* Work on a theano-driven model for the parser

spacy/_theano.pyx

@@ -0,0 +1,44 @@
+from thinc.example cimport Example
+
+
+cdef class TheanoModel(Model):
+    def __init__(self, n_classes, input_layer, train_func, predict_func, model_loc=None):
+        if model_loc is not None and path.isdir(model_loc):
+            model_loc = path.join(model_loc, 'model')
+        self.n_classes = n_classes
+        
+        tables = []
+        lengths = []
+        for window_size, n_dims, vocab_size in input_structure:
+            tables.append(EmbeddingTable(n_dims, vocab_size, initializer))
+            lengths.append(window_size)
+
+        self.input_layer = InputLayer(lengths, tables)
+
+        self.train_func = train_func
+        self.predict_func = predict_func
+
+        self.model_loc = model_loc
+        if self.model_loc and path.exists(self.model_loc):
+            self._model.load(self.model_loc, freq_thresh=0)
+
+    def train(self, Instance eg):
+        pass
+
+    def predict(self, Instance eg):
+
+    cdef const weight_t* score(self, atom_t* context) except NULL:
+        self.set_scores(self._scores, context)
+        return self._scores
+    
+    cdef int set_scores(self, weight_t* scores, atom_t* context) except -1:
+        # TODO f(context) --> Values
+        self._input_layer.fill(self._x, self._values, use_avg=False)
+        theano_scores = self._predict(self._x)
+        for i in range(self.n_classes):
+            output[i] = theano_scores[i]
+
+    cdef int update(self, atom_t* context, class_t guess, class_t gold, int cost) except -1:
+        # TODO f(context) --> Values
+        self._input_layer.fill(self._x, self._values, use_avg=False)
+