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)