def loadnumerical():
oldrows = advancedclassify.loadmatch('matchmaker.csv')
newrows = []
out = open('numerical.txt', 'w+')
for row in oldrows:
d = row.data
data = [float(d[0]), advancedclassify.yesno(d[1]), advancedclassify.yesno(d[2]), advancedclassify.yesno(d[5]),
advancedclassify.yesno(d[6]), advancedclassify.yesno(d[7]), advancedclassify.matchcount(d[3], d[8]),
milesdistance(d[4], d[9]), row.match]
newrows.append(advancedclassify.matchrow(data))
print(advancedclassify.matchrow(data).__str__())
out.write("%s\n" % data)
out.close()
return newrows
def loadnumerical():
oldrows = advancedclassify.loadmatch('matchmaker.csv')
newrows = []
out = open('numerical.txt', 'w+')
for row in oldrows:
d = row.data
data = [
float(d[0]),
advancedclassify.yesno(d[1]),
advancedclassify.yesno(d[2]),
advancedclassify.yesno(d[5]),
advancedclassify.yesno(d[6]),
advancedclassify.yesno(d[7]),
advancedclassify.matchcount(d[3], d[8]),
milesdistance(d[4], d[9]), row.match
]
newrows.append(advancedclassify.matchrow(data))
print(advancedclassify.matchrow(data).__str__())
out.write("%s\n" % data)
out.close()
return newrows
# agesonly = advancedclassify.loadmatch('agesonly.csv', allnum=True)
# matchmaker = advancedclassify.loadmatch('matchmaker.csv')
# advancedclassify.plotagematches(agesonly)
# avgs = advancedclassify.lineartrain(agesonly)
# print advancedclassify.dpclassify([30, 25], avgs)
# print advancedclassify.dpclassify([25, 40], avgs)
# print advancedclassify.dpclassify([48, 20], avgs)
# numbericalset = bingmapsapi.loadnumerical()
# json.dump(json.JSONEncoder(numbericalset), out)
# out.close()
numbericalset = []
with open("numerical.txt") as file:
for row in file:
row = json.loads(row.replace("\n", ""))
numbericalset.append(advancedclassify.matchrow(row))
print len(numbericalset)
scaledset, scalef = advancedclassify.scaledata(numbericalset)
# avgs = advancedclassify.lineartrain(scaledset)
# print numbericalset[0].match
# print advancedclassify.dpclassify(scalef(numbericalset[0].data), avgs)
# print numbericalset[11].match
# print advancedclassify.dpclassify(scalef(numbericalset[11].data), avgs)
answers, inputs = [r.match for r in scaledset], [r.data for r in scaledset]
prob = svm_problem(answers, inputs)
# m = svm_train(prob, '-t 3')
# svm_save_model('test.model', m)
m = svm_load_model('test.model')
# newrow = [28.0, -1, -1, 26.0, -1, 1, 2, 0.8]
# predicted_labels0 = svm_predict(scalef(newrow), m)
