def findData(self):
text_file = open("NewPredictedData.mat", "r")
totalCoeff = []
ranking = []
count = 0
m = [line.rstrip('\n').split(",") for line in text_file]
for coeff in m:
total = float(coeff[1]) + float(coeff[2]) + \
float(coeff[3]) + 3 * float(coeff[4])
totalCoeff.append(total)
amount = map(lambda x: x[0], m)
twoYearCoeff = map(lambda x: x[1], m)
threeYearCoeff = map(lambda x: x[2], m)
fourYearCoeff = map(lambda x: x[3], m)
firstGenCoeff = map(lambda x: x[4], m)
text_file.close()
for i, score in enumerate(firstGenCoeff):
ranking.append((fd.findRow(str(i)), float(score), amount[i]))
ranking = sorted(ranking, key=lambda school: school[1])
for i in reversed(ranking):
if count <= 100:
print i
count = count + 1
def rankWinners(self, judgement, decFunc = (lambda x: x)):
text_file = open("NewDataPredict3.mat", "r")
computed_mat = [line.rstrip('\n').split(",") for line in text_file]
text_file.close()
translated_mat = []
for index, r in enumerate(computed_mat):
if index < 76:
if index % 2 != 0:
translated_mat.append((fd.findRow(str(index)),
map(decFunc, r)))
else:
translated_mat.append((fd.findRow(str(index)),r))
ranking = sorted(translated_mat, key=lambda s: judgement(s[1]))
ranking = map(lambda x: (x[0], judgement(x[1])), ranking)
return ranking[::-1]
