2015-12-04 10:41:42 +00:00
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# settings.py
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def init():
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global number_of_nodes
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global max_time
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global num_trials
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global bite_prob
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global network_type
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global heal_prob
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global innovation_prob
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global imitation_prob
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global timeout
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global outside_effects_prob
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global anger_prob
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global joy_prob
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global sadness_prob
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global disgust_prob
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2015-12-17 11:56:39 +00:00
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global tweet_probability_users
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global tweet_relevant_probability
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global tweet_probability_about
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global sentiment_about
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global tweet_probability_enterprises
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2016-04-13 16:08:41 +00:00
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global enterprises
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2016-04-18 10:23:24 +00:00
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global neutral_discontent_spon_prob
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global neutral_discontent_infected_prob
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global neutral_content_spon_prob
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global neutral_content_infected_prob
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global discontent_content
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global discontent_neutral
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global content_discontent
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global content_neutral
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global variance_d_c
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global variance_c_d
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global standard_variance
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2016-04-21 10:56:06 +00:00
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global prob_neutral_making_denier
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global prob_infect
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global prob_cured_healing_infected
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global prob_cured_vaccinate_neutral
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global prob_vaccinated_healing_infected
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global prob_vaccinated_vaccinate_neutral
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global prob_generate_anti_rumor
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2015-12-04 10:41:42 +00:00
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network_type=1
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2016-05-04 10:20:23 +00:00
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number_of_nodes=1000
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2016-04-28 11:17:44 +00:00
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max_time=50
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2015-12-04 10:41:42 +00:00
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num_trials=1
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2016-05-04 10:20:23 +00:00
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timeout=2
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2015-12-04 10:41:42 +00:00
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#Zombie model
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bite_prob=0.01 # 0-1
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heal_prob=0.01 # 0-1
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#Bass model
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2016-04-13 16:08:41 +00:00
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innovation_prob=0.001
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imitation_prob=0.005
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2015-12-04 10:41:42 +00:00
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2015-12-17 11:56:39 +00:00
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#Sentiment Correlation model
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2015-12-04 10:41:42 +00:00
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outside_effects_prob = 0.2
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2016-04-13 16:08:41 +00:00
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anger_prob = 0.06
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2015-12-04 10:41:42 +00:00
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joy_prob = 0.05
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sadness_prob = 0.02
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disgust_prob = 0.02
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2015-12-17 11:56:39 +00:00
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#Big Market model
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2016-04-13 16:08:41 +00:00
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##Names
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enterprises = ["BBVA","Santander", "Bankia"]
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2015-12-17 11:56:39 +00:00
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##Users
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tweet_probability_users = 0.44
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tweet_relevant_probability = 0.25
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2016-04-13 16:08:41 +00:00
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tweet_probability_about = [0.15, 0.15, 0.15]
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sentiment_about = [0, 0, 0] #Valores por defecto
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2015-12-17 11:56:39 +00:00
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##Enterprises
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2016-04-13 16:08:41 +00:00
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tweet_probability_enterprises = [0.3, 0.3, 0.3]
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2015-12-17 11:56:39 +00:00
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2016-04-18 10:23:24 +00:00
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#SISa
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neutral_discontent_spon_prob = 0.04
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neutral_discontent_infected_prob = 0.04
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neutral_content_spon_prob = 0.18
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neutral_content_infected_prob = 0.02
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discontent_neutral = 0.13
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discontent_content = 0.07
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variance_d_c = 0.02
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content_discontent = 0.009
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variance_c_d = 0.003
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content_neutral = 0.088
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2016-04-21 10:56:06 +00:00
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standard_variance = 0.055
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2016-05-04 10:20:23 +00:00
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#Spread Model M2 and Control Model M2
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2016-04-27 11:30:31 +00:00
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prob_neutral_making_denier = 0.035
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2016-04-21 10:56:06 +00:00
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2016-04-27 11:30:31 +00:00
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prob_infect = 0.075
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2016-04-21 10:56:06 +00:00
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2016-04-27 11:30:31 +00:00
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prob_cured_healing_infected = 0.035
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prob_cured_vaccinate_neutral = 0.035
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2016-04-21 10:56:06 +00:00
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2016-04-27 11:30:31 +00:00
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prob_vaccinated_healing_infected = 0.035
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prob_vaccinated_vaccinate_neutral = 0.035
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prob_generate_anti_rumor = 0.035
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2016-04-21 10:56:06 +00:00
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2016-04-18 10:23:24 +00:00
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