commit 7604b28ff6e8293af383ae7328ea2285b3c9bba5
parent f768d3e770216d4227ffd989cf98f1628fc476a3
Author: Alex Auvolat <alex.auvolat@ens.fr>
Date: Wed, 29 Apr 2015 16:01:13 -0400
Debug.
Diffstat:
3 files changed, 24 insertions(+), 13 deletions(-)
diff --git a/hdist.py b/hdist.py
@@ -12,10 +12,10 @@ def hdist(a, b):
rearth = const(6371)
deg2rad = const(3.141592653589793 / 180)
- lat1 = a[:, 1] * deg2rad
- lon1 = a[:, 0] * deg2rad
- lat2 = b[:, 1] * deg2rad
- lon2 = b[:, 0] * deg2rad
+ lat1 = a[:, 0] * deg2rad
+ lon1 = a[:, 1] * deg2rad
+ lat2 = b[:, 0] * deg2rad
+ lon2 = b[:, 1] * deg2rad
dlat = abs(lat1-lat2)
dlon = abs(lon1-lon2)
diff --git a/model.py b/model.py
@@ -46,20 +46,30 @@ if __name__ == "__main__":
def setup_stream():
# Load the training and test data
- train = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5', which_set='train', subset=slice(0, config.train_size - config.n_valid), load_in_memory=True)
+ train = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5',
+ which_set='train',
+ subset=slice(0, config.train_size - config.n_valid),
+ load_in_memory=True)
train = DataStream(train, iteration_scheme=SequentialExampleScheme(config.train_size - config.n_valid))
train = transformers.add_first_k(config.n_begin_end_pts, train)
train = transformers.add_random_k(config.n_begin_end_pts, train)
train = transformers.add_destination(train)
- train = transformers.Select(train, ('origin_stand', 'origin_call', 'first_k_latitude', 'last_k_latitude', 'first_k_longitude', 'last_k_longitude', 'destination_latitude', 'destination_longitude'))
+ train = transformers.Select(train, ('origin_stand', 'origin_call', 'first_k_latitude',
+ 'last_k_latitude', 'first_k_longitude', 'last_k_longitude',
+ 'destination_latitude', 'destination_longitude'))
train_stream = Batch(train, iteration_scheme=ConstantScheme(config.batch_size))
- valid = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5', which_set='train', subset=slice(config.train_size - config.n_valid, config.train_size), load_in_memory=True)
+ valid = H5PYDataset('/data/lisatmp3/simonet/taxi/data.hdf5',
+ which_set='train',
+ subset=slice(config.train_size - config.n_valid, config.train_size),
+ load_in_memory=True)
valid = DataStream(valid, iteration_scheme=SequentialExampleScheme(config.n_valid))
valid = transformers.add_first_k(config.n_begin_end_pts, valid)
valid = transformers.add_last_k(config.n_begin_end_pts, valid)
valid = transformers.add_destination(valid)
- valid = transformers.Select(valid, ('origin_stand', 'origin_call', 'first_k_latitude', 'last_k_latitude', 'first_k_longitude', 'last_k_longitude', 'destination_latitude', 'destination_longitude'))
+ valid = transformers.Select(valid, ('origin_stand', 'origin_call', 'first_k_latitude',
+ 'last_k_latitude', 'first_k_longitude', 'last_k_longitude',
+ 'destination_latitude', 'destination_longitude'))
valid_stream = Batch(valid, iteration_scheme=ConstantScheme(1000))
return (train_stream, valid_stream)
@@ -68,15 +78,15 @@ def main():
# 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_firstk = tensor.concatenate((x_firstk_latitude, x_firstk_longitude), axis=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]
- x_lastk = tensor.concatenate((x_lastk_latitude, x_lastk_longitude), axis=1)
x_client = tensor.lvector('origin_call')
x_stand = tensor.lvector('origin_stand')
- y = tensor.concatenate((tensor.vector('destination_latitude')[:, None], tensor.vector('destination_longitude')[:, None]), axis=1)
+
+ y = tensor.concatenate((tensor.vector('destination_latitude')[:, None],
+ tensor.vector('destination_longitude')[:, None]), axis=1)
# Define the model
client_embed_table = LookupTable(length=config.n_clients+1, dim=config.dim_embed, name='client_lookup')
@@ -87,7 +97,8 @@ def main():
# Create the Theano variables
client_embed = client_embed_table.apply(x_client).flatten(ndim=2)
stand_embed = stand_embed_table.apply(x_stand).flatten(ndim=2)
- inputs = tensor.concatenate([x_firstk, x_lastk, client_embed, stand_embed],
+ inputs = tensor.concatenate([x_firstk_latitude, x_firstk_longitude,
+ x_lastk_latitude, x_lastk_longitude, client_embed, stand_embed],
axis=1)
# inputs = theano.printing.Print("inputs")(inputs)
outputs = mlp.apply(inputs)
diff --git a/transformers.py b/transformers.py
@@ -43,7 +43,7 @@ def add_random_k(k, stream):
lon = at_least_k(k, x[id_latitude], True, True)
loc = random.randrange(len(lat)-k+1)
return (numpy.array(lat[loc:loc+k], dtype=theano.config.floatX),
- numpy.array(lon[loc:loc+k], dtype=theano.config.floatX)),
+ numpy.array(lon[loc:loc+k], dtype=theano.config.floatX))
return Mapping(stream, random_k, ('last_k_latitude', 'last_k_longitude'))
def add_last_k(k, stream):
