.add(chinese(list(p), mods))
print(sum(ans))
from utils import factorize, product
for n in xrange(2, 1000000):
c = product(2 * p + 1 for prime, p in factorize(n))
if c > 1999:
print n
break
#!/usr/bin/env python2
# coding: utf-8
from collections import Counter
from utils import factorize
def triangle(n):
return (n * (n + 1)) / 2
i = 1
while True:
current = triangle(i)
prime_factors = factorize(current)
count = Counter(prime_factors)
num_divisors = sum(count.values())
if num_divisors >= 500:
print current
break
i += 1
def combine(dataFrame):
all = pd.DataFrame(index=dataFrame.index)
all["LotFrontage"] = dataFrame["LotFrontage"]
for key, group in lot_frontage_by_neighborhood:
idx = (dataFrame["Neighborhood"]
== key) & (dataFrame["LotFrontage"].isnull())
all.loc[idx, "LotFrontage"] = group.median()
all["LotArea"] = dataFrame["LotArea"]
all["MasVnrArea"] = dataFrame["MasVnrArea"]
all["MasVnrArea"].fillna(0, inplace=True)
all["BsmtFinSF1"] = dataFrame["BsmtFinSF1"]
all["BsmtFinSF1"].fillna(0, inplace=True)
all["BsmtFinSF2"] = dataFrame["BsmtFinSF2"]
all["BsmtFinSF2"].fillna(0, inplace=True)
all["BsmtUnfSF"] = dataFrame["BsmtUnfSF"]
all["BsmtUnfSF"].fillna(0, inplace=True)
all["TotalBsmtSF"] = dataFrame["TotalBsmtSF"]
all["TotalBsmtSF"].fillna(0, inplace=True)
all["1stFlrSF"] = dataFrame["1stFlrSF"]
all["2ndFlrSF"] = dataFrame["2ndFlrSF"]
all["GrLivArea"] = dataFrame["GrLivArea"]
all["GarageArea"] = dataFrame["GarageArea"]
all["GarageArea"].fillna(0, inplace=True)
all["WoodDeckSF"] = dataFrame["WoodDeckSF"]
all["OpenPorchSF"] = dataFrame["OpenPorchSF"]
all["EnclosedPorch"] = dataFrame["EnclosedPorch"]
all["3SsnPorch"] = dataFrame["3SsnPorch"]
all["ScreenPorch"] = dataFrame["ScreenPorch"]
all["BsmtFullBath"] = dataFrame["BsmtFullBath"]
all["BsmtFullBath"].fillna(0, inplace=True)
all["BsmtHalfBath"] = dataFrame["BsmtHalfBath"]
all["BsmtHalfBath"].fillna(0, inplace=True)
all["FullBath"] = dataFrame["FullBath"]
all["HalfBath"] = dataFrame["HalfBath"]
all["BedroomAbvGr"] = dataFrame["BedroomAbvGr"]
all["KitchenAbvGr"] = dataFrame["KitchenAbvGr"]
all["TotRmsAbvGrd"] = dataFrame["TotRmsAbvGrd"]
all["Fireplaces"] = dataFrame["Fireplaces"]
all["GarageCars"] = dataFrame["GarageCars"]
all["GarageCars"].fillna(0, inplace=True)
all["CentralAir"] = (dataFrame["CentralAir"] == "Y") * 1.0
all["OverallQual"] = dataFrame["OverallQual"]
all["OverallCond"] = dataFrame["OverallCond"]
# Quality measurements are stored as text but we can convert them to
# numbers where a higher number means higher quality.
qual_dict = {None: 0, "Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 5}
all["ExterQual"] = dataFrame["ExterQual"].map(qual_dict).astype(int)
all["ExterCond"] = dataFrame["ExterCond"].map(qual_dict).astype(int)
all["BsmtQual"] = dataFrame["BsmtQual"].map(qual_dict).astype(int)
all["BsmtCond"] = dataFrame["BsmtCond"].map(qual_dict).astype(int)
all["HeatingQC"] = dataFrame["HeatingQC"].map(qual_dict).astype(int)
all["KitchenQual"] = dataFrame["KitchenQual"].map(qual_dict).astype(int)
all["FireplaceQu"] = dataFrame["FireplaceQu"].map(qual_dict).astype(int)
all["GarageQual"] = dataFrame["GarageQual"].map(qual_dict).astype(int)
all["GarageCond"] = dataFrame["GarageCond"].map(qual_dict).astype(int)
all["BsmtExposure"] = dataFrame["BsmtExposure"].map({
None: 0,
"No": 1,
"Mn": 2,
"Av": 3,
"Gd": 4
}).astype(int)
bsmt_fin_dict = {
None: 0,
"Unf": 1,
"LwQ": 2,
"Rec": 3,
"BLQ": 4,
"ALQ": 5,
"GLQ": 6
}
all["BsmtFinType1"] = dataFrame["BsmtFinType1"].map(bsmt_fin_dict).astype(
int)
all["BsmtFinType2"] = dataFrame["BsmtFinType2"].map(bsmt_fin_dict).astype(
int)
all["Functional"] = dataFrame["Functional"].map({
None: 0,
"Sal": 1,
"Sev": 2,
"Maj2": 3,
"Maj1": 4,
"Mod": 5,
"Min2": 6,
"Min1": 7,
"Typ": 8
}).astype(int)
all["GarageFinish"] = dataFrame["GarageFinish"].map({
None: 0,
"Unf": 1,
"RFn": 2,
"Fin": 3
}).astype(int)
all["Fence"] = dataFrame["Fence"].map({
None: 0,
"MnWw": 1,
"GdWo": 2,
"MnPrv": 3,
"GdPrv": 4
}).astype(int)
all["YearBuilt"] = dataFrame["YearBuilt"]
all["YearRemodAdd"] = dataFrame["YearRemodAdd"]
all["GarageYrBlt"] = dataFrame["GarageYrBlt"]
all["GarageYrBlt"].fillna(0.0, inplace=True)
all["MoSold"] = dataFrame["MoSold"]
all["YrSold"] = dataFrame["YrSold"]
all["LowQualFinSF"] = dataFrame["LowQualFinSF"]
all["MiscVal"] = dataFrame["MiscVal"]
all["PoolQC"] = dataFrame["PoolQC"].map(qual_dict).astype(int)
all["PoolArea"] = dataFrame["PoolArea"]
all["PoolArea"].fillna(0, inplace=True)
# Add categorical features as numbers too. It seems to help a bit.
factorize(dataFrame, all, "MSSubClass")
factorize(dataFrame, all, "MSZoning", "RL")
factorize(dataFrame, all, "LotConfig")
factorize(dataFrame, all, "Neighborhood")
factorize(dataFrame, all, "Condition1")
factorize(dataFrame, all, "BldgType")
factorize(dataFrame, all, "HouseStyle")
factorize(dataFrame, all, "RoofStyle")
factorize(dataFrame, all, "Exterior1st", "Other")
factorize(dataFrame, all, "Exterior2nd", "Other")
factorize(dataFrame, all, "MasVnrType", "None")
factorize(dataFrame, all, "Foundation")
factorize(dataFrame, all, "SaleType", "Oth")
factorize(dataFrame, all, "SaleCondition")
# IR2 and IR3 don't appear that often, so just make a distinction
# between regular and irregular.
all["IsRegularLotShape"] = (dataFrame["LotShape"] == "Reg") * 1
# Most properties are level; bin the other possibilities together
# as "not level".
all["IsLandLevel"] = (dataFrame["LandContour"] == "Lvl") * 1
# Most land slopes are gentle; treat the others as "not gentle".
all["IsLandSlopeGentle"] = (dataFrame["LandSlope"] == "Gtl") * 1
# Most properties use standard circuit breakers.
all["IsElectricalSBrkr"] = (dataFrame["Electrical"] == "SBrkr") * 1
# About 2/3rd have an attached garage.
all["IsGarageDetached"] = (dataFrame["GarageType"] == "Detchd") * 1
# Most have a paved drive. Treat dirt/gravel and partial pavement
# as "not paved".
all["IsPavedDrive"] = (dataFrame["PavedDrive"] == "Y") * 1
# The only interesting "misc. feature" is the presence of a shed.
all["HasShed"] = (dataFrame["MiscFeature"] == "Shed") * 1.
# If YearRemodAdd != YearBuilt, then a remodeling took place at some point.
all["Remodeled"] = (all["YearRemodAdd"] != all["YearBuilt"]) * 1
# Did a remodeling happen in the year the house was sold?
all["RecentRemodel"] = (all["YearRemodAdd"] == all["YrSold"]) * 1
# Was this house sold in the year it was built?
all["VeryNewHouse"] = (all["YearBuilt"] == all["YrSold"]) * 1
all["Has2ndFloor"] = (all["2ndFlrSF"] == 0) * 1
all["HasMasVnr"] = (all["MasVnrArea"] == 0) * 1
all["HasWoodDeck"] = (all["WoodDeckSF"] == 0) * 1
all["HasOpenPorch"] = (all["OpenPorchSF"] == 0) * 1
all["HasEnclosedPorch"] = (all["EnclosedPorch"] == 0) * 1
all["Has3SsnPorch"] = (all["3SsnPorch"] == 0) * 1
all["HasScreenPorch"] = (all["ScreenPorch"] == 0) * 1
# Months with the largest number of deals may be significant.
all["HighSeason"] = dataFrame["MoSold"].replace({
1: 0,
2: 0,
3: 0,
4: 1,
5: 1,
6: 1,
7: 1,
8: 0,
9: 0,
10: 0,
11: 0,
12: 0
})
all["NewerDwelling"] = dataFrame["MSSubClass"].replace({
20: 1,
30: 0,
40: 0,
45: 0,
50: 0,
60: 1,
70: 0,
75: 0,
80: 0,
85: 0,
90: 0,
120: 1,
150: 0,
160: 0,
180: 0,
190: 0
})
all.loc[dataFrame.Neighborhood == 'NridgHt', "Neighborhood_Good"] = 1
all.loc[dataFrame.Neighborhood == 'Crawfor', "Neighborhood_Good"] = 1
all.loc[dataFrame.Neighborhood == 'StoneBr', "Neighborhood_Good"] = 1
all.loc[dataFrame.Neighborhood == 'Somerst', "Neighborhood_Good"] = 1
all.loc[dataFrame.Neighborhood == 'NoRidge', "Neighborhood_Good"] = 1
all["Neighborhood_Good"].fillna(0, inplace=True)
all["SaleCondition_PriceDown"] = dataFrame.SaleCondition.replace({
'Abnorml':
1,
'Alloca':
1,
'AdjLand':
1,
'Family':
1,
'Normal':
0,
'Partial':
0
})
# House completed before sale or not
all["BoughtOffPlan"] = dataFrame.SaleCondition.replace({
"Abnorml": 0,
"Alloca": 0,
"AdjLand": 0,
"Family": 0,
"Normal": 0,
"Partial": 1
})
all["BadHeating"] = dataFrame.HeatingQC.replace({
'Ex': 0,
'Gd': 0,
'TA': 0,
'Fa': 1,
'Po': 1
})
area_cols = [
'LotFrontage', 'LotArea', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea',
'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch',
'3SsnPorch', 'ScreenPorch', 'LowQualFinSF', 'PoolArea'
]
all["TotalArea"] = all[area_cols].sum(axis=1)
all["TotalArea1st2nd"] = all["1stFlrSF"] + all["2ndFlrSF"]
all["Age"] = 2010 - all["YearBuilt"]
all["TimeSinceSold"] = 2010 - all["YrSold"]
all["SeasonSold"] = all["MoSold"].map({
12: 0,
1: 0,
2: 0,
3: 1,
4: 1,
5: 1,
6: 2,
7: 2,
8: 2,
9: 3,
10: 3,
11: 3
}).astype(int)
all["YearsSinceRemodel"] = all["YrSold"] - all["YearRemodAdd"]
# Simplifications of existing features into bad/average/good.
all["SimplOverallQual"] = all.OverallQual.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2,
6: 2,
7: 3,
8: 3,
9: 3,
10: 3
})
all["SimplOverallCond"] = all.OverallCond.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2,
6: 2,
7: 3,
8: 3,
9: 3,
10: 3
})
all["SimplPoolQC"] = all.PoolQC.replace({1: 1, 2: 1, 3: 2, 4: 2})
all["SimplGarageCond"] = all.GarageCond.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2
})
all["SimplGarageQual"] = all.GarageQual.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2
})
all["SimplFireplaceQu"] = all.FireplaceQu.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2
})
all["SimplFireplaceQu"] = all.FireplaceQu.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2
})
all["SimplFunctional"] = all.Functional.replace({
1: 1,
2: 1,
3: 2,
4: 2,
5: 3,
6: 3,
7: 3,
8: 4
})
all["SimplKitchenQual"] = all.KitchenQual.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2
})
all["SimplHeatingQC"] = all.HeatingQC.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2
})
all["SimplBsmtFinType1"] = all.BsmtFinType1.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2,
6: 2
})
all["SimplBsmtFinType2"] = all.BsmtFinType2.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2,
6: 2
})
all["SimplBsmtCond"] = all.BsmtCond.replace({1: 1, 2: 1, 3: 1, 4: 2, 5: 2})
all["SimplBsmtQual"] = all.BsmtQual.replace({1: 1, 2: 1, 3: 1, 4: 2, 5: 2})
all["SimplExterCond"] = all.ExterCond.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2
})
all["SimplExterQual"] = all.ExterQual.replace({
1: 1,
2: 1,
3: 1,
4: 2,
5: 2
})
# Bin by neighborhood (a little arbitrarily). Values were computed by:
# train_dataFrame["SalePrice"].groupby(train_dataFrame["Neighborhood"]).median().sort_values()
neighborhood_map = {
"MeadowV": 0, # 88000
"IDOTRR": 1, # 103000
"BrDale": 1, # 106000
"OldTown": 1, # 119000
"Edwards": 1, # 119500
"BrkSide": 1, # 124300
"Sawyer": 1, # 135000
"Blueste": 1, # 137500
"SWISU": 2, # 139500
"NAmes": 2, # 140000
"NPkVill": 2, # 146000
"Mitchel": 2, # 153500
"SawyerW": 2, # 179900
"Gilbert": 2, # 181000
"NWAmes": 2, # 182900
"Blmngtn": 2, # 191000
"CollgCr": 2, # 197200
"ClearCr": 3, # 200250
"Crawfor": 3, # 200624
"Veenker": 3, # 218000
"Somerst": 3, # 225500
"Timber": 3, # 228475
"StoneBr": 4, # 278000
"NoRidge": 4, # 290000
"NridgHt": 4, # 315000
}
all["NeighborhoodBin"] = dataFrame["Neighborhood"].map(neighborhood_map)
return all
def largest_factor(number):
return factorize(number)[-1]
from utils import factorize
from math import e, ceil, log
from fractions import Fraction
from decimal import Decimal
limit = 10000
res = 0
for n in range(5, limit+1):
a = int(n/e)
b = int(ceil(n/e))
k = max([a, b], key=lambda x: x * log(1.0*n/x))
denom = Fraction(n, k).denominator
if not all(p in (2, 5) for p,_ in factorize(denom)):
res += n
else:
res -= n
print res
from utils import factorize
from array import array
def trianglenum(n):
return n*(n+1)/2
i = 1
factors = array('L')
while len(factors) <= 500:
i += 1
factors = factorize(trianglenum(i))
print trianglenum(i)
def largestPrimeFactor( num ):
factors = factorize( num, primes )
return max( factors )
def ss(xs):
res = defaultdict(int)
for i in range(len(xs) + 1):
for comb in combinations(xs, i):
s = sum(comb)
if s <= target:
res[s] += 1
return res
t = 81
excluded = [16, 27, 32, 48, 54, 64, 65, 80]
fs = [
Fraction(1, x)**2 for x in range(2, t)
if x not in excluded and all(p in [2, 3, 5, 7, 13]
for p, _ in factorize(x))
]
h = len(fs) // 2
left = fs[:h]
right = fs[h:]
ss1 = ss(left)
ss2 = ss(right)
res = 0
for k, v in ss1.items():
res += v * ss2[target - k]
print(res)
