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authorAlex Auvolat <alex.auvolat@ens.fr>2015-05-05 13:11:18 -0400
committerAlex Auvolat <alex.auvolat@ens.fr>2015-05-05 13:11:33 -0400
commit712035b88be1816d3fbd58ce69ae6464767c780e (patch)
tree85a56482c508e10b946e6501544e86f62cab53a7 /config/simple_mlp_tgtcls_1_cswdtx.py
parent66159d9fce0129116e82e74cf3eb1d9e048b253d (diff)
downloadtaxi-712035b88be1816d3fbd58ce69ae6464767c780e.tar.gz
taxi-712035b88be1816d3fbd58ce69ae6464767c780e.zip
Add day type and taxi id
Diffstat (limited to 'config/simple_mlp_tgtcls_1_cswdtx.py')
-rw-r--r--config/simple_mlp_tgtcls_1_cswdtx.py30
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diff --git a/config/simple_mlp_tgtcls_1_cswdtx.py b/config/simple_mlp_tgtcls_1_cswdtx.py
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+++ b/config/simple_mlp_tgtcls_1_cswdtx.py
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+import cPickle
+
+import data
+
+import model.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),
+ ('week_of_year', 52, 10),
+ ('day_of_week', 7, 10),
+ ('qhour_of_day', 24 * 4, 10),
+ ('day_type', 3, 10),
+ ('taxi_id', 448, 10),
+]
+
+dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
+dim_hidden = [500]
+dim_output = tgtcls.shape[0]
+
+learning_rate = 0.0001
+momentum = 0.99
+batch_size = 32