def algorithm1(train1, train2):
"""
- Performs algorithm 1 described in 'Mining Railway Delay Dependenies in
Large-Scale Real-World Delay Data' (Flier, Gelashvili, Graffagnino,
Nunkesser).
- train1 and train2 must be numpy arrays containing delay times of the trains
there can be no missing values, and train1[d], train2[d] must be delay data
from the same day d. Implicitly train1 and train2 must be of equal length.
"""
train1, train2 = trainsorting.sort_two_non_decreasing(train1, train2)
train1 = trainsorting.append(train1, float('inf'))
train2 = trainsorting.append(train2, float(0))
#Locals
diff = trainsorting.difference(train1, train2)
points = 0
points_star = 0
bft = diff[0] #buffertime
bft_star = 0
lpi = 0 #last point index
md_star = 0 # maximal delay
#The algorithm:
for i in range(len(train1)):
if diff[i] > bft:
if points > points_star:
#update best solution
points_star = points
bft_star = bft
md_star = train1[i-1]
bft = diff[i]
notdone = True
while notdone:
if (train1[lpi] < bft and
lpi < len(train1)):
lpi += 1
else:
notdone = False
points = i - lpi + 1
else:
points += 1
return (points_star, bft_star, md_star)
def algorithm2(train1, train2, minwidth=1):
"""
- Performs algorithm 2 described in 'Mining Railway Delay Dependenies in
Large-Scale Real-World Delay Data' (Flier, Gelashvili, Graffagnino,
Nunkesser).
- Optional parameter minwidth describes the minimum requiered headway
between trains using the same infrastructure, varies from station to
station.
- train1 and train2 must be numpy arrays containing delay times of the trains
there can be no missing values, and train1[d], train2[d] must be delay data
from the same day d. Implicitly train1 and train2 must be of equal length.
"""
train1 = trainsorting.append(train1, float('inf'))
train2 = trainsorting.append(train2, 0)
#Locals
intercepts = sorted(trainsorting.difference(train1, train2))
points = 0
points_star = 0
left = 0
leftstar = 0
right = 0
rightstar = 0
fai = 0 #index of first point above stripe
#The algorithm
for j in range(1, len(intercepts)):
left = intercepts[j-1]
right = intercepts[j]
if right - left >= minwidth:
while train1[fai] < left:
fai += 1
points = j - fai
if points > points_star:
#update best solution
points_star = points
leftstar = left
rightstar = right
return (points_star, leftstar, rightstar)
