def test_dCOV():
# Few simple tests to verify that the measure seems to be ok
for N in xrange(1, 10): # sweep through size of the first data
# We will compare to R implementation
M, T = 4, 100
x = np.random.normal(size=(N, T)) + np.random.normal()*10
R = np.random.normal(size=(N, M))
# linearly dependent variable after rotation
dCov, dCor, _, _ = dCOV(x, 10 * np.dot(R.T, x))
ok_(dCor > 0.7) # should be really high but might fluctuate
# completely independent variable
dCov, dCor, _, _ = dCOV(x, np.random.normal(size=x.shape))
# more dimension in x -- more uncertainty that they are
# independent below is a heuristic (for T=100) and we should
# just implement proper bootstrap significance estimation for
# dCor
ok_(dCor < 0.2+N/2.0) # should be really high but might fluctuate
# the same variable -- things should match for dCov and dVar's
dCov, dCor, dVarx, dVary = dCOV(x, x)
assert_equal(dCov, dVarx)
assert_equal(dCov, dVary)
assert_equal(dCor, 1.)
assert_equal(dcorcoef(x, x), 1)
#+ np.random.normal(size=(M, T)) \
# + np.random.normal(size=(M,))[:, None] # offset
# Test that would work on vectors
dCov, dCor, dVarx, dVary = dCOV(np.arange(N), np.sin(np.arange(N)/3.))
if N>1:
ok_(dCor > 0.6) # should be really high but might fluctuate
assert_equal(dcorcoef(np.arange(N), np.sin(np.arange(N)/3.)), dCor)
def test_dCOV_against_R_energy():
skip_if_no_external('cran-energy')
for N in range(1, 10): # sweep through size of the first data
# We will compare to R implementation
M, T = 4, 30
x = np.random.normal(size=(N, T)) + np.random.normal() * 10
R = np.random.normal(size=(N, M))
y = 10 * np.dot(R.T, x) + np.random.normal(size=(M, T)) \
+ np.random.normal(size=(M,))[:, None] # offset
# To assure that works for not all_est
pdCovs = dCOV(x, y, all_est=False)
dCovs = dCOV_R(x, y, all_est=False)
assert_array_almost_equal(pdCovs, dCovs)
for uv in True, False:
for out, outp in zip(dCOV_R(x, y, uv=uv), dCOV(x, y, uv=uv)):
assert_array_almost_equal(out, outp)
def test_dCOV_against_R_energy():
skip_if_no_external('cran-energy')
for N in xrange(1, 10): # sweep through size of the first data
# We will compare to R implementation
M, T = 4, 30
x = np.random.normal(size=(N, T)) + np.random.normal()*10
R = np.random.normal(size=(N, M))
y = 10*np.dot(R.T, x) + np.random.normal(size=(M, T)) \
+ np.random.normal(size=(M,))[:, None] # offset
# To assure that works for not all_est
pdCovs = dCOV(x, y, all_est=False)
dCovs = dCOV_R(x, y, all_est=False)
assert_array_almost_equal(pdCovs, dCovs)
for uv in True, False:
for out, outp in zip(dCOV_R(x, y, uv=uv),
dCOV(x, y, uv=uv)):
assert_array_almost_equal(out, outp)
