def metric_rewrite(self, possible_rules, narrative):
candidate_narratives = []
candidate_rules = []
#Check all possible rules
for result_rule_pair in possible_rules:
#Make a new candidate narrative
candidate_narrative = narrative.Copy()
#Get a new candidate rule
candidate_rule = result_rule_pair[1]
#Check if the rule has social results
social_results = self.get_social_results(candidate_rule,
candidate_narrative)
#If so we have a potential narrative/rule
if len(social_results) > 0:
result_candidate = self.applyRule(result_rule_pair,
social_results,
candidate_narrative)
candidate_narratives.append(result_candidate)
candidate_rules.append(candidate_rule)
#If we have potential rules, we can now pick the best narrative
#based off of metrics
if len(candidate_narratives) > 0:
#First gather all the metrics
metric_results = []
for candidate_narrative in candidate_narratives:
metrics = Metrics(candidate_narrative.Copy(),
self._metrics_to_optimize_name_only,
self._social_graph.get_preconditions())
metric_results.append(metrics.getMetrics(True))
#Now, using these results and our weights, pick the best metric
optimal_narrative = self.pick_optimal_narrative(metric_results)
#Increment the number of applications
self.inc_num_applications(candidate_rules[optimal_narrative])
#Set the new narrative
self._final_narrative = candidate_narratives[optimal_narrative]
def metric_rewrite(self, possible_rules, narrative): candidate_narratives = [] candidate_rules = [] #Check all possible rules for result_rule_pair in possible_rules: #Make a new candidate narrative candidate_narrative = narrative.Copy() #Get a new candidate rule candidate_rule = result_rule_pair[1] #Check if the rule has social results social_results = self.get_social_results(candidate_rule, candidate_narrative) #If so we have a potential narrative/rule if len(social_results) > 0: result_candidate = self.applyRule(result_rule_pair, social_results, candidate_narrative) candidate_narratives.append(result_candidate) candidate_rules.append(candidate_rule) #If we have potential rules, we can now pick the best narrative #based off of metrics if len(candidate_narratives) > 0: #First gather all the metrics metric_results = [] for candidate_narrative in candidate_narratives: metrics = Metrics(candidate_narrative.Copy(), self._metrics_to_optimize_name_only, self._social_graph.get_preconditions()) metric_results.append(metrics.getMetrics(True)) #Now, using these results and our weights, pick the best metric optimal_narrative = self.pick_optimal_narrative(metric_results) #Increment the number of applications self.inc_num_applications(candidate_rules[optimal_narrative]) #Set the new narrative self._final_narrative = candidate_narratives[optimal_narrative]
def go(self):
total_number_of_nodes = 0
total_number_of_edges = 0
total_num_branches = 0
total_longest_path = 0
total_shortest_path = 0
total_lowest_cost = 0
total_highest_cost = 0
total_fights = 0
total_unique_node_types = 0
total_repetitivity = 0
total_average_length = 0
total_average_cost = 0
total_average_encounters = 0
total_max_encounters = 0
total_min_encounters = 0
total_average_branches = 0
total_max_branches = 0
total_min_branches = 0
total_average_uniqueness = 0
total_max_uniqueness = 0
total_min_uniqueness = 0
total_edge_con = 0
total_attr_con = 0
number_of_nodes=[]
number_of_edges=[]
num_branches=[]
longest_path=[]
shortest_path=[]
lowest_cost=[]
highest_cost=[]
fights=[]
unique_node_types=[]
repetitivity=[]
average_length=[]
average_cost=[]
average_encounters=[]
max_encounters=[]
min_encounters=[]
average_branches=[]
max_branches=[]
min_branches=[]
average_uniqueness=[]
max_uniqueness=[]
min_uniqueness=[]
edge_con=[]
attr_con=[]
all_the_nodes = {}
metrics = []
for graph in self._graph_list:
new_metric = Metrics(graph, self._metrics_to_analyze, self._main_preconditions)
result = new_metric.printMetrics()
metrics.append(result)
if 'number_of_nodes' in result:
total_number_of_nodes += result["number_of_nodes"]
number_of_nodes.append(result["number_of_nodes"])
if 'number_of_edges' in result:
total_number_of_edges += result["number_of_edges"]
number_of_edges.append(result["number_of_edges"])
if 'num_branches' in result:
total_num_branches += result["num_branches"]
num_branches.append(result["num_branches"])
if 'longest_path' in result:
total_longest_path += result["longest_path"][0]
longest_path.append(result["longest_path"][0])
if 'shortest_path' in result:
total_shortest_path += result["shortest_path"][0]
shortest_path.append(result["shortest_path"][0])
if 'highest_cost' in result:
total_highest_cost += result["highest_cost"][0]
highest_cost.append(result["highest_cost"][0])
if 'lowest_cost' in result:
total_lowest_cost += result["lowest_cost"][0]
lowest_cost.append(result["lowest_cost"][0])
if 'fights' in result:
total_fights += result["fights"]
fights.append(result["fights"])
if 'unique_node_types' in result:
total_unique_node_types += result["unique_node_types"]
unique_node_types.append(result["unique_node_types"])
if 'repetitivity' in result:
total_repetitivity += result["repetitivity"]
repetitivity.append(result["repetitivity"])
if 'average_length' in result:
total_average_length += result["average_length"]
average_length.append(result["average_length"])
if 'average_cost' in result:
total_average_cost += result["average_cost"]
average_cost.append(result["average_cost"])
if 'average_encounters' in result:
total_average_encounters += result["average_encounters"]
average_encounters.append(result["average_encounters"])
if 'max_encounters' in result:
total_max_encounters += result["max_encounters"]
max_encounters.append(result["max_encounters"])
if 'min_encounters' in result:
total_min_encounters += result["min_encounters"]
min_encounters.append(result["min_encounters"])
if 'average_branches' in result:
total_average_branches += result["average_branches"]
average_branches.append(result["average_branches"])
if 'max_branches' in result:
total_max_branches += result["max_branches"]
max_branches.append(result["max_branches"])
if 'min_branches' in result:
total_min_branches += result["min_branches"]
min_branches.append(result["min_branches"])
if 'average_uniqueness' in result:
total_average_uniqueness += result["average_uniqueness"]
average_uniqueness.append(result["average_uniqueness"])
if 'max_uniqueness' in result:
total_max_uniqueness += result["max_uniqueness"]
max_uniqueness.append(result["max_uniqueness"])
if 'min_uniqueness' in result:
total_min_uniqueness += result["min_uniqueness"]
min_uniqueness.append(result["min_uniqueness"])
if 'edge_preconditions' in result:
total_edge_con += result["edge_preconditions"]
edge_con.append(result["edge_preconditions"])
if 'attr_preconditions' in result:
total_attr_con += result["attr_preconditions"]
attr_con.append(result["attr_preconditions"])
if 'repetition' in result:
for key in result["repetition"]:
if key in all_the_nodes:
all_the_nodes[key] += result["repetition"][key]
else:
all_the_nodes[key] = result["repetition"][key]
number_of_quests = float(len(metrics))
return_results = {}
print "----------------------------------- AVERAGES -------------------------"
print "\n"
if 'number_of_nodes' in result:
average_number_of_nodes = total_number_of_nodes/number_of_quests
std_dev_number_of_nodes = self.get_standard_deviation(number_of_nodes, average_number_of_nodes, number_of_quests)
return_results['average_number_of_nodes'] = average_number_of_nodes
return_results['std_dev_number_of_nodes'] = std_dev_number_of_nodes
print "\tAverage Number of Nodes : " + str(average_number_of_nodes)
print "\tStandard Deviation Number of Nodes : " + str(std_dev_number_of_nodes)
print "\tTotal Number of Nodes : " + str(total_number_of_nodes)
if 'number_of_edges' in result:
average_number_of_edges = total_number_of_edges/number_of_quests
std_dev_number_of_edges = self.get_standard_deviation(number_of_edges, average_number_of_edges, number_of_quests)
return_results['average_number_of_edges'] = average_number_of_edges
return_results['std_dev_number_of_edges'] = std_dev_number_of_edges
print "\tAverage Number of Edges : " + str(average_number_of_edges)
print "\tStandard Deviation Number of Edges : " + str(std_dev_number_of_edges)
print "\tTotal Number of Edges : " + str(total_number_of_edges)
if 'num_branches' in result:
average_num_branches = total_num_branches/number_of_quests
std_dev_num_branches = self.get_standard_deviation(num_branches, average_num_branches, number_of_quests)
return_results['average_num_branches'] = average_num_branches
return_results['std_dev_num_branches'] = std_dev_num_branches
print "\tAverage Number of Branches : " + str(average_num_branches)
print "\tStandard Deviation Number of Branches : " + str(std_dev_num_branches)
print "\tTotal Number of Branches : " + str(total_num_branches)
if 'average_branches' in result:
average_average_branches = total_average_branches/number_of_quests
std_dev_average_branches = self.get_standard_deviation(average_branches, average_average_branches, number_of_quests)
return_results['average_average_branches'] = average_average_branches
return_results['std_dev_average_branches'] = std_dev_average_branches
print "\tAverage Average Branches : " + str(average_average_branches)
print "\tStandard Average Branches : " + str(std_dev_average_branches)
if 'max_branches' in result:
average_max_branches = total_max_branches/number_of_quests
std_dev_max_branches = self.get_standard_deviation(max_branches, average_max_branches, number_of_quests)
return_results['average_max_branches'] = average_max_branches
return_results['std_dev_max_branches'] = std_dev_max_branches
print "\tAverage Max Branches : " + str(average_max_branches)
print "\tStandard Deviation Max Branches : " + str(std_dev_max_branches)
if 'min_branches' in result:
average_min_branches = total_min_branches/number_of_quests
std_dev_min_branches = self.get_standard_deviation(min_branches, average_min_branches, number_of_quests)
return_results['average_min_branches'] = average_min_branches
return_results['std_dev_min_branches'] = std_dev_min_branches
print "\tAverage Min Branches : " + str(average_min_branches)
print "\tStandard Deviation Min Branches : " + str(std_dev_min_branches)
if 'average_length' in result:
average_average_length = total_average_length/number_of_quests
std_dev_average_length = self.get_standard_deviation(average_length, average_average_length, number_of_quests)
return_results['average_average_length'] = average_average_length
return_results['std_dev_average_length'] = std_dev_average_length
print "\tAverage Average Path : " + str(average_average_length)
print "\tStandard Deviation Average Path : " + str(std_dev_average_length)
if 'longest_path' in result:
average_longest_path = total_longest_path/number_of_quests
std_dev_longest_path = self.get_standard_deviation(longest_path, average_longest_path, number_of_quests)
return_results['average_longest_path'] = average_longest_path
return_results['std_dev_longest_path'] = std_dev_longest_path
print "\tAverage Longest Path : " + str(average_longest_path)
print "\tStandard Deviation Longest Path : " + str(std_dev_longest_path)
if 'shortest_path' in result:
average_shortest_path = total_shortest_path/number_of_quests
std_dev_shortest_path = self.get_standard_deviation(shortest_path, average_shortest_path, number_of_quests)
return_results['average_shortest_path'] = average_shortest_path
return_results['std_dev_shortest_path'] = std_dev_shortest_path
print "\tAverage Shortest Path : " + str(average_shortest_path)
print "\tStandard Deviation Shortest Path : " + str(std_dev_shortest_path)
if 'average_cost' in result:
average_average_cost = total_average_cost/number_of_quests
std_dev_average_cost = self.get_standard_deviation(average_cost, average_average_cost, number_of_quests)
return_results['average_average_cost'] = average_average_cost
return_results['std_dev_average_cost'] = std_dev_average_cost
print "\tAverage Average Cost : " + str(average_average_cost)
print "\tStandard Deviation Average Cost : " + str(std_dev_average_cost)
if 'highest_cost' in result:
average_highest_cost = total_highest_cost/number_of_quests
std_dev_highest_cost = self.get_standard_deviation(highest_cost, average_highest_cost, number_of_quests)
return_results['average_highest_cost'] = average_highest_cost
return_results['std_dev_highest_cost'] = std_dev_highest_cost
print "\tAverage Highest Cost : " + str(average_highest_cost)
print "\tStandard Deviation Highest Cost : " + str(std_dev_highest_cost)
if 'lowest_cost' in result:
average_lowest_cost = total_lowest_cost/number_of_quests
std_dev_lowest_cost = self.get_standard_deviation(lowest_cost, average_lowest_cost, number_of_quests)
return_results['average_lowest_cost'] = average_lowest_cost
return_results['std_dev_lowest_cost'] = std_dev_lowest_cost
print "\tAverage Lowest Cost : " + str(average_lowest_cost)
print "\tStandard Deviation Lowest Cost : " + str(std_dev_lowest_cost)
if 'fights' in result:
average_fights = total_fights/number_of_quests
std_dev_fights = self.get_standard_deviation(fights, average_fights, number_of_quests)
return_results['average_fights'] = average_fights
return_results['std_dev_fights'] = std_dev_fights
print "\tAverage Encounters : " + str(average_fights)
print "\tStandard Deviation Encounters : " + str(std_dev_fights)
print "\tTotal Encounters : " + str(total_fights)
if 'average_encounters' in result:
average_average_encounters = total_average_encounters/number_of_quests
std_dev_average_encounters = self.get_standard_deviation(average_encounters, average_average_encounters, number_of_quests)
return_results['average_average_encounters'] = average_average_encounters
return_results['std_dev_average_encounters'] = std_dev_average_encounters
print "\tAverage Average Encounters : " + str(average_average_encounters)
print "\tStandard Average Encounters : " + str(std_dev_average_encounters)
if 'max_encounters' in result:
average_max_encounters = total_max_encounters/number_of_quests
std_dev_max_encounters = self.get_standard_deviation(max_encounters, average_max_encounters, number_of_quests)
return_results['average_max_encounters'] = average_max_encounters
return_results['std_dev_max_encounters'] = std_dev_max_encounters
print "\tAverage Max Encounters : " + str(average_max_encounters)
print "\tStandard Deviation Max Encounters : " + str(std_dev_max_encounters)
if 'min_encounters' in result:
average_min_encounters = total_min_encounters/number_of_quests
std_dev_min_encounters = self.get_standard_deviation(min_encounters, average_min_encounters, number_of_quests)
return_results['average_min_encounters'] = average_min_encounters
return_results['std_dev_min_encounters'] = std_dev_min_encounters
print "\tAverage Min Encounters : " + str(average_min_encounters)
print "\tStandard Deviation Min Encounters : " + str(std_dev_min_encounters)
if 'repetitivity' in result:
average_repetitivity = total_repetitivity/number_of_quests
std_dev_repetitivity = self.get_standard_deviation(repetitivity, average_repetitivity, number_of_quests)
return_results['average_repetitivity'] = average_repetitivity
return_results['std_dev_repetitivity'] = std_dev_repetitivity
print "\tAverage 'Uniqueness' : " + str(average_repetitivity)
print "\tStandard Deviation Uniqueness : " + str(std_dev_repetitivity)
if 'repetition' in result:
unique_node_types_total = len(all_the_nodes.keys())
repetivity = float(unique_node_types_total)/total_number_of_nodes
return_results['repetivity'] = repetivity
print "\tTotal Uniqueness : " + str(repetivity)
if 'unique_node_types' in result:
average_unique_node_types = total_unique_node_types/number_of_quests
std_dev_unique_node_types = self.get_standard_deviation(unique_node_types, average_unique_node_types, number_of_quests)
return_results['average_unique_node_types'] = average_unique_node_types
return_results['std_dev_unique_node_types'] = std_dev_unique_node_types
print "\tAverage Unique Node Types : " + str(average_unique_node_types)
print "\tStandard Deviation Unique Node Types : " + str(std_dev_unique_node_types)
print "\tTotal Unique Node Types : " + str(total_unique_node_types)
if 'average_uniqueness' in result:
average_average_uniqueness = total_average_uniqueness/number_of_quests
std_dev_average_uniqueness = self.get_standard_deviation(average_uniqueness, average_average_uniqueness, number_of_quests)
return_results['average_average_uniqueness'] = average_average_uniqueness
return_results['std_dev_average_uniqueness'] = std_dev_average_uniqueness
print "\tAverage Average Uniqueness : " + str(average_average_uniqueness)
print "\tStandard Deviation Average Uniqueness : " + str(std_dev_average_uniqueness)
if 'max_uniqueness' in result:
average_max_uniqueness = total_max_uniqueness/number_of_quests
std_dev_max_uniqueness = self.get_standard_deviation(max_uniqueness, average_max_uniqueness, number_of_quests)
return_results['average_max_uniqueness'] = average_max_uniqueness
return_results['std_dev_max_uniqueness'] = std_dev_max_uniqueness
print "\tAverage Max Uniqueness : " + str(average_max_uniqueness)
print "\tStandard Deviation Max Uniqueness : " + str(std_dev_max_uniqueness)
if 'min_uniqueness' in result:
average_min_uniqueness = total_min_uniqueness/number_of_quests
std_dev_min_uniqueness = self.get_standard_deviation(min_uniqueness, average_min_uniqueness, number_of_quests)
return_results['average_min_uniqueness'] = average_min_uniqueness
return_results['std_dev_min_uniqueness'] = std_dev_min_uniqueness
print "\tAverage Min Uniqueness : " + str(average_min_uniqueness)
print "\tStandard Deviation Min Uniqueness : " + str(std_dev_min_uniqueness)
if 'edge_preconditions' in result:
average_edge_con = total_edge_con/number_of_quests
std_dev_edge_con = self.get_standard_deviation(edge_con, average_edge_con, number_of_quests)
return_results['average_edge_con'] = average_edge_con
return_results['std_dev_edge_con'] = std_dev_edge_con
print "\tAverage Influence of Narratives on Edge Preconditions : " + str(average_edge_con)
print "\tStandard Deviation Influence of Narratives on Edge Preconditions : " + str(std_dev_edge_con)
if 'attr_preconditions' in result:
average_attr_con = total_attr_con/number_of_quests
std_dev_attr_con = self.get_standard_deviation(attr_con, average_attr_con, number_of_quests)
return_results['average_attr_con'] = average_attr_con
return_results['std_dev_attr_con'] = std_dev_attr_con
print "\tAverage Influence of Narratives on Attribute Preconditions : " + str(average_attr_con)
print "\tStandard Deviation Influence of Narratives on Attribute Preconditions : " + str(std_dev_attr_con)
return return_results
def go(self):
total_number_of_nodes = 0
total_number_of_edges = 0
total_num_branches = 0
total_longest_path = 0
total_shortest_path = 0
total_lowest_cost = 0
total_highest_cost = 0
total_fights = 0
total_unique_node_types = 0
total_repetitivity = 0
total_average_length = 0
total_average_cost = 0
total_average_encounters = 0
total_max_encounters = 0
total_min_encounters = 0
total_average_branches = 0
total_max_branches = 0
total_min_branches = 0
total_average_uniqueness = 0
total_max_uniqueness = 0
total_min_uniqueness = 0
total_edge_con = 0
total_attr_con = 0
number_of_nodes = []
number_of_edges = []
num_branches = []
longest_path = []
shortest_path = []
lowest_cost = []
highest_cost = []
fights = []
unique_node_types = []
repetitivity = []
average_length = []
average_cost = []
average_encounters = []
max_encounters = []
min_encounters = []
average_branches = []
max_branches = []
min_branches = []
average_uniqueness = []
max_uniqueness = []
min_uniqueness = []
edge_con = []
attr_con = []
all_the_nodes = {}
metrics = []
for graph in self._graph_list:
new_metric = Metrics(graph, self._metrics_to_analyze,
self._main_preconditions)
result = new_metric.printMetrics()
metrics.append(result)
if 'number_of_nodes' in result:
total_number_of_nodes += result["number_of_nodes"]
number_of_nodes.append(result["number_of_nodes"])
if 'number_of_edges' in result:
total_number_of_edges += result["number_of_edges"]
number_of_edges.append(result["number_of_edges"])
if 'num_branches' in result:
total_num_branches += result["num_branches"]
num_branches.append(result["num_branches"])
if 'longest_path' in result:
total_longest_path += result["longest_path"][0]
longest_path.append(result["longest_path"][0])
if 'shortest_path' in result:
total_shortest_path += result["shortest_path"][0]
shortest_path.append(result["shortest_path"][0])
if 'highest_cost' in result:
total_highest_cost += result["highest_cost"][0]
highest_cost.append(result["highest_cost"][0])
if 'lowest_cost' in result:
total_lowest_cost += result["lowest_cost"][0]
lowest_cost.append(result["lowest_cost"][0])
if 'fights' in result:
total_fights += result["fights"]
fights.append(result["fights"])
if 'unique_node_types' in result:
total_unique_node_types += result["unique_node_types"]
unique_node_types.append(result["unique_node_types"])
if 'repetitivity' in result:
total_repetitivity += result["repetitivity"]
repetitivity.append(result["repetitivity"])
if 'average_length' in result:
total_average_length += result["average_length"]
average_length.append(result["average_length"])
if 'average_cost' in result:
total_average_cost += result["average_cost"]
average_cost.append(result["average_cost"])
if 'average_encounters' in result:
total_average_encounters += result["average_encounters"]
average_encounters.append(result["average_encounters"])
if 'max_encounters' in result:
total_max_encounters += result["max_encounters"]
max_encounters.append(result["max_encounters"])
if 'min_encounters' in result:
total_min_encounters += result["min_encounters"]
min_encounters.append(result["min_encounters"])
if 'average_branches' in result:
total_average_branches += result["average_branches"]
average_branches.append(result["average_branches"])
if 'max_branches' in result:
total_max_branches += result["max_branches"]
max_branches.append(result["max_branches"])
if 'min_branches' in result:
total_min_branches += result["min_branches"]
min_branches.append(result["min_branches"])
if 'average_uniqueness' in result:
total_average_uniqueness += result["average_uniqueness"]
average_uniqueness.append(result["average_uniqueness"])
if 'max_uniqueness' in result:
total_max_uniqueness += result["max_uniqueness"]
max_uniqueness.append(result["max_uniqueness"])
if 'min_uniqueness' in result:
total_min_uniqueness += result["min_uniqueness"]
min_uniqueness.append(result["min_uniqueness"])
if 'edge_preconditions' in result:
total_edge_con += result["edge_preconditions"]
edge_con.append(result["edge_preconditions"])
if 'attr_preconditions' in result:
total_attr_con += result["attr_preconditions"]
attr_con.append(result["attr_preconditions"])
if 'repetition' in result:
for key in result["repetition"]:
if key in all_the_nodes:
all_the_nodes[key] += result["repetition"][key]
else:
all_the_nodes[key] = result["repetition"][key]
number_of_quests = float(len(metrics))
return_results = {}
print "----------------------------------- AVERAGES -------------------------"
print "\n"
if 'number_of_nodes' in result:
average_number_of_nodes = total_number_of_nodes / number_of_quests
std_dev_number_of_nodes = self.get_standard_deviation(
number_of_nodes, average_number_of_nodes, number_of_quests)
return_results['average_number_of_nodes'] = average_number_of_nodes
return_results['std_dev_number_of_nodes'] = std_dev_number_of_nodes
print "\tAverage Number of Nodes : " + str(average_number_of_nodes)
print "\tStandard Deviation Number of Nodes : " + str(
std_dev_number_of_nodes)
print "\tTotal Number of Nodes : " + str(total_number_of_nodes)
if 'number_of_edges' in result:
average_number_of_edges = total_number_of_edges / number_of_quests
std_dev_number_of_edges = self.get_standard_deviation(
number_of_edges, average_number_of_edges, number_of_quests)
return_results['average_number_of_edges'] = average_number_of_edges
return_results['std_dev_number_of_edges'] = std_dev_number_of_edges
print "\tAverage Number of Edges : " + str(average_number_of_edges)
print "\tStandard Deviation Number of Edges : " + str(
std_dev_number_of_edges)
print "\tTotal Number of Edges : " + str(total_number_of_edges)
if 'num_branches' in result:
average_num_branches = total_num_branches / number_of_quests
std_dev_num_branches = self.get_standard_deviation(
num_branches, average_num_branches, number_of_quests)
return_results['average_num_branches'] = average_num_branches
return_results['std_dev_num_branches'] = std_dev_num_branches
print "\tAverage Number of Branches : " + str(average_num_branches)
print "\tStandard Deviation Number of Branches : " + str(
std_dev_num_branches)
print "\tTotal Number of Branches : " + str(total_num_branches)
if 'average_branches' in result:
average_average_branches = total_average_branches / number_of_quests
std_dev_average_branches = self.get_standard_deviation(
average_branches, average_average_branches, number_of_quests)
return_results[
'average_average_branches'] = average_average_branches
return_results[
'std_dev_average_branches'] = std_dev_average_branches
print "\tAverage Average Branches : " + str(
average_average_branches)
print "\tStandard Average Branches : " + str(
std_dev_average_branches)
if 'max_branches' in result:
average_max_branches = total_max_branches / number_of_quests
std_dev_max_branches = self.get_standard_deviation(
max_branches, average_max_branches, number_of_quests)
return_results['average_max_branches'] = average_max_branches
return_results['std_dev_max_branches'] = std_dev_max_branches
print "\tAverage Max Branches : " + str(average_max_branches)
print "\tStandard Deviation Max Branches : " + str(
std_dev_max_branches)
if 'min_branches' in result:
average_min_branches = total_min_branches / number_of_quests
std_dev_min_branches = self.get_standard_deviation(
min_branches, average_min_branches, number_of_quests)
return_results['average_min_branches'] = average_min_branches
return_results['std_dev_min_branches'] = std_dev_min_branches
print "\tAverage Min Branches : " + str(average_min_branches)
print "\tStandard Deviation Min Branches : " + str(
std_dev_min_branches)
if 'average_length' in result:
average_average_length = total_average_length / number_of_quests
std_dev_average_length = self.get_standard_deviation(
average_length, average_average_length, number_of_quests)
return_results['average_average_length'] = average_average_length
return_results['std_dev_average_length'] = std_dev_average_length
print "\tAverage Average Path : " + str(average_average_length)
print "\tStandard Deviation Average Path : " + str(
std_dev_average_length)
if 'longest_path' in result:
average_longest_path = total_longest_path / number_of_quests
std_dev_longest_path = self.get_standard_deviation(
longest_path, average_longest_path, number_of_quests)
return_results['average_longest_path'] = average_longest_path
return_results['std_dev_longest_path'] = std_dev_longest_path
print "\tAverage Longest Path : " + str(average_longest_path)
print "\tStandard Deviation Longest Path : " + str(
std_dev_longest_path)
if 'shortest_path' in result:
average_shortest_path = total_shortest_path / number_of_quests
std_dev_shortest_path = self.get_standard_deviation(
shortest_path, average_shortest_path, number_of_quests)
return_results['average_shortest_path'] = average_shortest_path
return_results['std_dev_shortest_path'] = std_dev_shortest_path
print "\tAverage Shortest Path : " + str(average_shortest_path)
print "\tStandard Deviation Shortest Path : " + str(
std_dev_shortest_path)
if 'average_cost' in result:
average_average_cost = total_average_cost / number_of_quests
std_dev_average_cost = self.get_standard_deviation(
average_cost, average_average_cost, number_of_quests)
return_results['average_average_cost'] = average_average_cost
return_results['std_dev_average_cost'] = std_dev_average_cost
print "\tAverage Average Cost : " + str(average_average_cost)
print "\tStandard Deviation Average Cost : " + str(
std_dev_average_cost)
if 'highest_cost' in result:
average_highest_cost = total_highest_cost / number_of_quests
std_dev_highest_cost = self.get_standard_deviation(
highest_cost, average_highest_cost, number_of_quests)
return_results['average_highest_cost'] = average_highest_cost
return_results['std_dev_highest_cost'] = std_dev_highest_cost
print "\tAverage Highest Cost : " + str(average_highest_cost)
print "\tStandard Deviation Highest Cost : " + str(
std_dev_highest_cost)
if 'lowest_cost' in result:
average_lowest_cost = total_lowest_cost / number_of_quests
std_dev_lowest_cost = self.get_standard_deviation(
lowest_cost, average_lowest_cost, number_of_quests)
return_results['average_lowest_cost'] = average_lowest_cost
return_results['std_dev_lowest_cost'] = std_dev_lowest_cost
print "\tAverage Lowest Cost : " + str(average_lowest_cost)
print "\tStandard Deviation Lowest Cost : " + str(
std_dev_lowest_cost)
if 'fights' in result:
average_fights = total_fights / number_of_quests
std_dev_fights = self.get_standard_deviation(
fights, average_fights, number_of_quests)
return_results['average_fights'] = average_fights
return_results['std_dev_fights'] = std_dev_fights
print "\tAverage Encounters : " + str(average_fights)
print "\tStandard Deviation Encounters : " + str(std_dev_fights)
print "\tTotal Encounters : " + str(total_fights)
if 'average_encounters' in result:
average_average_encounters = total_average_encounters / number_of_quests
std_dev_average_encounters = self.get_standard_deviation(
average_encounters, average_average_encounters,
number_of_quests)
return_results[
'average_average_encounters'] = average_average_encounters
return_results[
'std_dev_average_encounters'] = std_dev_average_encounters
print "\tAverage Average Encounters : " + str(
average_average_encounters)
print "\tStandard Average Encounters : " + str(
std_dev_average_encounters)
if 'max_encounters' in result:
average_max_encounters = total_max_encounters / number_of_quests
std_dev_max_encounters = self.get_standard_deviation(
max_encounters, average_max_encounters, number_of_quests)
return_results['average_max_encounters'] = average_max_encounters
return_results['std_dev_max_encounters'] = std_dev_max_encounters
print "\tAverage Max Encounters : " + str(average_max_encounters)
print "\tStandard Deviation Max Encounters : " + str(
std_dev_max_encounters)
if 'min_encounters' in result:
average_min_encounters = total_min_encounters / number_of_quests
std_dev_min_encounters = self.get_standard_deviation(
min_encounters, average_min_encounters, number_of_quests)
return_results['average_min_encounters'] = average_min_encounters
return_results['std_dev_min_encounters'] = std_dev_min_encounters
print "\tAverage Min Encounters : " + str(average_min_encounters)
print "\tStandard Deviation Min Encounters : " + str(
std_dev_min_encounters)
if 'repetitivity' in result:
average_repetitivity = total_repetitivity / number_of_quests
std_dev_repetitivity = self.get_standard_deviation(
repetitivity, average_repetitivity, number_of_quests)
return_results['average_repetitivity'] = average_repetitivity
return_results['std_dev_repetitivity'] = std_dev_repetitivity
print "\tAverage 'Uniqueness' : " + str(average_repetitivity)
print "\tStandard Deviation Uniqueness : " + str(
std_dev_repetitivity)
if 'repetition' in result:
unique_node_types_total = len(all_the_nodes.keys())
repetivity = float(unique_node_types_total) / total_number_of_nodes
return_results['repetivity'] = repetivity
print "\tTotal Uniqueness : " + str(repetivity)
if 'unique_node_types' in result:
average_unique_node_types = total_unique_node_types / number_of_quests
std_dev_unique_node_types = self.get_standard_deviation(
unique_node_types, average_unique_node_types, number_of_quests)
return_results[
'average_unique_node_types'] = average_unique_node_types
return_results[
'std_dev_unique_node_types'] = std_dev_unique_node_types
print "\tAverage Unique Node Types : " + str(
average_unique_node_types)
print "\tStandard Deviation Unique Node Types : " + str(
std_dev_unique_node_types)
print "\tTotal Unique Node Types : " + str(total_unique_node_types)
if 'average_uniqueness' in result:
average_average_uniqueness = total_average_uniqueness / number_of_quests
std_dev_average_uniqueness = self.get_standard_deviation(
average_uniqueness, average_average_uniqueness,
number_of_quests)
return_results[
'average_average_uniqueness'] = average_average_uniqueness
return_results[
'std_dev_average_uniqueness'] = std_dev_average_uniqueness
print "\tAverage Average Uniqueness : " + str(
average_average_uniqueness)
print "\tStandard Deviation Average Uniqueness : " + str(
std_dev_average_uniqueness)
if 'max_uniqueness' in result:
average_max_uniqueness = total_max_uniqueness / number_of_quests
std_dev_max_uniqueness = self.get_standard_deviation(
max_uniqueness, average_max_uniqueness, number_of_quests)
return_results['average_max_uniqueness'] = average_max_uniqueness
return_results['std_dev_max_uniqueness'] = std_dev_max_uniqueness
print "\tAverage Max Uniqueness : " + str(average_max_uniqueness)
print "\tStandard Deviation Max Uniqueness : " + str(
std_dev_max_uniqueness)
if 'min_uniqueness' in result:
average_min_uniqueness = total_min_uniqueness / number_of_quests
std_dev_min_uniqueness = self.get_standard_deviation(
min_uniqueness, average_min_uniqueness, number_of_quests)
return_results['average_min_uniqueness'] = average_min_uniqueness
return_results['std_dev_min_uniqueness'] = std_dev_min_uniqueness
print "\tAverage Min Uniqueness : " + str(average_min_uniqueness)
print "\tStandard Deviation Min Uniqueness : " + str(
std_dev_min_uniqueness)
if 'edge_preconditions' in result:
average_edge_con = total_edge_con / number_of_quests
std_dev_edge_con = self.get_standard_deviation(
edge_con, average_edge_con, number_of_quests)
return_results['average_edge_con'] = average_edge_con
return_results['std_dev_edge_con'] = std_dev_edge_con
print "\tAverage Influence of Narratives on Edge Preconditions : " + str(
average_edge_con)
print "\tStandard Deviation Influence of Narratives on Edge Preconditions : " + str(
std_dev_edge_con)
if 'attr_preconditions' in result:
average_attr_con = total_attr_con / number_of_quests
std_dev_attr_con = self.get_standard_deviation(
attr_con, average_attr_con, number_of_quests)
return_results['average_attr_con'] = average_attr_con
return_results['std_dev_attr_con'] = std_dev_attr_con
print "\tAverage Influence of Narratives on Attribute Preconditions : " + str(
average_attr_con)
print "\tStandard Deviation Influence of Narratives on Attribute Preconditions : " + str(
std_dev_attr_con)
return return_results
