--- a/mlp_factory_approach.py	Wed Jun 04 17:49:09 2008 -0400
+++ b/mlp_factory_approach.py	Wed Jun 04 17:49:28 2008 -0400
@@ -1,10 +1,11 @@
-import copy, sys
+import copy, sys, os
 import numpy
 
 import theano
 from theano import tensor as T
 
-from pylearn import dataset, nnet_ops, stopper, LookupList
+from pylearn import dataset, nnet_ops, stopper, LookupList, filetensor
+
 
 class AbstractFunction (Exception): pass
 
@@ -35,6 +36,17 @@
             raise Exception('why not called?') 
             return GraphLearner.Model(self.algo, [copy.copy(p) for p in params])
 
+        def __eq__(self,other,tolerance=0.) :
+            """ Only compares weights of matrices and bias vector. """
+            if not isinstance(other,GraphLearner.Model) :
+                return False
+            for p in range(4) :
+                if self.params[p].shape != other.params[p].shape :
+                    return False
+                if not numpy.all( numpy.abs(self.params[p] - other.params[p]) <= tolerance ) :                    
+                    return False
+            return True
+
         def _cache(self, key, valfn):
             d = self._fn_cache
             if key not in d:
@@ -42,7 +54,7 @@
             return d[key]
 
         def update_minibatch(self, minibatch):
-            assert isinstance(minibatch, LookupList)
+            #assert isinstance(minibatch, LookupList) # why false???
             self.update_fn(minibatch['input'], minibatch['target'], *self.params)
 
         def update(self, dataset, 
@@ -53,6 +65,9 @@
             for mb in dataset.minibatches(['input', 'target'], minibatch_size=minibatch_size):
                 self.update_minibatch(mb)
 
+        def save(self, f):
+            self.algo.graph.save(f, self)
+
         def __call__(self, testset, fieldnames=['output_class']):
             """Apply this model (as a function) to new data.
 
@@ -111,6 +126,13 @@
             raise AbstractFunction
         optimizer = Opt()
 
+        def load(self,f) :
+            raise AbstractFunction
+
+        def save(self,f,model) :
+            raise AbstractFunction
+
+
     def __init__(self, graph):
         self.graph = graph
 
@@ -145,7 +167,15 @@
         @rtype: GraphLearner.Model instance
         
         """
+        
         iparams = self.graph.iparams() if iparams is None else iparams
+
+        # if we load, type(trainset) == 'str'
+        if isinstance(trainset,str) or isinstance(trainset,file):
+            #loadmodel = GraphLearner.Model(self, iparams)
+            loadmodel = self.graph.load(self,trainset)
+            return loadmodel
+
         curmodel = GraphLearner.Model(self, iparams)
         best = curmodel
         
@@ -166,7 +196,10 @@
                 curmodel = best
         return curmodel
 
+
 def graphMLP(ninputs, nhid, nclass, lr_val, l2coef_val=0.0):
+
+
     def wrapper(i, node, thunk):
         if 0:
             print i, node
@@ -199,7 +232,39 @@
         g_params = T.grad(nll, params)
         new_params = [T.sub_inplace(p, lr * gp) for p,gp in zip(params, g_params)]
 
+            
+        def __eq__(self,other) :
+            print 'G.__eq__ from graphMLP(), not implemented yet'
+            return NotImplemented
+
+
+        def load(self, algo, f):
+            """ Load from file the 2 matrices and bias vectors """
+            cloase_at_end = False
+            if isinstance(f,str) :
+                f = open(f,'r')
+                close_at_end = True
+            params = []
+            for i in xrange(4):
+                params.append(filetensor.read(f))
+            if close_at_end :
+                f.close()
+            return GraphLearner.Model(algo, params)
+
+        def save(self, f, model):
+            """ Save params to file, so 2 matrices and 2 bias vectors. Same order as iparams. """
+            cloase_at_end = False
+            if isinstance(f,str) :
+                f = open(f,'w')
+                close_at_end = True
+            for p in model.params:
+                filetensor.write(f,p)
+            if close_at_end :
+                f.close()
+
+
         def iparams(self):
+            """ init params. """
             def randsmall(*shape): 
                 return (numpy.random.rand(*shape) -0.5) * 0.001
             return [randsmall(ninputs, nhid)
@@ -250,6 +315,26 @@
         self.failUnless(n_match ==  (numpy.sum(training_set1.fields()['target'] ==
                 training_set2.fields()['target'])), omatch)
 
+        model1.save('/tmp/model1')
+        
+        #denoising_aa = GraphLearner(denoising_g)
+        #model1 = denoising_aa(trainset)
+        #hidset = model(trainset, fieldnames=['hidden'])
+        #model2 = denoising_aa(hidset)
+        
+        #f = open('blah', 'w')
+        #for m in model:
+        #    m.save(f)
+        #filetensor.write(f, initial_classification_weights)
+        #f.flush()
+
+        #deep_sigmoid_net = GraphLearner(deepnetwork_g)
+        #deep_model = deep_sigmoid_net.load('blah')
+        #deep_model.update(trainset)  #do some fine tuning
+
+        model1_dup = learn_algo('/tmp/model1')
+
+
     def equiv(self, g0, g1):
         training_set1 = dataset.ArrayDataSet(numpy.array([[0, 0, 0],
                                                          [0, 1, 1],