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Part changes to datasets.py and mocap.py to download data resources for examples. Not working currently!

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This commit is contained in:
Neil Lawrence 2013-06-06 09:04:02 +01:00
parent 642dbfc764
commit 45a2c2b3a6
4 changed files with 108 additions and 23 deletions
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66
GPy/util/datasets.py
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@ -9,6 +9,61 @@ import urllib2 as url
data_path = os.path.join(os.path.dirname(__file__), 'datasets')
default_seed = 10000
neil_url = 'http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/'
def prompt_user():
# raw_input returns the empty string for "enter"
yes = set(['yes', 'y'])
no = set(['no','n'])
choice = raw_input().lower()
if choice in yes:
return True
elif choice in no:
return False
else:
sys.stdout.write("Please respond with 'yes', 'y' or 'no', 'n'")
return prompt_user()
def download_data(dataset_name=None):
"""Helper function which contains the resource locations for each data set in one place"""
# Note: there may be a better way of doing this. One of the pythonistas will need to take a look. Neil
data_resources = {'oil': {'urls' : [neil_url + 'oil_data/'],
'files' : [['DataTrnLbls.txt', 'DataTrn.txt']],
'citation' : 'Bishop, C. M. and G. D. James (1993). Analysis of multiphase flows using dual-energy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research A327, 580-593',
'details' : """The three phase oil data used initially for demonstrating the Generative Topographic mapping.""",
'agreement' : None},
'brendan_faces' : {'url' : ['http://www.cs.nyu.edu/~roweis/data/'],
'files' [['frey_rawface.mat']],
'citation' : 'Frey, B. J., Colmenarez, A and Huang, T. S. Mixtures of Local Linear Subspaces for Face Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1998, 32-37, June 1998. Computer Society Press, Los Alamitos, CA.',
'details' : """A video of Brendan Frey's face popularized as a benchmark for visualization by the Locally Linear Embedding.""",
'agreement': None}
}
print('Acquiring resource: ' + dataset_name)
# TODO, check resource is in dictionary!
dr = data_resources[dataset_name]
print('Details of data: ')
print(dr['details'])
if dr['citation']:
print('Please cite:')
print(dr['citation'])
if dr['agreement']:
print('You must also agree to the following:')
print(dr['agreement'])
print('Do you wish to proceed with the download? [yes/no]')
if prompt_user()==False:
return False
for url, files in zip(dr['urls'], dr['files']):
for file in files:
download_resource(url + file)
return True
# Some general utilities.
def sample_class(f):
@ -17,7 +72,7 @@ def sample_class(f):
c = np.where(c, 1, -1)
return c
def fetch_dataset(resource, save_name = None, save_file = True, messages = True):
def download_resource(resource, save_name = None, save_file = True, messages = True):
if messages:
print "Downloading resource: " , resource, " ... ",
response = url.urlopen(resource)
@ -57,10 +112,11 @@ def simulation_BGPLVM():
# The data sets
def oil():
fid = open(os.path.join(data_path, 'oil', 'DataTrn.txt'))
download_data('oil')
fid = open(oil_train_file)
X = np.fromfile(fid, sep='\t').reshape((-1, 12))
fid.close()
fid = open(os.path.join(data_path, 'oil', 'DataTrnLbls.txt'))
fid = open(oil_trainlbls_file)
Y = np.fromfile(fid, sep='\t').reshape((-1, 3)) * 2. - 1.
fid.close()
return {'X': X, 'Y': Y, 'info': "The oil data from Bishop and James (1993)."}
@ -283,6 +339,10 @@ def cmu_mocap(subject, train_motions, test_motions=[], sample_every=4):
# Load in subject skeleton.
subject_dir = os.path.join(data_path, 'mocap', 'cmu', subject)
# Make sure the data is downloaded.
mocap.fetch_cmu(([subject], [train_motions]), skel_store_dir=subject_dir,motion_store_dir=subject_dir)
skel = GPy.util.mocap.acclaim_skeleton(os.path.join(subject_dir, subject + '.asf'))
# Set up labels for each sequence