Esempio n. 1
0
def test() : 
	import LoadData
	trainMat, classLabelVector = LoadData.loadTrainDataFromCSV(TRAIN_FILE)
	testMat = LoadData.loadTestDataFromCSV(TEST_FILE)
	rfbenchmarkVector  = LoadData.loadRFBenchmarkFromCSV(RF_BENCHMARK_FILE)

	columnLabels = []
	for i in range(1, 785) : 
		columnLabels.append(i)
	m = int(len(columnLabels) ** 0.5)
	# rf = createRandomForest(4, trainMat[50:], classLabelVector[50:], columnLabels, m)
	rf = createRandomForest(10, trainMat, classLabelVector, columnLabels, m)
	# testMat = trainMat[0:50]

	i = 0
	n = 0
	for testData in testMat : 
		classList = []
		for tree in rf : 
			label = classify(tree, columnLabels, testData)
			classList.append(label)
		voteLabel = majorityCnt(classList)
		if voteLabel == rfbenchmarkVector[i] : 
			n += 1
		# print "the real answer is ", classLabelVector[i], "the label is ", voteLabel
		i += 1
	print n
	accuracy = n / float(len(rfbenchmarkVector))
	print accuracy
Esempio n. 2
0
def test() : 
	import LoadData
	trainMat, classLabelVector = LoadData.loadTrainDataFromCSV(TRAIN_FILE)
	trainMat = array(trainMat)
	testMat = LoadData.loadTestDataFromCSV(TEST_FILE)

	k = 3

	# testMat = trainMat[0:50]
	# i = 0
	# for testData in testMat : 
	# 	label = classify_kNN(testData, trainMat[50:], classLabelVector[50:], k)
	# 	print "the real answer is ", classLabelVector[i], "the label is ", label
	# 	i += 1

	for testData in testMat : 
		label = classify_kNN(testData, trainMat, classLabelVector, k)