Remove _simple

1 files changed, 0 insertions, 71 deletions

diff --git a/model/joint_simple_mlp_tgtcls.py b/model/joint_simple_mlp_tgtcls.py
deleted file mode 100644
index d6d4e49..0000000
--- a/model/joint_simple_mlp_tgtcls.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import numpy
-import theano
-from theano import tensor
-from blocks import roles
-from blocks.bricks import application, MLP, Rectifier, Softmax
-
-import error
-from model.mlp import FFMLP, Stream
-
-
-class Model(FFMLP):
-    def __init__(self, config, **kwargs):
-        super(Model, self).__init__(config, **kwargs)
-        
-        self.dest_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden_dest] + [Softmax()],
-                       dims=[config.dim_hidden[-1]] + config.dim_hidden_dest + [config.dim_output_dest],
-                       name='dest_mlp')
-        self.time_mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden_time] + [Softmax()],
-                       dims=[config.dim_hidden[-1]] + config.dim_hidden_time + [config.dim_output_time],
-                       name='time_mlp')
-
-        self.dest_classes = theano.shared(numpy.array(config.dest_tgtcls, dtype=theano.config.floatX), name='dest_classes')
-        self.time_classes = theano.shared(numpy.array(config.time_tgtcls, dtype=theano.config.floatX), name='time_classes')
-
-        self.inputs.append('input_time')
-        self.children.extend([self.dest_mlp, self.time_mlp])
-
-    def _push_initialization_config(self):
-        super(Model, self)._push_initialization_config()
-        for mlp in [self.dest_mlp, self.time_mlp]:
-            mlp.weights_init = self.config.mlp_weights_init
-            mlp.biases_init = self.config.mlp_biases_init
-
-    @application(outputs=['destination', 'duration'])
-    def predict(self, **kwargs):
-        hidden = super(Model, self).predict(**kwargs)
-
-        dest_cls_probas = self.dest_mlp.apply(hidden)
-        dest_outputs = tensor.dot(dest_cls_probas, self.dest_classes)
-
-        time_cls_probas = self.time_mlp.apply(hidden)
-        time_outputs = kwargs['input_time'] + tensor.dot(time_cls_probas, self.time_classes)
-
-        self.add_auxiliary_variable(dest_cls_probas, name='destination classes ponderations')
-        self.add_auxiliary_variable(time_cls_probas, name='time classes ponderations')
-
-        return (dest_outputs, time_outputs)
-
-    @predict.property('inputs')
-    def predict_inputs(self):
-        return self.inputs
-
-    @application(outputs=['cost'])
-    def cost(self, **kwargs):
-        (destination_hat, time_hat) = self.predict(**kwargs)
-
-        destination = tensor.concatenate((kwargs['destination_latitude'][:, None],
-                                          kwargs['destination_longitude'][:, None]), axis=1)
-        time = kwargs['travel_time']
-
-        destination_cost = error.erdist(destination_hat, destination).mean()
-        time_cost = error.rmsle(time_hat.flatten(), time.flatten())
-
-        self.add_auxiliary_variable(destination_cost, [roles.COST], 'destination_cost')
-        self.add_auxiliary_variable(time_cost, [roles.COST], 'time_cost')
-
-        return destination_cost + self.config.time_cost_factor * time_cost
-
-    @cost.property('inputs')
-    def cost_inputs(self):
-        return self.inputs + ['destination_latitude', 'destination_longitude', 'travel_time']