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| author | ievred <ievgen.redko@univ-st-etienne.fr> | 2020-04-01 09:13:58 +0200 |
|---|---|---|
| committer | ievred <ievgen.redko@univ-st-etienne.fr> | 2020-04-01 09:13:58 +0200 |
| commit | 547a03ef87e4aa92edc1e89ee2db04114e1a8ad5 (patch) | |
| tree | f6795752c32fd95879324fc59ab280d3cb0b2551 /examples | |
| parent | 439860609df786a877383775dd901afe28480cc9 (diff) | |
fix test example add M to log
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/plot_otda_jcpot.py | 20 |
1 files changed, 6 insertions, 14 deletions
diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py index 5e5fff8..1641fb0 100644 --- a/examples/plot_otda_jcpot.py +++ b/examples/plot_otda_jcpot.py @@ -81,11 +81,7 @@ pl.axis('off') ############################################################################## # Instantiate Sinkhorn transport algorithm and fit them for all source domains # ---------------------------------------------------------------------------- -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-2, metric='euclidean') - -M1 = ot.dist(xs1, xt, 'euclidean') -M2 = ot.dist(xs2, xt, 'euclidean') - +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') def print_G(G, xs, ys, xt): for i in range(G.shape[0]): @@ -125,7 +121,7 @@ pl.axis('off') ############################################################################## # Instantiate JCPOT adaptation algorithm and fit it # ---------------------------------------------------------------------------- -otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, tol=1e-9, verbose=True, log=True) +otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) otda.fit(all_Xr, all_Yr, xt) ws1 = otda.proportions_.dot(otda.log_['all_domains'][0]['D2']) @@ -136,8 +132,8 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], M1, reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], M2, reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['all_domains'][0]['M'], reg=1e-2), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['all_domains'][1]['M'], reg=1e-2), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) @@ -154,10 +150,6 @@ pl.axis('off') ############################################################################## # Run oracle transport algorithm with known proportions # ---------------------------------------------------------------------------- - -otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log=True) -otda.fit(all_Xr, all_Yr, xt) - h_res = np.array([1 - pt, pt]) ws1 = h_res.dot(otda.log_['all_domains'][0]['D2']) @@ -168,8 +160,8 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], M1, reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], M2, reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['all_domains'][0]['M'], reg=1e-2), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['all_domains'][1]['M'], reg=1e-2), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) |