def modelOutput(trainFile, testFile, modelType):
"""
output is:
(naive bayes) variable name | 'class'
(tan) variable name | name of its parents
# empty
followed by:
predict class | actual class | posterior probability (12 digits after decimal point)
# empty
followed by:
The number of the test-set examples that were correctly classified.
"""
attributes, labels, instances = data_provider(trainFile)
if modelType == 'n':
model = Bayes(attributes, labels, instances)
elif modelType == 't':
model = TAN(attributes, labels, instances)
else:
import sys
print >> sys.stderr, 'model type should be [n] or [t] !!!'
sys.exit()
attributes, labels, instances = data_provider(testFile)
# format output part1: attribute name | 'class'
model.printTree()
print
correctClassCnt = 0
for test in instances:
result = model.classify(test)
if result[0] == result[1]:
correctClassCnt += 1
# format output part2: predict class | actual class | posterior probability
print formatOutput(result)
print
# format output part3: correctly classified number of test instances
print correctClassCnt
