--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/sparse_random_autoassociator/model.py	Mon Jul 07 01:54:46 2008 -0400
@@ -0,0 +1,37 @@
+"""
+The model for an autoassociator for sparse inputs, using Ronan Collobert + Jason
+Weston's sampling trick (2008).
+"""
+
+from graph import trainfn
+import parameters
+import numpy
+from globals import LR
+
+class Model:
+    def __init__(self):
+        self.parameters = parameters.Parameters(randomly_initialize=True)
+
+    def update(self, instance, nonzero_indexes, zero_indexes):
+        xnonzero = numpy.asarray([instance[idx] for idx in nonzero_indexes])
+        print
+        print "xnonzero:", xnonzero
+
+        (ynonzero, yzero, loss, gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero) = trainfn(xnonzero, self.parameters.w1[nonzero_indexes, :], self.parameters.b1, self.parameters.w2[:, nonzero_indexes], self.parameters.w2[:, zero_indexes], self.parameters.b2[nonzero_indexes], self.parameters.b2[zero_indexes])
+        print "OLD ynonzero:", ynonzero
+        print "OLD yzero:", yzero
+        print "OLD total loss:", loss
+
+        # SGD update
+        self.parameters.w1[nonzero_indexes, :]  -= LR * gw1nonzero
+        self.parameters.b1						-= LR * gb1
+        self.parameters.w2[:, nonzero_indexes]  -= LR * gw2nonzero
+        self.parameters.w2[:, zero_indexes]		-= LR * gw2zero
+        self.parameters.b2[nonzero_indexes]		-= LR * gb2nonzero
+        self.parameters.b2[zero_indexes]		-= LR * gb2zero
+
+        # Recompute the loss, to make sure it's descreasing
+        (ynonzero, yzero, loss, gw1nonzero, gb1, gw2nonzero, gw2zero, gb2nonzero, gb2zero) = trainfn(xnonzero, self.parameters.w1[nonzero_indexes, :], self.parameters.b1, self.parameters.w2[:, nonzero_indexes], self.parameters.w2[:, zero_indexes], self.parameters.b2[nonzero_indexes], self.parameters.b2[zero_indexes])
+        print "NEW ynonzero:", ynonzero
+        print "NEW yzero:", yzero
+        print "NEW total loss:", loss