X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pytorch.git;a=blobdiff_plain;f=denoising-ae-field.py;h=f96c23a1243a003b7c0f8cb038e0f63062ca4b9c;hp=2aa3648991583c7681d145d727a7df5b40de5616;hb=e916a8624b6a09737696c124f35059030f0f20e4;hpb=7b9a3c65d7d1f534de76b49ddabca70c69773390

diff --git a/denoising-ae-field.py b/denoising-ae-field.py
index 2aa3648..f96c23a 100755
--- a/denoising-ae-field.py
+++ b/denoising-ae-field.py
@@ -1,5 +1,10 @@
 #!/usr/bin/env python
 
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
 import math
 import matplotlib.pyplot as plt
 
@@ -8,6 +13,19 @@ from torch import nn
 
 ######################################################################
 
+def data_rectangle(nb):
+    x = torch.rand(nb, 1) - 0.5
+    y = torch.rand(nb, 1) * 2 - 1
+    data = torch.cat((y, x), 1)
+    alpha = math.pi / 8
+    data = data @ torch.tensor(
+        [
+            [ math.cos(alpha), math.sin(alpha)],
+            [-math.sin(alpha), math.cos(alpha)]
+        ]
+    )
+    return data, 'rectangle'
+
 def data_zigzag(nb):
     a = torch.empty(nb).uniform_(0, 1).view(-1, 1)
     # zigzag
@@ -100,7 +118,7 @@ def save_image(data_name, model, data):
 
 ######################################################################
 
-for data_source in [ data_zigzag, data_spiral, data_penta ]:
+for data_source in [ data_rectangle, data_zigzag, data_spiral, data_penta ]:
     data, data_name = data_source(1000)
     data = data - data.mean(0)
     model = train_model(data)