initial commit
meaning 5, signals 10
obverter_ori changed to only looping through learning rules
loop through learning rules only; calculate the means of each learning rules, sorted.
obverter_ ori fixed average of communication accuracy rounded to 3 decimal points.
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added burnin numbers to disregard half of the initial results when measuring communication accuracy average
obverter using weight proportion. rough initial attemps. works but needs tuning for good output.
initial attemps for objected oriented version. this uses weight proportion instead.
reorgnisation of files
check communication accuracy of all rules. check good rules and ok rules.
check communication accuracy of only learning rules. check good rules and learning rules
initial attemp of object oriented version: using weight proportion learning procedure to calculate obverter. should change.
initial code to calculate obverter using weight proportion. lack of theoretical backing, or not?
fixed greater or equal to 0.98
changed the standard for good/ok rules into calculating the average of last 10 calculations (100 interactions)
changed burin
check the non_learners instead of all rules
commented out scipy
 I don't remember what.
added results file. timecourse.py put print globals in a function. put every run of result into a list. can be accessed by indexing.