def task3(configData):
#if you wish to use this code to perform task 3, you may do so
#NOTE task 3 will require you to remake your bloom filter multiple times to perform the appropriate trials
#this will necessitate either making a new bloom filter constructor or changing the config dictionary to
#hold the appropriate values for k and n (filter size) based on c value, derived as in the notes
#REMEMBER for type 2 hashes n must be prime. util.findNextPrime(n) is provided for you to use to find the next largest
#prime value of some integer.
c = 15
configData['n'] = configData['m'] * c
configData['type'] = 2
# instantiate bloom filter object
bf = BloomFilter(configData, c)
# bfInputData holds a list of integers. Using these values you must :
# insert the first configData['m'] of them into the bloom filter
# test all of them for membership in the bloom filter
bfInputData = util.readIntFileDat(configData['inFileName'])
if (len(bfInputData) == 0):
print('No Data to add to bloom filter')
return
else:
print('bfInputData has ' + str(len(bfInputData)) + ' elements')
# testBF will insert elements and test membership
outputResList = testBF_3(bfInputData, bf, configData['m'])
# write results to output file
# util.writeFileDat(configData['outFileName'], outputResList)
# load appropriate validation data list for this hash function and compare to results
# util.compareResults(outputResList, configData)
print('Task 3 complete')
def task1(configData):
# if you wish to use this code to perform task 1, you may do so
# NOTE : task 1 does not require you to instantiate a bloom filter
# Start random chosen data
bfInputData = util.readIntFileDat(configData['inFileName'])
ht1 = HashType1(configData, True)
ht2 = HashType2(configData, True)
ht1.k = 1
ht2.k = 1
ht1list = []
ht2list = []
x = bfInputData[:10000]
for i in range(0, 10000):
ht1list.append(ht1.getHashList(bfInputData[i]))
ht2list.append(ht2.getHashList(bfInputData[i]))
plt.scatter(x, ht1list, marker='.', s=1)
plt.axis((0, max(x), 0, max(ht1list)[0]))
plt.title('Type 1 Hash Function Values Mapped')
plt.xlabel('input data value')
plt.ylabel('hash value')
plt.show()
plt.scatter(x, ht2list, marker='.', s=1)
plt.axis((0, max(x), 0, max(ht2list)[0]))
plt.title('Type 2 Hash Function Values Mapped')
plt.xlabel('input data value')
plt.ylabel('hash value')
plt.show()
ht1list = []
ht2list = []
x = []
for i in range(0, 20000):
if bfInputData[i] % 2 == 0:
x.append(bfInputData[i])
ht1list.append(ht1.getHashList(bfInputData[i]))
ht2list.append(ht2.getHashList(bfInputData[i]))
plt.scatter(x, ht1list, marker='.', s=1)
plt.axis((0, max(x), 0, max(ht1list)[0]))
plt.title('Type 1 Hash Function Even Values Mapped')
plt.xlabel('input data value')
plt.ylabel('hash value')
plt.show()
plt.scatter(x, ht2list, marker='.', s=1)
plt.axis((0, max(x), 0, max(ht2list)[0]))
plt.title('Type 2 Hash Function Even Values Mapped')
plt.xlabel('input data value')
plt.ylabel('hash value')
plt.show()
print('Task 1 complete')
def task1(configData):
bfInputData = util.readIntFileDat(configData['inFileName'])
ht1 = HashType1(configData, True)
ht2 = HashType2(configData, True)
ht1.k = 1
ht2.k = 1
ht1list = []
ht2list = []
x = bfInputData[:10000]
for i in range(0, 10000):
ht1list.append(ht1.getHashList(bfInputData[i]))
ht2list.append(ht2.getHashList(bfInputData[i]))
plt.scatter(x, ht1list, marker='.', s=1)
plt.axis((0, max(x), 0, max(ht1list)[0]))
plt.title('Type 1 Hash Function Values Mapped')
plt.xlabel('input data value')
plt.ylabel('hash value')
plt.show()
plt.scatter(x, ht2list, marker='.', s=1)
plt.axis((0, max(x), 0, max(ht2list)[0]))
plt.title('Type 2 Hash Function Values Mapped')
plt.xlabel('input data value')
plt.ylabel('hash value')
plt.show()
ht1list = []
ht2list = []
x = []
for i in range(0, 20000):
if bfInputData[i] % 2 == 0:
x.append(bfInputData[i])
ht1list.append(ht1.getHashList(bfInputData[i]))
ht2list.append(ht2.getHashList(bfInputData[i]))
plt.scatter(x, ht1list, marker='.', s=1)
plt.axis((0, max(x), 0, max(ht1list)[0]))
plt.title('Type 1 Hash Function Even Values Mapped')
plt.xlabel('input data value')
plt.ylabel('hash value')
plt.show()
plt.scatter(x, ht2list, marker='.', s=1)
plt.axis((0, max(x), 0, max(ht2list)[0]))
plt.title('Type 2 Hash Function Even Values Mapped')
plt.xlabel('input data value')
plt.ylabel('hash value')
plt.show()
print('Task 1 complete')
def task2(configData):
# instantiate bloom filter object
bf = BloomFilter(configData)
# bfInputData holds a list of integers. Using these values you must :
# insert the first configData['m'] of them into the bloom filter
# test all of them for membership in the bloom filter
bfInputData = util.readIntFileDat(configData['inFileName'])
if (len(bfInputData) == 0):
print('No Data to add to bloom filter')
return
else:
print('bfInputData has ' + str(len(bfInputData)) + ' elements')
# testBF will insert elements and test membership
outputResList = testBF(bfInputData, bf, configData['m'])
# write results to output file
util.writeFileDat(configData['outFileName'], outputResList)
# load appropriate validation data list for this hash function and compare to results
util.compareResults(outputResList, configData)
print('Task 2 complete')
def computeFalsePositive(configData, numTrials, k, c, hashType):
configData['k'] = k
configData['type'] = hashType
configData['n'] = util.findNextPrime(c * configData['m'])
sumFalsePositive = 0
for i in range(0, numTrials):
# initialize bloom filter
bf = BloomFilter(configData)
bfInputData = util.readIntFileDat(configData['inFileName'])
# add data to bloom filter
for j in range(0, configData['m']):
bf.add(bfInputData[j])
falsePositive = 0
# test false positive
for l in range(configData['m'], len(bfInputData)):
if bf.contains(bfInputData[l]):
falsePositive += 1
sumFalsePositive += falsePositive / float(configData['m'])
avgFalsePositive = sumFalsePositive / numTrials
return avgFalsePositive
