def test_struct(self):
sdata = np.array([(0.5, -1.3, 1, 100.11, 1111111),
(2.5, -3.3, 2, 200.22, 2222222),
(4.5, -5.3, 2, 350.33, 3333333),
(6.5, -7.3, 0, 470.44, 4444444),
(8.5, -9.3, 1, 270.55, 55555)],
dtype=[('x','f4'), ('y','f4'), ('categ','i4'), ('value','f8'), ('super','i8')])
hdata = np.array([(0.5, -1.3, 1, 100.11, 1111111),
(2.5, -3.3, 2, 200.22, 2222222),
(4.5, -5.3, 2, 350.33, 3333333),
(6.5, -7.3, 0, 470.44, 4444444),
(8.5, -9.3, 1, 270.55, 55555)],
dtype = np.float64)
sr = d4p.cosine_distance().compute(sdata)
hr = d4p.cosine_distance().compute(hdata)
self.assertTrue(np.allclose(hr.cosineDistance, sr.cosineDistance))
def main(readcsv=read_csv, method='defaultDense'):
data = readcsv(os.path.join('data', 'batch', 'distance.csv'), range(10))
# Create algorithm to compute cosine distance (no parameters)
algorithm = d4p.cosine_distance()
# Computed cosine distance with file or numpy array
res1 = algorithm.compute(os.path.join('data', 'batch', 'distance.csv'))
res2 = algorithm.compute(data)
assert np.allclose(res1.cosineDistance, res2.cosineDistance)
return res1
def _daal4py_cosine_distance_dense(X):
X_fptype = getFPType(X)
alg = daal4py.cosine_distance(fptype=X_fptype, method='defaultDense')
res = alg.compute(X)
return res.cosineDistance
def cosine(X):
cos_dist = cosine_distance().compute(X)
