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-rw-r--r--config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py37
1 files changed, 0 insertions, 37 deletions
diff --git a/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py b/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
deleted file mode 100644
index a067111..0000000
--- a/config/dest_simple_mlp_tgtcls_1_cswdtx_alexandre.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import os
-import cPickle
-
-from blocks.initialization import IsotropicGaussian, Constant
-
-import data
-from model.dest_simple_mlp_tgtcls import Model, Stream
-
-
-n_begin_end_pts = 5 # how many points we consider at the beginning and end of the known trajectory
-
-with open(os.path.join(data.path, 'arrival-clusters.pkl')) as f: tgtcls = cPickle.load(f)
-
-dim_embeddings = [
- ('origin_call', data.origin_call_train_size, 10),
- ('origin_stand', data.stands_size, 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]
-
-embed_weights_init = IsotropicGaussian(0.01)
-mlp_weights_init = IsotropicGaussian(0.1)
-mlp_biases_init = Constant(0.01)
-
-learning_rate = 0.01
-momentum = 0.9
-batch_size = 200
-
-valid_set = 'cuts/test_times_0'
-max_splits = 100