From 197a01e99304de9156c625ae08985b4b48ffc42d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Matt=C3=A9o=20Delabre?= <spam@delab.re>
Date: Sun, 15 Dec 2019 00:03:02 -0500
Subject: [PATCH] Fix plot scaling

---
 plot.py | 46 +++++++++++++++++++++++++---------------------
 1 file changed, 25 insertions(+), 21 deletions(-)

diff --git a/plot.py b/plot.py
index 2442262..c95fed2 100644
--- a/plot.py
+++ b/plot.py
@@ -14,13 +14,36 @@ input_file = 'output/constellation-order-{}.pth'.format(order)
 # Color map used for decision regions
 color_map = matplotlib.cm.Set1
 
+# Restore model from file
+model = constellation.ConstellationNet(
+    order=order,
+    encoder_layers_sizes=(4,),
+    decoder_layers_sizes=(4,),
+    channel_model=constellation.GaussianChannel()
+)
+
+model.load_state_dict(torch.load(input_file))
+model.eval()
+
+# Extract encoding
+with torch.no_grad():
+    encoded_vectors = model.encoder(
+        util.messages_to_onehot(
+            torch.arange(0, order),
+            order
+        )
+    )
+
 # Setup plot
 fig = pyplot.figure()
 ax = SubplotZero(fig, 111)
 fig.add_subplot(ax)
 
 # Extend axes symmetrically around zero so that they fit data
-extent = 1.5
+extent = max(
+    abs(encoded_vectors.min()),
+    abs(encoded_vectors.max())
+) * 1.05
 ax.set_xlim(-extent, extent)
 ax.set_ylim(-extent, extent)
 
@@ -68,21 +91,10 @@ ax.annotate(
 
 ax.grid()
 
-# Restore model from file
-model = constellation.ConstellationNet(
-    order=order,
-    encoder_layers_sizes=(4,),
-    decoder_layers_sizes=(4,),
-    channel_model=constellation.GaussianChannel()
-)
-
-model.load_state_dict(torch.load(input_file))
-model.eval()
-
 # Plot decision regions
 color_norm = matplotlib.colors.BoundaryNorm(range(order + 1), order)
 
-step = 0.01
+step = 0.001 * extent
 grid_range = torch.arange(-extent, extent, step)
 grid_y, grid_x = torch.meshgrid(grid_range, grid_range)
 grid_images = model.decoder(torch.stack((grid_x, grid_y), dim=2)).argmax(dim=2)
@@ -97,14 +109,6 @@ ax.imshow(
 )
 
 # Plot encoded vectors
-with torch.no_grad():
-    encoded_vectors = model.encoder(
-        util.messages_to_onehot(
-            torch.arange(0, order),
-            order
-        )
-    )
-
 ax.scatter(
     *zip(*encoded_vectors.tolist()),
     zorder=10,