--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/algorithms/logistic_regression.py	Wed Oct 22 15:56:53 2008 -0400
@@ -0,0 +1,95 @@
+import theano
+from theano import tensor as T
+from theano.tensor import nnet_ops
+from theano.compile import module
+from theano import printing, pprint
+from theano import compile
+
+import numpy as N
+
+
+class Module_Nclass(module.FancyModule):
+    class __instance_type__(module.FancyModuleInstance):
+        def initialize(self, n_in, n_out, rng=N.random):
+            #self.component is the LogisticRegressionTemplate instance that built this guy.
+
+            self.w = rng.randn(n_in, n_out)
+            self.b = rng.randn(n_out)
+            self.lr = 0.01
+            self.__hide__ = ['params']
+
+    def __init__(self, x=None, targ=None, w=None, b=None, lr=None):
+        super(Module_Nclass, self).__init__() #boilerplate
+
+        self.x = x if x is not None else T.matrix()
+        self.targ = targ if targ is not None else T.lvector()
+
+        self.w = w if w is not None else module.Member(T.dmatrix())
+        self.b = b if b is not None else module.Member(T.dvector())
+        self.lr = lr if lr is not None else module.Member(T.dscalar())
+
+        self.params = [p for p in [self.w, self.b] if p.owner is None]
+
+        xent, y = nnet_ops.crossentropy_softmax_1hot(
+                T.dot(self.x, self.w) + self.b, self.targ)
+        sum_xent = T.sum(xent)
+
+        self.y = y
+        self.sum_xent = sum_xent
+
+        #define the apply method
+        self.pred = T.argmax(T.dot(self.x, self.w) + self.b, axis=1)
+        self.apply = module.Method([self.x], self.pred)
+
+        if self.params:
+            gparams = T.grad(sum_xent, self.params)
+
+            self.update = module.Method([self.x, self.targ], sum_xent,
+                    updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
+
+class Module(module.FancyModule):
+    class __instance_type__(module.FancyModuleInstance):
+        def initialize(self, n_in):
+            #self.component is the LogisticRegressionTemplate instance that built this guy.
+
+            self.w = N.random.randn(n_in,1)
+            self.b = N.random.randn(1)
+            self.lr = 0.01
+            self.__hide__ = ['params']
+
+    def __init__(self, x=None, targ=None, w=None, b=None, lr=None):
+        super(Module, self).__init__() #boilerplate
+
+        self.x = x if x is not None else T.matrix()
+        self.targ = targ if targ is not None else T.lcol()
+
+        self.w = w if w is not None else module.Member(T.dmatrix())
+        self.b = b if b is not None else module.Member(T.dvector())
+        self.lr = lr if lr is not None else module.Member(T.dscalar())
+
+        self.params = [p for p in [self.w, self.b] if p.owner is None]
+
+        y = nnet_ops.sigmoid(T.dot(self.x, self.w))
+        xent = -self.targ * T.log(y) - (1.0 - self.targ) * T.log(1.0 - y)
+        sum_xent = T.sum(xent)
+
+        self.y = y
+        self.xent = xent
+        self.sum_xent = sum_xent
+
+        #define the apply method
+        self.pred = (T.dot(self.x, self.w) + self.b) > 0.0
+        self.apply = module.Method([self.x], self.pred)
+
+        #if this module has any internal parameters, define an update function for them
+        if self.params:
+            gparams = T.grad(sum_xent, self.params)
+            self.update = module.Method([self.x, self.targ], sum_xent,
+                                        updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
+
+
+
+class Learner(object):
+    """TODO: Encapsulate the algorithm for finding an optimal regularization coefficient"""
+    pass
+