From 1b5c35b62980038960e1c1bdd15dce4b8cdd1e7e Mon Sep 17 00:00:00 2001 From: Hexuan_Liu Date: Fri, 29 Oct 2021 09:09:47 -0700 Subject: [MRG] add normalization of distances for WDA (#172) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * edit dr.py * Correct normalization + optional parameter * pep8? * final! Co-authored-by: Rémi Flamary --- ot/dr.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) (limited to 'ot') diff --git a/ot/dr.py b/ot/dr.py index de39662..7469270 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -109,7 +109,7 @@ def fda(X, y, p=2, reg=1e-16): return Popt, proj -def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None): +def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None, normalize=False): r""" Wasserstein Discriminant Analysis [11]_ @@ -139,6 +139,8 @@ def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None): else should be a pymanopt.solvers P0 : ndarray, shape (d, p) Initial starting point for projection. + normalize : bool, optional + Normalise the Wasserstaiun distane by the average distance on P0 (default : False) verbose : int, optional Print information along iterations. @@ -164,6 +166,18 @@ def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None): # compute uniform weighs wc = [np.ones((x.shape[0]), dtype=np.float32) / x.shape[0] for x in xc] + # pre-compute reg_c,c' + if P0 is not None and normalize: + regmean = np.zeros((len(xc), len(xc))) + for i, xi in enumerate(xc): + xi = np.dot(xi, P0) + for j, xj in enumerate(xc[i:]): + xj = np.dot(xj, P0) + M = dist(xi, xj) + regmean[i, j] = np.sum(M) / (len(xi) * len(xj)) + else: + regmean = np.ones((len(xc), len(xc))) + def cost(P): # wda loss loss_b = 0 @@ -174,7 +188,7 @@ def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None): for j, xj in enumerate(xc[i:]): xj = np.dot(xj, P) M = dist(xi, xj) - G = sinkhorn(wc[i], wc[j + i], M, reg, k) + G = sinkhorn(wc[i], wc[j + i], M, reg * regmean[i, j], k) if j == 0: loss_w += np.sum(G * M) else: -- cgit v1.2.3