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binpacking.py
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binpacking.py
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# explanations for member functions are provided in requirements.py
import random as rand
import math
from collections.abc import Iterable
# Use the provided Merge Sort for sorting
class MergeSort:
def __init__(self):
self.time = 0
def sort(self, data):
sorted_data = self.sortHelper(data, 0, len(data))
return sorted_data
def sortHelper(self, data, low, high):
if high - low > 1:
mid = low + (high-low)//2
self.sortHelper(data, low, mid)
self.sortHelper(data, mid, high)
self.merge(data, low, mid, high)
def merge(self, data, low, mid, high):
temp = []
i = low
j = mid
while (i < mid and j < high):
if data[i] > data[j]: # fixed to return decreasing
temp.append(data[i])
i += 1
else:
temp.append(data[j])
j += 1
while (i < mid):
temp.append(data[i])
i += 1
while (j < high):
temp.append(data[j])
j += 1
for k in range(len(temp)):
data[k+low] = temp[k]
# Implement the Next Fit Bin Packing Algorithm
# bins: is a list of lists, where each inner list shows the contents of a bin (do not change)
# waste: is the computed waste for the input data (do not change)
# times: is a list to hold the run times (do not change)
# num_bins: stores the number of bins required to pack the data (do not change)
# reset: is a method to reset the state of the packing object (do not change)
# measure: is a method to compute the waste by estimating the optimal and calling pack on the data
# pack: is a method which implements the bin packing algorithm
class NextFit:
def __init__(self):
self.bins = []
self.waste = []
self.times = []
self.num_bins = 0
def reset(self):
self.bins = []
self.waste = []
self.times = []
self.num_bins = 0
def measure(self, data):
optimal = sum(data) / 1.0
self.num_bins = self.pack(data)
waste = self.num_bins - optimal
self.waste.append(waste)
return waste
def pack(self, data):
self.bins.append([])
self.num_bins = 1
for i in data:
current = self.bins[-1]
current_size = sum(current)
if current_size + i <= 1.0:
current.append(i)
else:
self.bins.append([i])
self.num_bins += 1
return self.num_bins
# Implement the First Fit Bin Packing Algorithm
# bins: is a list of lists, where each inner list shows the contents of a bin (do not change)
# bin_sums: is a list of sums, one for each bin
# waste: is the computed waste for the input data (do not change)
# times: is a list to hold the run times (do not change)
# num_bins: stores the number of bins required to pack the data (do not change)
# reset: is a method to reset the state of the packing object (do not change)
# measure: is a method to compute the waste by estimating the optimal and calling pack on the data
# pack: is a method which implements the bin packing algorithm
class FirstFit:
def __init__(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
def reset(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
def measure(self, data):
optimal = sum(data) / 1.0
self.num_bins = self.pack(data)
waste = self.num_bins - optimal
self.waste.append(waste)
return waste
def pack(self, data):
for i in data:
added=False
for j in range(len(self.bins)):
if self.bin_sums[j] + i <= 1.0:
self.bins[j].append(i)
self.bin_sums[j] += i
added=True
break
if not added:
self.bins.append([i])
self.bin_sums.append(i)
self.num_bins += 1
return self.num_bins
# Implement the Best Fit Bin Packing Algorithm
# bins: is a list of lists, where each inner list shows the contents of a bin (do not change)
# bin_sums: is a list of sums, one for each bin
# waste: is the computed waste for the input data (do not change)
# times: is a list to hold the run times (do not change)
# num_bins: stores the number of bins required to pack the data (do not change)
# reset: is a method to reset the state of the packing object (do not change)
# measure: is a method to compute the waste by estimating the optimal and calling pack on the data
# pack: is a method which implements the bin packing algorithm
class BestFit:
def __init__(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
def reset(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
def measure(self, data):
optimal = sum(data) / 1.0
self.num_bins = self.pack(data)
waste = self.num_bins - optimal
self.waste.append(waste)
return waste
def pack(self, data):
for i in data:
min_space = float('inf')
min_bin_ind = -1
for bin_ind in range(len(self.bins)):
bin_sum = sum(self.bins[bin_ind])
bin_space = 1.0 - bin_sum
if i <= bin_space and bin_space < min_space:
min_space = bin_space
min_bin_ind = bin_ind
if min_bin_ind == -1:
self.bins.append([i])
self.bin_sums.append(i)
self.num_bins += 1
else:
self.bins[min_bin_ind].append(i)
self.bin_sums[min_bin_ind] += i
return self.num_bins
# Implement the First Fit Decreasing Bin Packing Algorithm
# bins: is a list of lists, where each inner list shows the contents of a bin (do not change)
# bin_sums: is a list of sums, one for each bin
# waste: is the computed waste for the input data (do not change)
# times: is a list to hold the run times (do not change)
# num_bins: stores the number of bins required to pack the data (do not change)
# sorter: sorting object
# packer: bin packing object
# reset: is a method to reset the state of the packing object (do not change)
# measure: is a method to compute the waste by estimating the optimal and calling pack on the data
# pack: is a method which implements the bin packing algorithm
class FirstFitDec:
def __init__(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
self.sorter = MergeSort()
self.packer = FirstFit()
def reset(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
self.sorter = MergeSort()
self.packer = FirstFit()
def measure(self, data):
self.sorter.sort(data)
waste = self.packer.measure(data)
self.bins = self.packer.bins
self.bin_sums = self.packer.bin_sums
self.waste = self.packer.waste
self.times = self.packer.times
self.num_bins = self.packer.num_bins
return waste
# Implement the Best Fit Decreasing Bin Packing Algorithm
# bins: is a list of lists, where each inner list shows the contents of a bin (do not change)
# bin_sums: is a list of sums, one for each bin
# waste: is the computed waste for the input data (do not change)
# times: is a list to hold the run times (do not change)
# num_bins: stores the number of bins required to pack the data (do not change)
# sorter: sorting object
# packer: bin packing object
# reset: is a method to reset the state of the packing object (do not change)
# measure: is a method to compute the waste by estimating the optimal and calling pack on the data
# pack: is a method which implements the bin packing algorithm
class BestFitDec:
def __init__(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
self.sorter = MergeSort()
self.packer = BestFit()
def reset(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
self.sorter = MergeSort()
self.packer = BestFit()
def measure(self, data):
self.sorter.sort(data)
waste = self.packer.measure(data)
self.bins = self.packer.bins
self.bin_sums = self.packer.bin_sums
self.waste = self.packer.waste
self.times = self.packer.times
self.num_bins = self.packer.num_bins
return waste
# Implement a Custom Fit Bin Packing Algorithm
# The goal is to modify the best performing (fewest bins) algorithm
# to try to improve the packing (number of bins) for at least 1 set of the standard input data.
# HINT: try modifying data after sorting
# bins: is a list of lists, where each inner list shows the contents of a bin (do not change)
# bin_sums: is a list of sums, one for each bin
# waste: is the computed waste for the input data (do not change)
# times: is a list to hold the run times (do not change)
# num_bins: stores the number of bins required to pack the data (do not change)
# sorter: sorting object
# packer: bin packing object
# reset: is a method to reset the state of the packing object (do not change)
# measure: is a method to compute the waste by estimating the optimal and calling pack on the data
# pack: is a method which implements the bin packing algorithm
class CustomFit1:
def __init__(self, threshold = 0.15):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
self.threshold = threshold
self.sorter = MergeSort()
def reset(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
self.sorter = MergeSort()
def pack(self, data):
for elem in data:
for bin_index, contents in enumerate(self.bins):
space_left = 1.0 - sum(contents)
if space_left >= elem:
if space_left >= self.threshold:
self.bins[bin_index].append(elem)
break
else:
continue
else:
self.bins.append([elem])
self.num_bins += 1
def measure(self, data):
self.sorter.sort(data)
self.pack(data)
optimal = sum(data) / 1.0
waste = self.num_bins - optimal
self.waste.append(waste)
return waste
class CustomFit2:
def __init__(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
# self.sorter = MergeSort()
self.packer = BestFit() # TODO: Use the best bin packing algorithm based on the test data
def reset(self):
self.bins = [[]]
self.bin_sums = [0]
self.waste = []
self.times = []
self.num_bins = 1
# self.sorter = MergeSort()
self.packer = BestFit() # TODO: Use the best bin packing algorithm based on the test data
def modify(self, data):
modified_data=[]
limit=0.20
small_items=[item for item in data if item <=limit]
other_items=[item for item in data if item > limit]
while small_items:
group=[]
group_sum=0
while small_items and group_sum+small_items[-1] <= 1:
item=small_items.pop()
group.append(item)
group_sum+=item
modified_data.append(group_sum)
modified_data=other_items+modified_data
return modified_data
def modify2(self, data, num_ranges=5):
range_size=1/num_ranges
ranges=[[] for i in range(num_ranges-1)]
for item in data:
range_index=int(item//range_size)
if range_index==num_ranges:
range_index-=1
ranges[range_index].append(item)
new_data=[]
for i in ranges[::-1]:
new_data.extend(i)
return new_data
def measure(self, data):
# Implement Optimization
data=self.modify2(data)
waste = self.packer.measure(data)
self.bins = self.packer.bins
self.bin_sums = self.packer.bin_sums
self.waste = self.packer.waste
self.times = self.packer.times
self.num_bins = self.packer.num_bins
return waste
# feel free to define new methods in addition to the above
# fill in the definitions of each required member function (above),
# and for any additional member functions you define