def ComputeCCA(X, Y):
assert X.shape[0] == Y.shape[0], (X.shape, Y.shape, "Unequal number of rows")
assert X.shape[0] > 1, (X.shape, "Must have more than 1 row")
X = NormCenterMatrix(X)
Y = NormCenterMatrix(Y)
X_q, _, _ = decomp_qr.qr(X, overwrite_a=True, mode='economic', pivoting=True)
Y_q, _, _ = decomp_qr.qr(Y, overwrite_a=True, mode='economic', pivoting=True)
C = np.dot(X_q.T, Y_q)
r = linalg.svd(C, full_matrices=False, compute_uv=False)
d = min(X.shape[1], Y.shape[1])
r = r[:d]
r = np.minimum(np.maximum(r, 0.0), 1.0) # remove roundoff errs
return r.mean()
def ComputeCCA(X, Y): assert X.shape[0] == Y.shape[0], (X.shape, Y.shape, "Unequal number of rows") assert X.shape[0] > 1, (X.shape, "Must have more than 1 row") X = NormCenterMatrix(X) Y = NormCenterMatrix(Y) X_q, _, _ = decomp_qr.qr(X, overwrite_a=True, mode='economic', pivoting=True) Y_q, _, _ = decomp_qr.qr(Y, overwrite_a=True, mode='economic', pivoting=True) C = np.dot(X_q.T, Y_q) r = linalg.svd(C, full_matrices=False, compute_uv=False) d = min(X.shape[1], Y.shape[1]) r = r[:d] r = np.minimum(np.maximum(r, 0.0), 1.0) # remove roundoff errs return r.mean()
def original(**kwargs):
embeddings = load_embeddings(**kwargs)
lg = kwargs["lg"]
features = load_features(lg)
common_phonemes = embeddings.index.intersection(features.index)
S = features.loc[common_phonemes]
X = embeddings.loc[common_phonemes]
assert X.shape[0] == S.shape[0], (X.shape, S.shape,
"Unequal number of rows")
assert X.shape[0] > 1, (X.shape, "Must have more than 1 row")
X = norm_center_matrix(X)
S = norm_center_matrix(S)
X_q, _, _ = decomp_qr.qr(X,
overwrite_a=True,
mode="economic",
pivoting=True)
S_q, _, _ = decomp_qr.qr(S,
overwrite_a=True,
mode="economic",
pivoting=True)
C = np.dot(X_q.T, S_q)
r = linalg.svd(C, full_matrices=False, compute_uv=False)
d = min(X.shape[1], S.shape[1])
r = r[:d]
r = np.minimum(np.maximum(r, 0.0), 1.0) # remove roundoff errs
r = r.mean()
# Write results to disk
level, lg, name = kwargs["level"], kwargs["lg"], kwargs["name"]
if "hidden" in kwargs:
hyperparams = f"{kwargs['size']}-{kwargs['hidden']}"
else:
hyperparams = f"{kwargs['size']}-{kwargs['window']}"
path = f"results/{level}/qvec/{lg}/{name}/{hyperparams}"
ensure_dir(path)
epoch = kwargs["epoch"]
filename = os.path.join(path, f"{epoch}.txt")
with open(filename, "w") as file:
file.write(str(r))
return r
def solve_linear(self, order_to_opt):
self.computeCost(order_to_opt)
self.computeReorderingMatrix()
self.computeR()
for i in range(self.D_):
R_pf = self.R[self.n_fixed_constrains:, 0:self.n_fixed_constrains]
R_pp = self.R[self.n_fixed_constrains:, self.n_fixed_constrains:]
df = self.df_compact[:, i]
R_pp_q, R_pp_r = QR.qr(R_pp)
RHS = np.dot(R_pf, df)
df = np.dot(np.dot(np.linalg.inv(R_pp_r), R_pp_q.T), RHS)
return True
