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| from __future__ import division | |
| import random | |
| # Set parameters for the simulation | |
| population = 2 ** 10 | |
| trustLines = 16 | |
| people = {} | |
| def initiateWoT(): | |
| for node in range(population): | |
| people[node] = random.sample(range(1, population), trustLines) | |
| initiateWoT() | |
| creditLines = 4 | |
| totalHops = 4 | |
| # Calculate geometric series for creditLines^totalHops | |
| # to get total nodes in branching tree. | |
| # Includes the person paying the tax, 1 + totalHops. | |
| def calculateTreeSize(): | |
| computeTree = 0 | |
| for step in range(1 + totalHops): | |
| computeTree += creditLines ** step | |
| return computeTree | |
| treeSize = calculateTreeSize() | |
| def swarmRedistribution(): | |
| duplicates = {} | |
| branchingTree = {} | |
| taxPayer = random.randint(0, population) | |
| branchingTree[0] = taxPayer | |
| class LinkUtility(): | |
| index = {} | |
| counter = 0 | |
| linkUtility = LinkUtility() | |
| for creditLine in range(creditLines): | |
| node = 0 | |
| while node < trustLines - linkUtility.counter: | |
| issueIOU = random.randint(0, trustLines - 1 - linkUtility.counter) | |
| node += 1 | |
| try: | |
| duplicates[issueIOU] | |
| except: | |
| duplicates[issueIOU] = True | |
| linkUtility.counter += 1 | |
| for step in range(1 + totalHops): | |
| computeBranch = [] | |
| for node in range(creditLines ** step): | |
| taxPayer = random.randint(0, population) | |
| computeBranch.append(taxPayer) | |
| branchingTree[step] = computeBranch | |
| print(branchingTree[0][0]) | |
| # branchingTree = random.sample(range(1, trustLines), treeSize) | |
| del duplicates | |
| swarmRedistribution() |
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