mirror of https://github.com/gsi-upm/soil
Settings modules
parent
dd4ce15a3d
commit
f29f5fa5bf
@ -1,103 +1,62 @@
|
||||
# settings.py
|
||||
def init():
|
||||
global number_of_nodes
|
||||
global max_time
|
||||
global num_trials
|
||||
global bite_prob
|
||||
global network_type
|
||||
global heal_prob
|
||||
global innovation_prob
|
||||
global imitation_prob
|
||||
global timeout
|
||||
global outside_effects_prob
|
||||
global anger_prob
|
||||
global joy_prob
|
||||
global sadness_prob
|
||||
global disgust_prob
|
||||
global tweet_probability_users
|
||||
global tweet_relevant_probability
|
||||
global tweet_probability_about
|
||||
global sentiment_about
|
||||
global tweet_probability_enterprises
|
||||
global enterprises
|
||||
global neutral_discontent_spon_prob
|
||||
global neutral_discontent_infected_prob
|
||||
global neutral_content_spon_prob
|
||||
global neutral_content_infected_prob
|
||||
global discontent_content
|
||||
global discontent_neutral
|
||||
global content_discontent
|
||||
global content_neutral
|
||||
global variance_d_c
|
||||
global variance_c_d
|
||||
global standard_variance
|
||||
global prob_neutral_making_denier
|
||||
global prob_infect
|
||||
global prob_cured_healing_infected
|
||||
global prob_cured_vaccinate_neutral
|
||||
global prob_vaccinated_healing_infected
|
||||
global prob_vaccinated_vaccinate_neutral
|
||||
global prob_generate_anti_rumor
|
||||
|
||||
network_type=1
|
||||
number_of_nodes=1000
|
||||
max_time=50
|
||||
num_trials=1
|
||||
timeout=2
|
||||
|
||||
#Zombie model
|
||||
bite_prob=0.01 # 0-1
|
||||
heal_prob=0.01 # 0-1
|
||||
|
||||
#Bass model
|
||||
innovation_prob=0.001
|
||||
imitation_prob=0.005
|
||||
|
||||
#Sentiment Correlation model
|
||||
outside_effects_prob = 0.2
|
||||
anger_prob = 0.06
|
||||
joy_prob = 0.05
|
||||
sadness_prob = 0.02
|
||||
disgust_prob = 0.02
|
||||
|
||||
#Big Market model
|
||||
##Names
|
||||
enterprises = ["BBVA","Santander", "Bankia"]
|
||||
##Users
|
||||
tweet_probability_users = 0.44
|
||||
tweet_relevant_probability = 0.25
|
||||
tweet_probability_about = [0.15, 0.15, 0.15]
|
||||
sentiment_about = [0, 0, 0] #Default values
|
||||
##Enterprises
|
||||
tweet_probability_enterprises = [0.3, 0.3, 0.3]
|
||||
|
||||
#SISa
|
||||
neutral_discontent_spon_prob = 0.04
|
||||
neutral_discontent_infected_prob = 0.04
|
||||
neutral_content_spon_prob = 0.18
|
||||
neutral_content_infected_prob = 0.02
|
||||
|
||||
discontent_neutral = 0.13
|
||||
discontent_content = 0.07
|
||||
variance_d_c = 0.02
|
||||
|
||||
content_discontent = 0.009
|
||||
variance_c_d = 0.003
|
||||
content_neutral = 0.088
|
||||
|
||||
standard_variance = 0.055
|
||||
|
||||
#Spread Model M2 and Control Model M2
|
||||
prob_neutral_making_denier = 0.035
|
||||
|
||||
prob_infect = 0.075
|
||||
|
||||
prob_cured_healing_infected = 0.035
|
||||
prob_cured_vaccinate_neutral = 0.035
|
||||
|
||||
prob_vaccinated_healing_infected = 0.035
|
||||
prob_vaccinated_vaccinate_neutral = 0.035
|
||||
prob_generate_anti_rumor = 0.035
|
||||
|
||||
|
||||
|
||||
# General configuration
|
||||
|
||||
# Network settings
|
||||
network_type = 1
|
||||
number_of_nodes = 1000
|
||||
max_time = 50
|
||||
num_trials = 1
|
||||
timeout = 2
|
||||
|
||||
# Zombie model
|
||||
bite_prob = 0.01 # 0-1
|
||||
heal_prob = 0.01 # 0-1
|
||||
|
||||
# Bass model
|
||||
innovation_prob = 0.001
|
||||
imitation_prob = 0.005
|
||||
|
||||
# Sentiment Correlation model
|
||||
outside_effects_prob = 0.2
|
||||
anger_prob = 0.06
|
||||
joy_prob = 0.05
|
||||
sadness_prob = 0.02
|
||||
disgust_prob = 0.02
|
||||
|
||||
# Big Market model
|
||||
## Names
|
||||
enterprises = ["BBVA", "Santander", "Bankia"]
|
||||
## Users
|
||||
tweet_probability_users = 0.44
|
||||
tweet_relevant_probability = 0.25
|
||||
tweet_probability_about = [0.15, 0.15, 0.15]
|
||||
sentiment_about = [0, 0, 0] # Default values
|
||||
## Enterprises
|
||||
tweet_probability_enterprises = [0.3, 0.3, 0.3]
|
||||
|
||||
# SISa
|
||||
neutral_discontent_spon_prob = 0.04
|
||||
neutral_discontent_infected_prob = 0.04
|
||||
neutral_content_spon_prob = 0.18
|
||||
neutral_content_infected_prob = 0.02
|
||||
|
||||
discontent_neutral = 0.13
|
||||
discontent_content = 0.07
|
||||
variance_d_c = 0.02
|
||||
|
||||
content_discontent = 0.009
|
||||
variance_c_d = 0.003
|
||||
content_neutral = 0.088
|
||||
|
||||
standard_variance = 0.055
|
||||
|
||||
# Spread Model M2 and Control Model M2
|
||||
prob_neutral_making_denier = 0.035
|
||||
|
||||
prob_infect = 0.075
|
||||
|
||||
prob_cured_healing_infected = 0.035
|
||||
prob_cured_vaccinate_neutral = 0.035
|
||||
|
||||
prob_vaccinated_healing_infected = 0.035
|
||||
prob_vaccinated_vaccinate_neutral = 0.035
|
||||
prob_generate_anti_rumor = 0.035
|
||||
|
@ -0,0 +1,104 @@
|
||||
# settings.py
|
||||
def init():
|
||||
global number_of_nodes
|
||||
global max_time
|
||||
global num_trials
|
||||
global bite_prob
|
||||
global timeout
|
||||
global network_type
|
||||
global heal_prob
|
||||
global innovation_prob
|
||||
global imitation_prob
|
||||
global outside_effects_prob
|
||||
global anger_prob
|
||||
global joy_prob
|
||||
global sadness_prob
|
||||
global disgust_prob
|
||||
global tweet_probability_users
|
||||
global tweet_relevant_probability
|
||||
global tweet_probability_about
|
||||
global sentiment_about
|
||||
global tweet_probability_enterprises
|
||||
global enterprises
|
||||
global neutral_discontent_spon_prob
|
||||
global neutral_discontent_infected_prob
|
||||
global neutral_content_spon_prob
|
||||
global neutral_content_infected_prob
|
||||
global discontent_content
|
||||
global discontent_neutral
|
||||
global content_discontent
|
||||
global content_neutral
|
||||
global variance_d_c
|
||||
global variance_c_d
|
||||
global standard_variance
|
||||
global prob_neutral_making_denier
|
||||
global prob_infect
|
||||
global prob_cured_healing_infected
|
||||
global prob_cured_vaccinate_neutral
|
||||
global prob_vaccinated_healing_infected
|
||||
global prob_vaccinated_vaccinate_neutral
|
||||
global prob_generate_anti_rumor
|
||||
|
||||
# Network settings
|
||||
network_type = 1
|
||||
number_of_nodes = 1000
|
||||
max_time = 50
|
||||
num_trials = 1
|
||||
timeout = 2
|
||||
|
||||
# Zombie model
|
||||
bite_prob = 0.01 # 0-1
|
||||
heal_prob = 0.01 # 0-1
|
||||
|
||||
# Bass model
|
||||
innovation_prob = 0.001
|
||||
imitation_prob = 0.005
|
||||
|
||||
# Sentiment Correlation model
|
||||
outside_effects_prob = 0.2
|
||||
anger_prob = 0.06
|
||||
joy_prob = 0.05
|
||||
sadness_prob = 0.02
|
||||
disgust_prob = 0.02
|
||||
|
||||
# Big Market model
|
||||
## Names
|
||||
enterprises = ["BBVA", "Santander", "Bankia"]
|
||||
## Users
|
||||
tweet_probability_users = 0.44
|
||||
tweet_relevant_probability = 0.25
|
||||
tweet_probability_about = [0.15, 0.15, 0.15]
|
||||
sentiment_about = [0, 0, 0] # Default values
|
||||
## Enterprises
|
||||
tweet_probability_enterprises = [0.3, 0.3, 0.3]
|
||||
|
||||
# SISa
|
||||
neutral_discontent_spon_prob = 0.04
|
||||
neutral_discontent_infected_prob = 0.04
|
||||
neutral_content_spon_prob = 0.18
|
||||
neutral_content_infected_prob = 0.02
|
||||
|
||||
discontent_neutral = 0.13
|
||||
discontent_content = 0.07
|
||||
variance_d_c = 0.02
|
||||
|
||||
content_discontent = 0.009
|
||||
variance_c_d = 0.003
|
||||
content_neutral = 0.088
|
||||
|
||||
standard_variance = 0.055
|
||||
|
||||
# Spread Model M2 and Control Model M2
|
||||
prob_neutral_making_denier = 0.035
|
||||
|
||||
prob_infect = 0.075
|
||||
|
||||
prob_cured_healing_infected = 0.035
|
||||
prob_cured_vaccinate_neutral = 0.035
|
||||
|
||||
prob_vaccinated_healing_infected = 0.035
|
||||
prob_vaccinated_vaccinate_neutral = 0.035
|
||||
prob_generate_anti_rumor = 0.035
|
||||
|
||||
|
||||
|
Loading…
Reference in New Issue