# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1242334810 14400
# Node ID e69249897f893bfa0d0be2754e0caa614aa8194a
# Parent  7d8bb6d087bcda1cd34387e6eb367abaafe6af04
added DynamicNormalizer to exponential_mean

diff -r 7d8bb6d087bc -r e69249897f89 pylearn/algorithms/exponential_mean.py
--- a/pylearn/algorithms/exponential_mean.py	Thu May 14 16:59:20 2009 -0400
+++ b/pylearn/algorithms/exponential_mean.py	Thu May 14 17:00:10 2009 -0400
@@ -141,3 +141,25 @@
         obj.mean.initialize()
         obj.mean_sqr.initialize()
 
+class DynamicNormalizer(theano.Module):
+    """
+    Normalizes `input` using geometric-decaying estimates of the mean and variance.  The
+    `output` should mean near zero, and variance near 1.
+    """
+    def __init__(self, input, input_shape, max_denom=100, eps=1.0e-8):
+        super(DynamicNormalizer, self).__init__()
+        self.input = input
+        self.d_mean = exp_mean(input, input_shape, max_denom=max_denom)
+        self.d_var = exp_var(input, input_shape, max_denom=max_denom)
+        self.output = (input - self.d_mean.curval) / theano.tensor.sqrt(self.d_var.curval+eps)
+
+    def updates(self):
+        rval = {}
+        rval.update(self.d_mean.updates())
+        rval.update(self.d_var.updates())
+        return rval
+
+    def _instance_initialize(self, obj):
+        obj.d_mean.initialize()
+        obj.d_var.initialize()
+
diff -r 7d8bb6d087bc -r e69249897f89 pylearn/algorithms/tests/test_exponential_mean.py
--- a/pylearn/algorithms/tests/test_exponential_mean.py	Thu May 14 16:59:20 2009 -0400
+++ b/pylearn/algorithms/tests/test_exponential_mean.py	Thu May 14 17:00:10 2009 -0400
@@ -48,3 +48,50 @@
             assert not numpy.allclose(dmean, nmean)
 
     assert i > rows_to_test
+
+def test_dynamic_normalizer():
+    x = theano.tensor.dvector()
+
+    rows_to_test = 100
+    cols=2
+
+    D = exponential_mean.DynamicNormalizer(x, (cols,), rows_to_test)
+
+    M = theano.Module()
+    M.dn = D
+    M.dn_mean = exponential_mean.exp_mean(D.output, (cols,), 50)
+    M.dn_var = exponential_mean.exp_var(D.output, (cols,), 50)
+    M.x_mean = exponential_mean.exp_mean(x, (cols,), 10)
+
+    updates = D.updates()
+    #print len(updates)
+    updates.update(M.dn_mean.updates())
+    #print len(updates)
+    updates.update(M.dn_var.updates())
+    #print len(updates)
+    updates.update(M.x_mean.updates())
+    #print len(updates)
+
+
+
+    M.f = theano.Method([x], [D.output, M.dn_mean.curval, M.dn_var.curval, M.x_mean.curval] , updates)
+
+    m = M.make()
+    m.dn.initialize()
+    m.dn_mean.initialize()
+    m.dn_var.initialize()
+    m.x_mean.initialize()
+
+
+    rng = numpy.random.RandomState(3284)
+    xval = rng.rand(rows_to_test+100,cols)
+
+    for i, xrow in enumerate(xval):
+        n_x = m.f(xrow)
+
+        #print n_x
+
+    assert numpy.all(numpy.abs(n_x[1]) < 0.15) # the means should be close to 0
+    assert numpy.all(numpy.abs(n_x[2]-1) < 0.07) # the variance should be close to 1.0
+    assert i > rows_to_test
+