Remove _simple

1 files changed, 0 insertions, 54 deletions

diff --git a/config/joint_simple_mlp_tgtcls_111_cswdtx_bigger.py b/config/joint_simple_mlp_tgtcls_111_cswdtx_bigger.py
deleted file mode 100644
index 8e991a1..0000000
--- a/config/joint_simple_mlp_tgtcls_111_cswdtx_bigger.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os
-import cPickle
-
-from blocks.initialization import IsotropicGaussian, Constant
-
-import data
-from model.joint_simple_mlp_tgtcls import Model, Stream
-
-
-n_begin_end_pts = 10     # 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:
-    dest_tgtcls = cPickle.load(f)
-
-# generate target classes for time prediction as a Fibonacci sequence
-time_tgtcls = [1, 2]
-for i in range(21):
-    time_tgtcls.append(time_tgtcls[-1] + time_tgtcls[-2])
-
-dim_embeddings = [
-    ('origin_call', data.origin_call_size, 15),
-    ('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),
-]
-
-# Common network part
-dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
-dim_hidden = [5000]
-
-# Destination prediction part
-dim_hidden_dest = [1000]
-dim_output_dest = dest_tgtcls.shape[0]
-
-# Time prediction part
-dim_hidden_time = [500]
-dim_output_time = len(time_tgtcls)
-
-# Cost ratio between distance cost and time cost
-time_cost_factor = 4
-
-embed_weights_init = IsotropicGaussian(0.01)
-mlp_weights_init = IsotropicGaussian(0.1)
-mlp_biases_init = Constant(0.01)
-
-# use adadelta, so no learning_rate or momentum
-batch_size = 200
-
-valid_set = 'cuts/test_times_0'
-
-max_splits = 100