def test_pca_preconditioner(self): X = dot(randn(5, 5), randn(5, 1000)) + randn(5, 1) Y = dot(randn(2, 2), randn(2, 1000)) + dot(randn(2, 5), X) wt = PCAPreconditioner(X, Y, num_pcs=X.shape[0]) # joint covariance C = cov(vstack(wt(X, Y)), bias=True) self.assertLess(max(abs(C - eye(7))), 1e-8) # test inverse Xw, Yw = wt(X, Y) Xr, Yr = wt.inverse(Xw, Yw) self.assertLess(max(abs(Xr - X)), 1e-10) self.assertLess(max(abs(Yr - Y)), 1e-10) pca = PCAPreconditioner(X, Y, num_pcs=3) # test inverse Xp, Yp = pca(X, Y) Xr, Yr = pca.inverse(Xp, Yp) self.assertLess(max(abs(Yr - Y)), 1e-10)
def test_pca_preconditioner_logjacobian(self):
eigenvalues = rand(5) + .5
meanIn = randn(5, 1)
meanOut = randn(2, 1)
whiteIn = randn(5, 5)
whiteIn = dot(whiteIn, whiteIn.T)
whiteOut = randn(2, 2)
whiteOut = dot(whiteOut, whiteOut.T)
predictor = randn(2, 5)
wt = PCAPreconditioner(eigenvalues, meanIn, meanOut, whiteIn,
inv(whiteIn), whiteOut, inv(whiteOut),
predictor)
self.assertAlmostEqual(
mean(wt.logjacobian(randn(5, 10), randn(2, 10))),
slogdet(whiteOut)[1])
def test_pca_preconditioner_logjacobian(self): eigenvalues = rand(5) + .5 meanIn = randn(5, 1) meanOut = randn(2, 1) whiteIn = randn(5, 5) whiteIn = dot(whiteIn, whiteIn.T) whiteOut = randn(2, 2) whiteOut = dot(whiteOut, whiteOut.T) predictor = randn(2, 5) wt = PCAPreconditioner( eigenvalues, meanIn, meanOut, whiteIn, inv(whiteIn), whiteOut, inv(whiteOut), predictor) self.assertAlmostEqual(mean(wt.logjacobian(randn(5, 10), randn(2, 10))), slogdet(whiteOut)[1])
def test_pca_preconditioner(self):
X = dot(randn(5, 5), randn(5, 1000)) + randn(5, 1)
Y = dot(randn(2, 2), randn(2, 1000)) + dot(randn(2, 5), X)
wt = PCAPreconditioner(X, Y, num_pcs=X.shape[0])
# joint covariance
C = cov(vstack(wt(X, Y)), bias=True)
self.assertLess(max(abs(C - eye(7))), 1e-8)
# test inverse
Xw, Yw = wt(X, Y)
Xr, Yr = wt.inverse(Xw, Yw)
self.assertLess(max(abs(Xr - X)), 1e-10)
self.assertLess(max(abs(Yr - Y)), 1e-10)
pca = PCAPreconditioner(X, Y, num_pcs=3)
# test inverse
Xp, Yp = pca(X, Y)
Xr, Yr = pca.inverse(Xp, Yp)
self.assertLess(max(abs(Yr - Y)), 1e-10)
def test_pca_preconditioner_pickle(self):
wt0 = PCAPreconditioner(randn(5, 1000), randn(2, 1000), num_pcs=3)
tmp_file = mkstemp()[1]
# store transformation
with open(tmp_file, 'w') as handle:
dump({'wt': wt0}, handle)
# load transformation
with open(tmp_file) as handle:
wt1 = load(handle)['wt']
X, Y = randn(5, 100), randn(2, 100)
X0, Y0 = wt0(X, Y)
X1, Y1 = wt1(X, Y)
# make sure linear transformation hasn't changed
self.assertLess(max(abs(X0 - X1)), 1e-20)
self.assertLess(max(abs(Y0 - Y1)), 1e-20)