Python Program Project

import sys ''' WRITE YOUR CODE BELOW. ''' from numpy import zeros, float32, random import numpy as np # pgmpy import pgmpy from pgmpy.models import BayesianModel from pgmpy.factors.discrete import TabularCPD from pgmpy.inference import VariableElimination #You are not allowed to use following set of modules from 'pgmpy' Library. # # pgmpy.sampling.* # pgmpy.factors.* # pgmpy.estimators.* def make_power_plant_net(): """Create a Bayes Net representation of the above power plant problem. Use the following as the name attribute: "alarm","faulty alarm", "gauge","faulty gauge", "temperature". (for the tests to work.) """ # TODO: finish this function BayesNet = BayesianModel() BayesNet.add_node("alarm") BayesNet.add_node("faulty alarm") BayesNet.add_node("gauge") BayesNet.add_node("faulty gauge") BayesNet.add_node("temperature") BayesNet.add_edge("temperature","faulty gauge") BayesNet.add_edge("temperature","gauge") BayesNet.add_edge("faulty gauge","gauge") BayesNet.add_edge("gauge","alarm") BayesNet.add_edge("faulty alarm","alarm") #raise NotImplementedError return BayesNet def set_probability(bayes_net): """Set probability distribution for each node in the power plant system. Use the following as the name attribute: "alarm","faulty alarm", "gauge","faulty gauge", "temperature". (for the tests to work.) """ # TODO: set the probability distribution for each node cpdTemphot = TabularCPD('temperature', 2, values=[[0.8], [0.2]]) cpdAlarmfaul = TabularCPD('faulty alarm', 2, values=[[0.85], [0.15]]) cpdFg_Th = TabularCPD('faulty gauge', 2, values=[[0.95, 0.2],[ 0.05, 0.8]], evidence=['temperature'], evidence_card=[2]) cpdG_Fg_T = TabularCPD('gauge', 2, values=[[0.95, 0.2, 0.05, 0.8], \ [0.05, 0.8, 0.95, 0.2]], evidence=['temperature', 'faulty gauge'], evidence_card=[2, 2]) cpdA_Fa_G = TabularCPD('alarm', 2, values=[[0.9, 0.1, 0.55, 0.45], \ [0.1, 0.9, 0.45, 0.55]], evidence=['faulty alarm', 'gauge'], evidence_card=[2, 2]) bayes_net.add_cpds(cpdTemphot, cpdAlarmfaul, cpdFg_Th, cpdG_Fg_T, cpdA_Fa_G) #raise NotImplementedError return bayes_net def get_alarm_prob(bayes_net): """Calculate the marginal probability of the alarm ringing in the power plant system.""" # TODO: finish this function solver = VariableElimination(bayes_net) marginal_prob = solver.query(variables=['alarm'], joint=False) prob = marginal_prob['alarm'].values alarm_prob = prob[-1] #print(prob) #raise NotImplementedError return alarm_prob def get_gauge_prob(bayes_net): """Calculate the marginal probability of the gauge showing hot in the power plant system.""" # TODO: finish this function solver = VariableElimination(bayes_net) marginal_prob = solver.query(variables=['gauge'], joint=False) prob = marginal_prob['gauge'].values # raise NotImplementedError gauge_prob = prob[-1] return gauge_prob def get_temperature_prob(bayes_net): """Calculate the conditional probability of the temperature being hot in the power plant system, given that the alarm sounds and neither the gauge nor alarm is faulty.""" # TODO: finish this function solver = VariableElimination(bayes_net) conditional_prob = solver.query(variables=['temperature'],evidence={'alarm':1,'faulty alarm':0,'faulty gauge':0}, joint=False) prob = conditional_prob['temperature'].values #raise NotImplementedError temp_prob = prob[-1] return temp_prob # Functions to do for the mindfulness experiment """ Variables in the experiment report: 1. Gender: M or F 2. Age: K (Kid) or A (Adult) 3. Pre-Time: Total puzzle solving time in seconds in the pre-meditation learning session. 4. Pre-Count: Total number of words found when reaching the 5 minutes limit. 5. Active: Number of seconds in “Active” state. 6. Neutral: Number of seconds in “Neutral” state. 7. Calm: Number of seconds in “Calm” state. 8. Recoveries: Number of times the Muse app loses connection to the Muse headband. 9. Birds: Number of bird chirps heard. Every time the subject meditate well, there will be a bid chirp. 10. Post-Time: Total puzzle solving time in seconds in the post-meditation learning session. 11. Post-Counts: Total number of words found when reaching the 5 minutes limit. Variables to be created in the Bayes net, all are Boolean variables: 1. Male: Whether the subject is a male 2. Adult: Whether the subject is an adult. 3. Faster: Whether the subject solves the problem faster after the mindfulness exercises or not. 4. Calm: Is the subject in a calm mind state the majority of the time? 5. Disconnect: Whether the device has ever been disconnected in reading EEG signals. 6. Birds: Whether the subject has entered a good meditating state. """ def make_mindfulness_net(): """Create a Bayes Net representation of the mindfulness experiment. Use the 6 variables listed above. (for the tests to work.) """ BayesNet = BayesianModel() #raise NotImplementedError return BayesNet def set_mindfulness_probability(bayes_net): """Set probability distribution for each node in the mindfulness Bayes net. Use the 6 variables listed above. (for the tests to work.) """ # TODO: set the probability distribution for each node #raise NotImplementedError return bayes_net def get_faster_prob(bayes_net): """Calculate the marginal probability for a subject to solve the problem faster after mindfulness exercise.""" # TODO: finish this function solver = VariableElimination(bayes_net) faster_prob = 0 # This is dummy value, needs to be changed. #print(prob) #raise NotImplementedError return faster_prob def get_birds_prob(bayes_net): """Calculate the marginal probability for a subject to enter good meditating state durng mindfulness exercise.""" # TODO: finish this function solver = VariableElimination(bayes_net) # raise NotImplementedError birds_prob = 0 # This is dummy value, needs to be changed. return birds_prob def get_male_faster_prob(bayes_net): """Calculate the conditional probability whether a male can solve the problem faster after mindfulness.""" # TODO: finish this function solver = VariableElimination(bayes_net) #raise NotImplementedError male_faster_prob = 0 # This is dummy value, needs to be changed. return male_faster_prob def get_adult_faster_prob(bayes_net): """Calculate the conditional probability whether an adult can solve the problem faster after mindfulness.""" # TODO: finish this function solver = VariableElimination(bayes_net) #raise NotImplementedError adult_faster_prob = 0 # This is dummy value, needs to be changed. return adult_faster_prob def get_female_birds_prob(bayes_net): """Calculate the conditional probability for a female subject to enter a good meditating state, given that she doesn't solves the problem faster after mindfulness, but she is calm the majority of the time during mindfulness.""" # TODO: finish this function solver = VariableElimination(bayes_net) #raise NotImplementedError female_birds_prob = 0 # This is dummy value, needs to be changed. return female_birds_prob