previousScoreImprovement = arrangement.makeBestSwapFromUnhappiestGroup()
print arrangement
print previousScoreImprovement
arrangement.score = arrangement.calculateScore()
return arrangement
#--------------- Run ----------------------------------#
# Convert CSV
jsonArrangement = csv2json(INPUT_CSV_FILENAME)
# uncomment the following two line to run with sample data and comment the line above
# with open('grouper/sample_data/class40participants.json') as csvFile:
# jsonArrangement = json.dumps(json.load(csvFile))
print jsonArrangement;
arrangements = [Arrangement(jsonString = jsonArrangement) for x in range(NUM_ITERATIONS)]
for arrangement in arrangements:
arrangement.randomizeGroups()
results = []
for arrangement in arrangements:
results.append(swapUnhappiest(arrangement))
results = sorted(results, key = lambda x: x.score)
print results[-3:]
# # Save to file
for i in range(1, 5):
arrangement2csv(results[-i], OUTPUT_FOLDER + OUTPUT_FILENAME + '_' + str(i) + '.csv')
# ------------- Genetic Algorithm. Random Swaps -------------#
# for x in range(0, 10):
# for arrangement in arrangements:
# arrangement.randomizeGroups()
# for i in range(0, 40):
# tempArrangements = []
# for arrangement in arrangements:
# for i in range(5):
# arrangement.swapRandomIndividuals()
# arrangement.score = arrangement.calculateScore()
# tempArrangements.append(copy.deepcopy(arrangement))
# result = sorted(tempArrangements, key=lambda x: x.score)
# arrangements = result[-10:]
# print arrangements
#-------------- Make best swap from unhappiest group -----------#
for arrangement in arrangements:
arrangement.randomizeGroups()
for i in range(0, 10):
arrangement.makeBestSwapFromUnhappiestGroup()
print arrangement
arrangement.score = arrangement.calculateScore()
result = sorted(arrangements, key=lambda x: x.score)
print result
for i in range(1, 4):
arrangement2csv(result[-i], 'arrangement_' + str(i) + '.csv')
