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main.py
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main.py
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import sys
import numpy as np
import hashlib
import random
import generator
import Parser as parser
from Algorithms import FlajoletMartin as FM
import Experiment
#Literatuur notes:
# n = stream size
# hx = hash function number x
# S = set
# m = key values
ranges = [10**2,10**3,10**4,10**5,2*10**5,3*10**5,4*10**5,5*10**5,6*10**5,7*10**5,8*10**5,9*10**5,10**6]
results = {}
for j in range(len(ranges)):
ranger = ranges[j]
results.update({ranger:{}})
for i in range(1,7):
results[ranger].update({i:[]})
def main(runPc = True, runLogLog = True,ranger=10000):
''''hashes, size, arraysPerStream = parser.parseInput(sys.argv)
distinctCounts = []
for i in range(0, hashes, 1):
bitArrays = generator.bitstring(size, 100)
distinctCount = FM.run(bitArrays)
distinctCounts.append(distinctCount)
averageDC = sum(distinctCounts) / hashes
medianDC = np.median(distinctCounts)
print("Mean: " + str(averageDC))
print("Median: " + str(medianDC))'''
#print(hashlib.algorithms_available)
if(runPc):
experimentCounting(ranger)
if (runLogLog):
logLog()
def experimentCounting(ranger):
#print("Probabilistic Counting")
#print("\nTo use hash functions:")
hashes = list(hashlib.algorithms_guaranteed)
for element in hashes:
if element.lower().startswith("shake_"):
hashes.remove(element)
#print(hashlib.algorithms_guaranteed)
#print(hashes)
setups = Experiment.getSetup(["distinct","hashes"])
distincts = setups.get("various numbers of distinct elements")
numHashes = setups.get("number of hashes")
#for i in range(len(distincts)):
# distinct = distincts[i][0]
# calcCounting(distinct,3,hashes)
for i in range(len(numHashes)):
numHash = numHashes[i]
calcCounting(ranger,numHash,hashes)
def calcCounting(distinct,numHash,hashes):
hashGroups = generator.partitionIntoGroups(hashes, numHash)
numbers = generator.generateRandomIntegers(10 ** 4, 0, distinct)
trueCount = len(np.unique(numbers))
# numbers = np.random.choice(numbers, 100000) # take sample
groupAvgs = []
#print("\nDistinct: " + str(distinct) + "\nNumber of hashes per group: " + str(numHash))
for i, hashGroup in enumerate(hashGroups):
#print("Hash group " + str(i + 1) + ": " + str(hashGroup))
sumCounts = 0
for hashName in hashGroup:
#print("\t Running: " + hashName)
hashFunction = getattr(hashlib, hashName)
binaries = generator.getHashBinaries(numbers, hashFunction)
distinctCount = FM.probabilisticCounting(binaries)
sumCounts += distinctCount
groupAvg = sumCounts / len(hashGroup)
groupAvgs.append(groupAvg)
#print("Distinct elements: " + str(trueCount))
estimatedCount = np.median(groupAvgs)
#print("Median of averages: " + str(estimatedCount))
RAE = printError(trueCount, estimatedCount)
results[distinct][numHash].append(RAE)
def logLog():
print("################################################################")
print(":: LogLog ::")
print("################################################################")
setups = Experiment.getSetup(["distinct"])
#test = [(name, setup) for name, setup in setups.items() if setup[0] == True]
for i, (name, setup) in enumerate(setups.items()):
whiteSpace = " " * int(((44 - len(name)) / 2))
print("\n================================================================")
print("|"+ whiteSpace + "Running setup: " + name + "..." + whiteSpace + " |")
print("================================================================")
for j in range(len(setup)):
print("EXPERIMENT " + str(j + 1) + "\n")
sumError = 0
sumMemory = 0
distincts = []
estimates = []
for l in range(10):
numDistincts = setup[j][1]
# number of bits to use as a bucket number
buckets = setup[j][2]
print("\tIteration " + str(l+1))
print("\tNumber of bits to use as a BUCKET number: " + str(buckets) + " => " + str(2**buckets) + " buckets")
n = setup[j][0]
#numbers = generator.generateRandomIntegers(n, 0, numRange, True)
numbers = generator.generateRandomBitIntegers(n, numDistincts)
distinct = len(np.unique(numbers))
distincts.append(distinct)
result = FM.estimateCardinalityByLogLog(numbers,buckets)
estimate = result.get("estimate")
sumMemory += result.get("memory")
estimates.append(estimate)
print("\n\tActual distinct values: " + str(distinct))
print("\tEstimated distinct values: " + str(estimate))
sumError += printError(distinct, estimate, True)
print("________________________________________________________________")
printReport(sumError, sumMemory, distincts, estimates, 10)
generator.resetGenCounter()
print("________________________________________________________________\n")
def printError(trueCount, estimatedCount, printInfo = False):
# RAE = abs(true_count - estimated_count)/true_count
RAE = abs(trueCount - estimatedCount) / trueCount
if printInfo:
print("\tRelative Approximation Error: " + str(round(RAE,4)))
return RAE
def printReport(sumError, sumMemory, distincts, estimates, length):
print("Mean Actual distinct values: " + str(int(round(np.mean(distincts)))))
print("Mean Estimated distinct values: " + str(int(round(np.mean(estimates)))))
meanError = sumError / length
meanMemory = sumMemory / length
print("Mean Relative Approximation Error: " + str(round(meanError,4)))
print("Mean Memory Usage: " + str(meanMemory) + " bits | " + str(meanMemory / 8) + " Bytes | " + str(
round(((meanMemory / 8) / 1024), 4)) + " kB")
'''for ranger in ranges:
for i in range(10):
main(True,False,ranger)
for ranger in ranges:
print(ranger)
for i in range(1,len(results[ranger])+1):
RAE = sum(results[ranger][i]) / len(results[ranger][i])
print(str(RAE))
print()'''
main(False,True,None)