def weighted_random_walk(self, start, transaction):
walker_on = start
visible_transactions, not_visible_transactions = self.get_visible_transactions(
transaction)
valid_tips = self.get_valid_tips(visible_transactions,
not_visible_transactions)
#If only genesis a valid tip, approve genesis
if (valid_tips == [walker_on]):
return walker_on
while (walker_on not in valid_tips):
approvers = list(self.DG.predecessors(walker_on))
visible_approvers = common_elements(approvers,
visible_transactions)
transition_probabilities = self.calc_transition_probabilities(
visible_approvers)
#Choose with transition probabilities
walker_on = np.random.choice(visible_approvers,
p=transition_probabilities)
return walker_on
def weighted_random_walk(self, transaction, valid_tips, initial_walker_on):
#Start walk at genesis
walker_on = initial_walker_on
# #Start walk at genesis
# walker_on = self.transactions[0]
#If only genesis a valid tip, approve genesis
if (valid_tips == [walker_on]):
return walker_on
while (walker_on not in valid_tips):
approvers = list(self.DG.predecessors(walker_on))
visible_approvers = common_elements(
approvers, transaction.agent.visible_transactions)
transition_probabilities = self.calc_transition_probabilities_multiple_agents(
visible_approvers, transaction.agent)
#Choose with transition probabilities
walker_on = np.random.choice(visible_approvers,
p=transition_probabilities)
return walker_on
def unweighted_random_walk(self, transaction, valid_tips):
#Start walk at genesis
walker_on = self.transactions[0]
#If only genesis a valid tip, approve genesis
if (valid_tips == [walker_on]):
return walker_on
while (walker_on not in valid_tips):
approvers = list(self.DG.predecessors(walker_on))
visible_approvers = common_elements(
approvers, transaction.agent.visible_transactions)
walker_on = np.random.choice(visible_approvers)
return walker_on
def random_walk(self, start, transaction):
walker_on = start
visible_transactions, not_visible_transactions = self.get_visible_transactions(transaction)
valid_tips = self.get_valid_tips(visible_transactions, not_visible_transactions)
#If only genesis a valid tip, approve genesis
if (valid_tips == [walker_on]):
return walker_on
while (walker_on not in valid_tips):
approvers = list(self.DG.predecessors(walker_on))
visible_approvers = common_elements(approvers, visible_transactions)
walker_on = random.choice(visible_approvers)
return walker_on
def calc_exit_probabilities_multiple_agents(self, incoming_transaction):
for agent in self.agents:
#Reset exit probability of all transactions to 0%, just needed when run multiple times throughout simulation
for transaction in self.DG.nodes:
transaction.exit_probability_multiple_agents[agent] = 0
#Set genesis to 100%
self.transactions[0].exit_probability_multiple_agents[agent] = 1
#Determine visible transaction for t + 1, so that all transactions (h = 1) are included
self.get_visible_transactions(
incoming_transaction.arrival_time + self.latency, agent)
#Start at genesis, tips in the end
sorted = list(reversed(list(nx.topological_sort(self.DG))))
#Calculate exit probabilities
for transaction in sorted:
for agent in self.agents:
if (transaction in agent.visible_transactions):
#Get visible direct approvers and transition probabilities to walk to them
approvers = list(self.DG.predecessors(transaction))
visible_approvers = common_elements(
approvers, agent.visible_transactions)
transition_probabilities = self.calc_transition_probabilities_multiple_agents(
visible_approvers, agent)
#For every visible direct approver update the exit probability by adding the exit probability
#of the current transaction times the transition probabilitiy of walking to the approver
for (approver, transition_probability) in zip(
visible_approvers, transition_probabilities):
approver.exit_probability_multiple_agents[agent] += (
transaction.exit_probability_multiple_agents[agent]
* transition_probability)