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import numpy as np
import pytest
import torch
from ding.torch_utils.math_helper import cov
@pytest.mark.unittest
class TestMathHelper:
def test_cov(self):
r'''
Overview:
Test the conv
'''
# test 1D
# test dtype and rowvar
x1 = np.array([1, 2, 3])
cov1 = np.cov(x1, rowvar=False)
x1_tensor = torch.FloatTensor(x1)
cov1_tensor = cov(x1_tensor, rowvar=False).detach().numpy()
assert (np.abs(cov1 - cov1_tensor) < 1e-6).any()
# test 2D
x2 = np.array([[0., 2.], [1., 1.], [2., 0.]]).T
cov2 = np.cov(x2, rowvar=True)
x2_tensor = torch.FloatTensor(x2)
cov2_tensor = cov(x2_tensor, rowvar=True).detach().numpy()
assert (np.abs(cov2 - cov2_tensor) < 1e-6).any()
# test bias
cov3 = np.cov(x2, rowvar=True, bias=True)
cov3_tensor = cov(x2_tensor, rowvar=True, bias=True).detach().numpy()
assert (np.abs(cov3 - cov3_tensor) < 1e-6).any()
# test ddof
aweights = np.array([1., 2., 3.])
cov4 = np.cov(x2, rowvar=True, ddof=0, aweights=aweights)
cov4_tensor = cov(x2_tensor, rowvar=True, ddof=0, aweights=aweights).detach().numpy()
assert (np.abs(cov4 - cov4_tensor) < 1e-6).any()
# test aweights
cov5 = np.cov(x2, rowvar=True, aweights=aweights)
aweights_tensor = torch.FloatTensor(aweights)
cov5_tensor = cov(x2_tensor, rowvar=True, aweights=aweights_tensor).detach().numpy()
assert (np.abs(cov5 - cov5_tensor) < 1e-6).any()
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