Add models for time predictioAdd models for time prediction

1 files changed, 0 insertions, 71 deletions

diff --git a/model/simple_mlp.py b/model/simple_mlp.py
deleted file mode 100644
index fc065f7..0000000
--- a/model/simple_mlp.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from blocks.bricks import MLP, Rectifier, Linear, Sigmoid, Identity
-from blocks.bricks.lookup import LookupTable
-
-from blocks.initialization import IsotropicGaussian, Constant
-
-from theano import tensor
-
-import data
-import hdist
-
-class Model(object):
-    def __init__(self, config):
-        # The input and the targets
-        x_firstk_latitude = (tensor.matrix('first_k_latitude') - data.porto_center[0]) / data.data_std[0]
-        x_firstk_longitude = (tensor.matrix('first_k_longitude') - data.porto_center[1]) / data.data_std[1]
-
-        x_lastk_latitude = (tensor.matrix('last_k_latitude') - data.porto_center[0]) / data.data_std[0]
-        x_lastk_longitude = (tensor.matrix('last_k_longitude') - data.porto_center[1]) / data.data_std[1]
-
-        input_list = [x_firstk_latitude, x_firstk_longitude, x_lastk_latitude, x_lastk_longitude]
-        embed_tables = []
-
-        self.require_inputs = ['first_k_latitude', 'first_k_longitude', 'last_k_latitude', 'last_k_longitude']
-
-        for (varname, num, dim) in config.dim_embeddings:
-            self.require_inputs.append(varname)
-            vardata = tensor.lvector(varname)
-            tbl = LookupTable(length=num, dim=dim, name='%s_lookup'%varname)
-            embed_tables.append(tbl)
-            input_list.append(tbl.apply(vardata))
-
-        y = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
-                                tensor.vector('destination_longitude')[:, None]), axis=1)
-
-        # Define the model
-        mlp = MLP(activations=[Rectifier() for _ in config.dim_hidden] + [Identity()],
-                           dims=[config.dim_input] + config.dim_hidden + [config.dim_output])
-
-        # Create the Theano variables
-        inputs = tensor.concatenate(input_list, axis=1)
-        # inputs = theano.printing.Print("inputs")(inputs)
-        outputs = mlp.apply(inputs)
-
-        # Normalize & Center
-        # outputs = theano.printing.Print("normal_outputs")(outputs)
-        outputs = data.data_std * outputs + data.porto_center
-
-        # outputs = theano.printing.Print("outputs")(outputs)
-        # y = theano.printing.Print("y")(y)
-
-        outputs.name = 'outputs'
-
-        # Calculate the cost
-        cost = hdist.erdist(outputs, y).mean()
-        cost.name = 'cost'
-        hcost = hdist.hdist(outputs, y).mean()
-        hcost.name = 'hcost'
-
-        # Initialization
-        for tbl in embed_tables:
-            tbl.weights_init = IsotropicGaussian(0.001)
-        mlp.weights_init = IsotropicGaussian(0.01)
-        mlp.biases_init = Constant(0.001)
-
-        for tbl in embed_tables:
-            tbl.initialize()
-        mlp.initialize()
-
-        self.cost = cost
-        self.hcost = hcost
-        self.outputs = outputs