axis_incremental fixes

sampler/axis_incremental.py

@@ -37,6 +37,7 @@
 
 condition_dim = embeddings.shape[1]
 m, n = len(args.axes), 10
+shift = 7.5
 
 
 # Prepare generator
@@ -48,26 +49,28 @@
 
 # Sample some noise data -- this needs to be shared between orig and mod
 # sample pairs
-noise_data = generator.get_noise((m * n, generator.noise_dim)).eval()
+noise_data = generator.get_noise((n, generator.noise_dim)).eval()
 
 
 # Begin modifying axes
 base_conditional_data = args.conditional_sampler(generator, n, 1,
                                                  embedding_file=args.embedding_file)
+print 'Mean for each axis:'
+pprint.pprint(zip(args.axes, base_conditional_data[:, args.axes].mean(axis=1)))
+base_conditional_data[:, args.axes] -= 0.5 * shift
+
 mod_conditional_data = base_conditional_data.copy()
 
 # Build up a flat array of modified conditional data
 mod_conditional_steps = []
 for axis in args.axes:
-    # TODO customize
-    shift = 5.
-
     mod_conditional_data[:, axis] += shift
     mod_conditional_steps.extend(mod_conditional_data.copy())
 
+mod_conditional_steps = np.array(mod_conditional_steps)
 
 samples_orig = topo_sample_f(noise_data, base_conditional_data).swapaxes(0, 3)
-samples_mod = topo_sample_f(noise_data, mod_conditional_data).swapaxes(0, 3)
+samples_mod = topo_sample_f(np.tile(noise_data, (m, 1)), mod_conditional_steps).swapaxes(0, 3)
 
 pv = PatchViewer(grid_shape=(m + 1, n), patch_shape=(32,32),
                  is_color=True)