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authorAlex Auvolat <alex.auvolat@ens.fr>2015-05-05 14:15:21 -0400
committerAlex Auvolat <alex.auvolat@ens.fr>2015-05-05 14:15:21 -0400
commit54613c1f9cf510ca7a71d6619418f2247515aec6 (patch)
treebed9a5a11ef5b7feecee44095a29400e32f76b05 /config/dest_simple_mlp_tgtcls_0_cs.py
parent712035b88be1816d3fbd58ce69ae6464767c780e (diff)
downloadtaxi-54613c1f9cf510ca7a71d6619418f2247515aec6.tar.gz
taxi-54613c1f9cf510ca7a71d6619418f2247515aec6.zip
Add models for time predictioAdd models for time prediction
Diffstat (limited to 'config/dest_simple_mlp_tgtcls_0_cs.py')
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diff --git a/config/dest_simple_mlp_tgtcls_0_cs.py b/config/dest_simple_mlp_tgtcls_0_cs.py
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+import cPickle
+
+import data
+
+import model.dest_simple_mlp_tgtcls as model
+
+n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
+n_end_pts = 5
+
+n_valid = 1000
+
+with open(data.DATA_PATH + "/arrival-clusters.pkl") as f: tgtcls = cPickle.load(f)
+
+dim_embeddings = [
+ ('origin_call', data.n_train_clients+1, 10),
+ ('origin_stand', data.n_stands+1, 10)
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = []
+dim_output = tgtcls.shape[0]
+
+learning_rate = 0.0001
+momentum = 0.99
+batch_size = 32