def test_XXYY_meanfree(self):
# many passes
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
compute_YY=True,
remove_mean=True)
for i in range(0, self.T, self.L):
cc.add(self.X[i:i + self.L], self.Y[i:i + self.L])
np.testing.assert_allclose(cc.weight_XY(), self.T)
np.testing.assert_allclose(cc.sum_X(), self.sx)
np.testing.assert_allclose(cc.moments_XX(), self.Mxx0)
np.testing.assert_allclose(cc.moments_XY(), self.Mxy0)
np.testing.assert_allclose(cc.moments_YY(), self.Myy0)
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
compute_YY=True,
remove_mean=True)
for i in range(0, self.T, self.L):
cc.add(self.X[i:i + self.L],
self.Y[i:i + self.L],
column_selection=self.cols_2)
np.testing.assert_allclose(cc.moments_XX(), self.Mxx0[:, self.cols_2])
np.testing.assert_allclose(cc.moments_XY(), self.Mxy0[:, self.cols_2])
np.testing.assert_allclose(cc.moments_YY(), self.Myy0[:, self.cols_2])
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
compute_YY=True,
remove_mean=True,
diag_only=True)
for i in range(0, self.T, self.L):
cc.add(self.X[i:i + self.L], self.Y[i:i + self.L])
np.testing.assert_allclose(cc.moments_XX(), np.diag(self.Mxx0))
np.testing.assert_allclose(cc.moments_XY(), np.diag(self.Mxy0))
np.testing.assert_allclose(cc.moments_YY(), np.diag(self.Myy0))
def test_XXXY_sym_withmean(self):
# many passes
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
remove_mean=False,
symmetrize=True)
for i in range(0, self.T, self.L):
cc.add(self.X[i:i + self.L], self.Y[i:i + self.L])
np.testing.assert_allclose(cc.weight_XY(), 2 * self.T)
np.testing.assert_allclose(cc.sum_X(), self.s_sym)
np.testing.assert_allclose(cc.moments_XX(), self.Mxx_sym)
np.testing.assert_allclose(cc.moments_XY(), self.Mxy_sym)
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
remove_mean=False,
symmetrize=True)
for i in range(0, self.T, self.L):
cc.add(self.X[i:i + self.L],
self.Y[i:i + self.L],
column_selection=self.cols_2)
np.testing.assert_allclose(cc.moments_XX(), self.Mxx_sym[:,
self.cols_2])
np.testing.assert_allclose(cc.moments_XY(), self.Mxy_sym[:,
self.cols_2])
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
remove_mean=False,
symmetrize=True,
diag_only=True)
for i in range(0, self.T, self.L):
cc.add(self.X[i:i + self.L], self.Y[i:i + self.L])
np.testing.assert_allclose(cc.moments_XX(), np.diag(self.Mxx_sym))
np.testing.assert_allclose(cc.moments_XY(), np.diag(self.Mxy_sym))
def test_XXXY_weighted_withmean(self):
# many passes
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
remove_mean=False)
for i in range(0, self.T, self.L):
iX = self.X[i:i + self.L, :]
iY = self.Y[i:i + self.L, :]
iwe = self.weights[i:i + self.L]
cc.add(iX, iY, weights=iwe)
np.testing.assert_allclose(cc.weight_XY(), self.wesum)
np.testing.assert_allclose(cc.sum_X(), self.sx_w)
np.testing.assert_allclose(cc.moments_XX(), self.Mxx_w)
np.testing.assert_allclose(cc.moments_XY(), self.Mxy_w)
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
remove_mean=False)
for i in range(0, self.T, self.L):
iX = self.X[i:i + self.L, :]
iY = self.Y[i:i + self.L, :]
iwe = self.weights[i:i + self.L]
cc.add(iX, iY, weights=iwe, column_selection=self.cols_2)
np.testing.assert_allclose(cc.moments_XX(), self.Mxx_w[:, self.cols_2])
np.testing.assert_allclose(cc.moments_XY(), self.Mxy_w[:, self.cols_2])
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
remove_mean=False,
diag_only=True)
for i in range(0, self.T, self.L):
iX = self.X[i:i + self.L, :]
iY = self.Y[i:i + self.L, :]
iwe = self.weights[i:i + self.L]
cc.add(iX, iY, weights=iwe)
np.testing.assert_allclose(cc.moments_XX(), np.diag(self.Mxx_w))
np.testing.assert_allclose(cc.moments_XY(), np.diag(self.Mxy_w))
def test_XX_withmean(self):
# many passes
cc = running_moments.RunningCovar(remove_mean=False)
for i in range(0, self.T, self.L):
cc.add(self.X[i:i + self.L])
np.testing.assert_allclose(cc.weight_XX(), self.T)
np.testing.assert_allclose(cc.sum_X(), self.sx)
np.testing.assert_allclose(cc.moments_XX(), self.Mxx)
cc = running_moments.RunningCovar(remove_mean=False)
for i in range(0, self.T, self.L):
cc.add(self.X[i:i + self.L], column_selection=self.cols_2)
np.testing.assert_allclose(cc.moments_XX(), self.Mxx[:, self.cols_2])
cc = running_moments.RunningCovar(remove_mean=False, diag_only=True)
for i in range(0, self.T, self.L):
cc.add(self.X[i:i + self.L])
np.testing.assert_allclose(cc.moments_XX(), np.diag(self.Mxx))
def test_XXXY_weighted_sym_meanfree(self):
# many passes
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
remove_mean=True,
symmetrize=True)
for i in range(0, self.T, self.L):
iwe = self.weights[i:i + self.L]
cc.add(self.X[i:i + self.L], self.Y[i:i + self.L], weights=iwe)
np.testing.assert_allclose(cc.weight_XY(), 2 * self.wesum)
np.testing.assert_allclose(cc.sum_X(), self.s_sym_w)
np.testing.assert_allclose(cc.moments_XX(), self.Mxx0_sym_w)
np.testing.assert_allclose(cc.moments_XY(), self.Mxy0_sym_w)
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
remove_mean=True,
symmetrize=True)
for i in range(0, self.T, self.L):
iwe = self.weights[i:i + self.L]
cc.add(self.X[i:i + self.L],
self.Y[i:i + self.L],
weights=iwe,
column_selection=self.cols_2)
np.testing.assert_allclose(cc.moments_XX(),
self.Mxx0_sym_w[:, self.cols_2])
np.testing.assert_allclose(cc.moments_XY(),
self.Mxy0_sym_w[:, self.cols_2])
cc = running_moments.RunningCovar(compute_XX=True,
compute_XY=True,
remove_mean=True,
symmetrize=True,
diag_only=True)
for i in range(0, self.T, self.L):
iwe = self.weights[i:i + self.L]
cc.add(self.X[i:i + self.L], self.Y[i:i + self.L], weights=iwe)
np.testing.assert_allclose(cc.moments_XX(), np.diag(self.Mxx0_sym_w))
np.testing.assert_allclose(cc.moments_XY(), np.diag(self.Mxy0_sym_w))