a = AdaBoost(train = False)
for i in range(0, len(tmp), 4):
alpha, demention, label, threshold = None, None, None, None
for j in range(i, i + 4):
if (j % 4) == 0:
alpha = float(tmp[j])
elif (j % 4) == 1:
demention = int(tmp[j])
elif (j % 4) == 2:
label = float(tmp[j])
elif (j % 4) == 3:
threshold = float(tmp[j])
classifier = a.Weaker(train = False)
classifier.constructor(demention, label, threshold)
a.G[i/4] = classifier
a.alpha[i/4] = alpha
a.N += 1
print "Construction finished"
fileObj.close()
output = a.prediction(Original_Data)
print numpy.count_nonzero(output[0:TESTING_POSITIVE_SAMPLE] > 0) * 1./ TESTING_POSITIVE_SAMPLE
print numpy.count_nonzero(output[TESTING_POSITIVE_SAMPLE:TESTING_SAMPLE_NUM] < 0) * 1./ TESTING_NEGATIVE_SAMPLE
for i in range(0, len(tmp), 4):
alpha, demention, label, threshold = None, None, None, None
for j in range(i, i + 4):
if (j % 4) == 0:
alpha = float(tmp[j])
elif (j % 4) == 1:
demention = int(tmp[j])
elif (j % 4) == 2:
label = float(tmp[j])
elif (j % 4) == 3:
threshold = float(tmp[j])
classifier = a.Weaker(train=False)
classifier.constructor(demention, label, threshold)
a.G[i / 4] = classifier
a.alpha[i / 4] = alpha
a.N += 1
print "Construction finished"
fileObj.close()
output = a.prediction(Original_Data)
print numpy.count_nonzero(
output[0:TESTING_POSITIVE_SAMPLE] > 0) * 1. / TESTING_POSITIVE_SAMPLE
print numpy.count_nonzero(output[TESTING_POSITIVE_SAMPLE:TESTING_SAMPLE_NUM] <
0) * 1. / TESTING_NEGATIVE_SAMPLE
for i in range(len(Tag)):
if Tag[i] == 1:
pyplot.plot(Original_Data[0][i], Original_Data[1][i], \
'+r', markersize = 10)
else:
pyplot.plot(Original_Data[0][i], Original_Data[1][i], \
'+b', markersize = 10)
a = AdaBoost(Original_Data, Tag)
a.train(100)
TestCase = [[0.55, 1.1, 5.35, 7.0, 8.5, -1.0, 3.0, 3.0, 4.0, 2, 3],
[4.4, 2.8, 0.9, -12, -13, -9, -10, -9, -5, 0, 2.5]]
output = a.prediction(TestCase)
for i in range(len(output)):
if output[i] == 1:
pyplot.plot(TestCase[0][i], TestCase[1][i], \
'or', markersize = 20)
else:
pyplot.plot(TestCase[0][i], TestCase[1][i], \
'ob', markersize = 20)
pyplot.show()
"""
Demo Two
"""
Original_Data = numpy.array([[0], [1], [2], [3], [4], [5], [6], [7], [8],
[9]]).transpose()
[+1],
[+1],
[+1],
]
).transpose()
Tag = Tag.flatten()
for i in range(len(Tag)):
if Tag[i] == 1:
pyplot.plot(Original_Data[0][i], Original_Data[1][i], "+r", markersize=10)
else:
pyplot.plot(Original_Data[0][i], Original_Data[1][i], "+b", markersize=10)
a = AdaBoost(Original_Data, Tag)
a.train(100)
TestCase = [[0.55, 1.1, 5.35], [4.4, 2.8, 0.9]]
output = a.prediction(TestCase)
for i in range(len(output)):
if output[i] == 1:
pyplot.plot(TestCase[0][i], TestCase[1][i], "or", markersize=20)
else:
pyplot.plot(TestCase[0][i], TestCase[1][i], "ob", markersize=20)
pyplot.show()
