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from numpy.testing import *
import numpy as N
set_local_path('../..')
from svm.classification import *
from svm.dataset import *
from svm.kernel import *
from svm.predict import *
restore_path()
class test_classification_speed(NumpyTestCase):
def check_large_test_dataset(self):
x = N.random.randn(150, 3)
# XXX shows bug where we can't get any support vectors
#x = N.random.randn(4, 2)
#x = N.random.randn(10, 3)
labels = N.random.random_integers(1, 5, x.shape[0])
#labels = N.random.random_integers(1, 2, x.shape[0])
traindata = LibSvmClassificationDataSet(labels, x)
#kernel = RBFKernel(traindata.gamma)
kernel = LinearKernel()
#kernel = PolynomialKernel(2, 5, 10)
model = LibSvmCClassificationModel(kernel)
#xdim, ydim, zdim = 1, 1, x.shape[1]
xdim, ydim, zdim = 2, 2, x.shape[1]
img = N.random.randn(xdim, ydim, zdim)
testdata1 = LibSvmTestDataSet(img.reshape(xdim*ydim, zdim))
testdata2 = LibSvmTestDataSet(list(img.reshape(xdim*ydim, zdim)))
refresults = model.fit(traindata)
refv1 = refresults.predict_values(testdata1)
refv2 = refresults.predict_values(testdata2)
results = model.fit(traindata, LibSvmPythonPredictor)
#v11 = results.predict_values(testdata1)
#v12 = results.predict_values(testdata2)
results.compact()
v21 = results.predict_values(testdata1)
#v22 = results.predict_values(testdata2)
print refv1
print refv2
#print v11
#print v12
print v21
#print v22
if __name__ == '__main__':
NumpyTest().run()
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