Test #1: Build Naïve Bayes classifiers from give rock-paper-scissor transcripts using numpy

• Rock-paper-scissor transcripts: As in Lab 4 and Lab 5, examine the same zip file containing two sample transcripts generated from playing with Agent#1 and Agent#2 respectively. For each transcript in the zip file, use numpy.loadtext to load the data into a 2-D numpy array to conduct basic data analysis on the data collected in these two transcripts as described below. For the test, we want to implement functions to learn from the rock-paper-scissor transcript data to build Naïve Bayes classifiers for prediction.
• Implement the following functions for Naïve Bayes classification: Create a Jupiter notebook test1.ipynb to do the following
1. Implement a function getNaiveBayesStatistics(TrainingData) that can process the rock-paper-scissor transcript stored in a 2-D numpy array TrainingData and return a python dictionary that holds the information of all the statistics needed for Naïve Bayes classification. The dictionary should be in the format of { 'agentPrev1': agentPrev1, 'agentPrev2': agentPrev2, 'userPrev1': userPrev1, 'userPrev2': userPrev2, 'prior':prior, } where

a) prior is a 1-D numpy array storing the probability distribution of the agent playing rock or paper or scissor over all the matches recorded in the entire transcript,

b) agentPrev1 is a 2-D numpy array storing the probability distributions of the agent playing rock or paper or scissor in the previous match given that the agent plays rock or paper or scissor respectively in the current match,

c) agentPrev2 is a 2-D numpy array storing the probability distributions of the agent playing rock or paper or scissor 2 matches ago given that the agent plays rock or paper or scissor respectively in the current match,

d) userPrev1 is a 2-D numpy array storing the probability distributions of the user playing rock or paper or scissor in the previous match given that the agent plays rock or paper or scissor respectively in the current match, and

e) userPrev2 is a 2-D numpy array storing the probability distributions of the user playing rock or paper or scissor 2 matches ago given that the agent plays rock or paper or scissor respectively in the current match.

Note that you can accomplish this task conveniently based on similar things what you learned and did for Lab 4 and Lab 5 earlier.

2. Implement a function getProbabilityNaiveBayesOneCase(NiaiveStatisticsDictionary, newCase) that can return a 1-D numpy array storing the predicted probabilities of seeing the agent playing rock or paper or scissor in a newCase given the Naïve Bayes statistics stored in NaiveStatisticsDictionary. Note that newCase should be a 1-D numpy array for storing the agent’s actions in the previous two moves and the user’s actions in the previous two moves while NaiveStatisticsDictionary should be a dictionary in the format described in the implementation of getNaiveBayesStatistics above. The task of the function is to apply the Naïve Bayes classification to the new case based on the Naïve Bayes statistics stored in NaiveStatisticsDictionary.
3. Implement a function predictNaiveBayesOneCase(NiaiveStatisticsDictionary, newCase) that can return the most likely action (rock or paper or scissor as a number). This function is essentially a variant of getProbabilityNaiveBayesOneCase(statisticsDictionary, newCase) but return the most likely action (rock or paper or scissor as a number) instead of the probabilities of all three of them.
4. Implement a function predictNaiveBayesWholeTranscript(NiaiveStatisticsDictionary, TestingData) to examine all the matches in a rock-paper-scissor transcript stored in a 2-D numpy array TesingData, properly call predictNaiveBayesOneCase to predict the outcomes of all the cases (except for the first two cases in the transcript), and return the predicted outcomes as a 1-D numpy array.
5. Implement a function getPrecisionNaiveBayesWholeTranscript(NiaiveStatisticsDictionary, TestingData) is a variant of predictNaiveBayesWholeTranscript above. Instead of returning the predicted outcomes as a 1-D numpy array, this function compares the predicted outcomes with the actual outcomes in TestingData and returns the percentage of correctly predicted outcomes accomplished by the Naïve Bayes classifier.
• Use your functions for Naïve Bayes classification: In your Jupiter notebook test1.ipynb, do the following to test your implementation above.
1. Use numpy.loadtext to load the data of the transcript of Agent 1 into a 2-D numpy array TrainingData. Use your function getNaiveBayesStatistics to learn from this training data and stored the Naïve Bayes model returned as a dictionary NiaiveStatisticsDictionary. Display the contents of the dictionary.
2. Create a 1-D numpy array newCase = np.array([0,1,2,0]) to represent a case where the agent played Rock and Paper in the previous two moves while the user played Scissor and Rock in the previous two moves. Call the function getProbabilityNaiveBayesOneCase(NiaiveStatisticsDictionary, newCase) to determine predicted probabilities of seeing rock or paper or scissor. Display the probabilities. Call the function predictNaiveBayesOneCase(NiaiveStatisticsDictionary, newCase) to determine the predicted move. Display the predicted move.
3. Use numpy.loadtext to load the data of the transcript of Agent 1 into a 2-D numpy array TestingData1. Call the function predictNaiveBayesWholeTranscript(NiaiveStatisticsDictionary, TestingData1) to determine the predicted outcomes. Display the predicted outcomes. Call the function getPrecisionNaiveBayesWholeTranscript(NiaiveStatisticsDictionary, TestingData1) to determine the precision of the prediction. Display the precision.
4. Use numpy.loadtext to load the data of the transcript of Agent 2 into a 2-D numpy array TestingData2. Call the function predictNaiveBayesWholeTranscript(NiaiveStatisticsDictionary, TestingData2) to determine the predicted outcomes. Display the predicted outcomes.
5. Call the function getPrecisionNaiveBayesWholeTranscript(NiaiveStatisticsDictionary, TestingData2) to determine the precision of the prediction. Display the precision.
6. Repeat 1 to 5 above again except that in step 1 use numpy.loadtext to load the data of the transcript of Agent 2 into a 2-D numpy array TrainingData.

• Submission of your work: Fill out this self-evaluation report. Upload the self-evaluation report and your Test1.ipynb (to show your code and results) and under canvas.