def test(classifyFile, testres, lamda):
feaId = []
info = []
for line in open("D:/worktmp/people/Gap.txt"):
feaId.append(line.split()[0])
info.append(string.atof(line.split()[1]))
feasUsed = feaId[0:FEA_USED_NUM]
feasUsed.extend(['5'])
count = 0
outputFile = open(testres, 'w+')
for line in testSet:
p1_log = 0
p2_log = 0
changed = False
count += 1
print count, len(testSet)
record = line2Record(line, '1')
real = record[0]
vector = record[1]
cols = vector.keys()
for i in feasUsed:
idx = i
if idx in cols:
c1 = cnt(idx, '1', vector[idx]) / float(cntClass['1'])
c2 = cnt(idx, '2', vector[idx]) / float(cntClass['2'])
if (c1 > 0 and c1 < FEA_THRESHOLD and c2 > 0
and c2 < FEA_THRESHOLD):
continue
p1_log += math.log(
(cnt(idx, '1', vector[idx]) +
lamda)) - math.log(cntClass['1'] +
lamda * distinctNums(idx, True))
p2_log += math.log(
(cnt(idx, '2', vector[idx]) +
lamda)) - math.log(cntClass['2'] +
lamda * distinctNums(idx, True))
changed = True
p1 = 0.0
if changed == False:
continue
# p1 = cntClass['1'] / sum(cntClass.values())
else:
p1_log += math.log(
(cntClass['1'] + lamda
)) - math.log(sum(cntClass.values()) + len(cntClass) * lamda)
p2_log += math.log(
(cntClass['2'] + lamda
)) - math.log(sum(cntClass.values()) + len(cntClass) * lamda)
p1 = 1 / (math.exp(p2_log - p1_log) + 1)
outputFile.write(str(p1) + '\t' + real + '\n')
def test(classifyFile, testres, lamda):
feaId = []
info = []
for line in open("D:/worktmp/people/Gap.txt"):
feaId.append(line.split()[0])
info.append(string.atof(line.split()[1]))
feasUsed = feaId[0:FEA_USED_NUM]
feasUsed.extend(['5'])
count = 0
outputFile = open(testres, 'w+')
for line in testSet:
p1_log = 0;
p2_log = 0;
changed = False
count += 1
print count, len(testSet)
record = line2Record(line, '1')
real = record[0]
vector = record[1]
cols = vector.keys()
for i in feasUsed:
idx = i
if idx in cols:
c1 = cnt(idx, '1', vector[idx]) / float(cntClass['1'])
c2 = cnt(idx, '2', vector[idx]) / float(cntClass['2'])
if (c1 > 0 and c1 < FEA_THRESHOLD and c2 > 0 and c2 < FEA_THRESHOLD):
continue
p1_log += math.log((cnt(idx, '1', vector[idx]) + lamda)) - math.log(cntClass['1'] + lamda * distinctNums(idx, True))
p2_log += math.log((cnt(idx, '2', vector[idx]) + lamda)) - math.log(cntClass['2'] + lamda * distinctNums(idx, True))
changed = True
p1 = 0.0
if changed == False:
continue
# p1 = cntClass['1'] / sum(cntClass.values())
else:
p1_log += math.log((cntClass['1'] + lamda)) - math.log(sum(cntClass.values()) + len(cntClass) * lamda)
p2_log += math.log((cntClass['2'] + lamda)) - math.log(sum(cntClass.values()) + len(cntClass) * lamda)
p1 = 1 / (math.exp(p2_log - p1_log) + 1)
outputFile.write(str(p1) + '\t' + real + '\n')
def train(trainFile, classIdx, sparse):
count = 0
for line in open(trainFile):
count += 1
print count
if count % NUM_ALL == NUM_TEST:
testSet.append(line)
continue
record = line2Record(line, classIdx)
clas = record[0]
# if clas == '2':
# cntClass[clas] = cntClass.get(clas, 0) + 4
# else:
cntClass[clas] = cntClass.get(clas, 0) + 1
for key, value in record[1].items():
cntFeas[key] = cntFeas.get(key, {})
cntFeas[key][clas] = cntFeas[key].get(clas, {})
# if clas == '2':
# cntFeas[key][clas][value] = cntFeas[key][clas].get(value, 0) + 4
# else:
cntFeas[key][clas][value] = cntFeas[key][clas].get(value, 0) + 1
def train(trainFile, classIdx, sparse):
count = 0
for line in open(trainFile):
count += 1
print count
if count % NUM_ALL == NUM_TEST:
testSet.append(line)
continue
record = line2Record(line, classIdx)
clas = record[0]
# if clas == '2':
# cntClass[clas] = cntClass.get(clas, 0) + 4
# else:
cntClass[clas] = cntClass.get(clas, 0) + 1
for key, value in record[1].items():
cntFeas[key] = cntFeas.get(key, {})
cntFeas[key][clas] = cntFeas[key].get(clas, {})
# if clas == '2':
# cntFeas[key][clas][value] = cntFeas[key][clas].get(value, 0) + 4
# else:
cntFeas[key][clas][value] = cntFeas[key][clas].get(value, 0) + 1
def makeDataSet(trainFile, cIdx):
dataSet = []
for line in open(trainFile):
dataSet.append(line2Record(line, cIdx))
return dataSet
