def predictSVM():
## loading the Votes
log = "Step 1: loading votes..."
writeLog(log)
print log
d = shelve.open('./models/Votes_Score_noscale')
Votes = d['vote']
d.close()
## loading the testing data
log = "Step 2: loading testing data..."
writeLog(log)
print log
test_x, test_y = loadDigitTestData()
## predict the testing data
log = "Step 3: predicting the data..."
writeLog(log)
print log
predict_y = Votes.argmax(axis=1)
m, n = shape(test_y)
matchCount = 0
for i in range(m):
if predict_y[i] == test_y[i]:
matchCount += 1
else:
# print i
# print str(test_y[i]) + '\t' + str(predict_y[i]) + '\t' + str(Votes[i])
# raw_input()
writeLog(str(test_y[i]) + '\t' + str(predict_y[i]) + '\t' + str(Votes[i]))
accuracy = float(matchCount) / m
log = "step 4: show the result..."
writeLog(log)
print log
log = 'The classify accuracy is: %.3f%%' % (accuracy * 100)
writeLog(log)
print log
def testSVM():
## loading data
log = "Step 1: loading data..."
writeLog(log)
print log
test_x, test_y = loadDigitTestData()
# scales from -1 to 1
test_x = test_x/255.0*2 - 1
# initialize the vote matrix for testing data, Votes[m, 10]
m, dump = shape(test_y)
Votes = mat(zeros((m, 10)))
## testing data
log = "Step 2: testing data..."
for i in range(10):
for j in range(i+1, 10):
log = "--working on model: " + str(i) + '&' + str(j)
print log
writeLog(log)
# loading the models
d = shelve.open('./models/svm_' + str(i) + '_' + str(j))
svmClassifier = d['svm']
d.close()
# testing using the given model and votes
Votes_k, Votes_l = SVM.testDigitScores(svmClassifier, test_x, m)
# write to the Votes
Votes[:, i] += Votes_k
Votes[:, j] += Votes_l
## saving Votes matrix
log = "Step 3: saving votes..."
print log
writeLog(log)
d = shelve.open('./models/Votes_Score_noscale')
d['vote'] = Votes
d.close()
