--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/sandbox/simple_autoassociator/main.py	Tue Jul 08 18:46:26 2008 -0400
@@ -0,0 +1,34 @@
+#!/usr/bin/python
+"""
+    A simple autoassociator.
+
+    The learned model is::
+       h   = sigmoid(dot(x, w1) + b1)
+       y   = sigmoid(dot(h, w2) + b2)
+
+    Binary xent loss.
+
+    LIMITATIONS:
+       - Only does pure stochastic gradient (batchsize = 1).
+"""
+
+
+import numpy
+
+nonzero_instances = []
+nonzero_instances.append({0: 1, 1: 1})
+nonzero_instances.append({0: 1, 2: 1})
+
+#nonzero_instances.append({1: 0.1, 5: 0.5, 9: 1})
+#nonzero_instances.append({2: 0.3, 5: 0.5, 8: 0.8})
+##nonzero_instances.append({1: 0.2, 2: 0.3, 5: 0.5})
+
+import model
+model = model.Model()
+
+for i in xrange(100000):
+    # Select an instance
+    instance = nonzero_instances[i % len(nonzero_instances)]
+
+    # SGD update over instance
+    model.update(instance)