* Begin reorganizing neuralnet work

spacy/_ml.pxd

@@ -21,8 +21,8 @@ cdef int arg_max_if_zero(const weight_t* scores, const int* costs, int n_classes
 
 
 cdef class Model:
-    cdef int n_classes
-    cdef int n_feats
+    cdef readonly int n_classes
+    cdef readonly int n_feats
     
     cdef const weight_t* score(self, atom_t* context) except NULL
     cdef int set_scores(self, weight_t* scores, atom_t* context) except -1

spacy/_theano.pyx

@@ -10,8 +10,7 @@ from os import path
 
 cdef class TheanoModel(Model):
     def __init__(self, n_classes, input_spec, train_func, predict_func, model_loc=None,
-                 eta=0.001, mu=0.9,
-                 debug=None):
+                 eta=0.001, mu=0.9, debug=None):
         if model_loc is not None and path.isdir(model_loc):
             model_loc = path.join(model_loc, 'model')
 

spacy/syntax/parser.pxd

@@ -8,6 +8,6 @@ from ..tokens cimport Tokens, TokenC
 
 
 cdef class Parser:
-    cdef readonly object cfg
-    cdef readonly Model model
-    cdef readonly TransitionSystem moves
+    cdef public object cfg
+    cdef public Model model
+    cdef public TransitionSystem moves

spacy/syntax/parser.pyx

@@ -59,13 +59,11 @@ def get_templates(name):
 
 
 cdef class Parser:
-    def __init__(self, StringStore strings, model_dir, transition_system,
-                 get_model=Model):
+    def __init__(self, StringStore strings, model_dir, transition_system):
         assert os.path.exists(model_dir) and os.path.isdir(model_dir)
         self.cfg = Config.read(model_dir, 'config')
         self.moves = transition_system(strings, self.cfg.labels)
-        templates = get_templates(self.cfg.features)
-        self.model = get_model(self.moves.n_moves, templates, model_dir)
+        self.model = Model(self.moves.n_moves, self.cfg.templates, model_dir)
 
     def __call__(self, Tokens tokens):
         cdef StateClass stcls = StateClass.init(tokens.data, tokens.length)
@@ -73,6 +71,8 @@ cdef class Parser:
 
         cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
                                   self.model.n_feats, self.model.n_feats)
+        eg.scores[0] = 10
+        assert eg.c.scores[0] == 10
         while not stcls.is_final():
             memset(eg.c.scores, 0, eg.c.nr_class * sizeof(weight_t))
         
@@ -91,7 +91,9 @@ cdef class Parser:
         self.moves.initialize_state(stcls)
         cdef Example eg = Example(self.model.n_classes, CONTEXT_SIZE,
                                   self.model.n_feats, self.model.n_feats)
-        cdef int cost = 0
+        cdef weight_t loss = 0
+        words = [w.orth_ for w in tokens]
+        cdef Transition G
         while not stcls.is_final():
             memset(eg.c.scores, 0, eg.c.nr_class * sizeof(weight_t))
         
@@ -101,9 +103,13 @@ cdef class Parser:
 
             self.model.train(eg)
 
+            G = self.moves.c[eg.c.guess]
+
+            #if eg.c.cost != 0:
+            #    print self.moves.move_name(G.move, G.label), stcls.print_state(words)
             self.moves.c[eg.c.guess].do(stcls, self.moves.c[eg.c.guess].label)
-            cost += eg.c.cost
-        return cost
+            loss += eg.c.loss
+        return loss