mirror of
https://github.com/SheffieldML/GPy.git
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Merge branch 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
Alan Saul
commit
b142b68876
4 changed files with 241 additions and 85 deletions
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@ -151,7 +151,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_f_eval=50, plot=
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data_show = GPy.util.visualize.vector_show(y)
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lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes) # , sense_axes=sense_axes)
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raw_input('Press enter to finish')
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plt.close('all')
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plt.close(fig)
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return m
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def oil_100():
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@ -304,75 +304,72 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
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m.plot_scales("MRD Scales")
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return m
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# # Commented out because dataset is missing
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# def brendan_faces():
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# from GPy import kern
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# data = GPy.util.datasets.brendan_faces()
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# Q = 2
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# Y = data['Y'][0:-1:10, :]
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# # Y = data['Y']
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# Yn = Y - Y.mean()
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# Yn /= Yn.std()
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def brendan_faces():
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from GPy import kern
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data = GPy.util.datasets.brendan_faces()
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Q = 2
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Y = data['Y'][0:-1:10, :]
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# Y = data['Y']
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Yn = Y - Y.mean()
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Yn /= Yn.std()
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# m = GPy.models.GPLVM(Yn, Q)
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# # m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
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m = GPy.models.GPLVM(Yn, Q)
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# m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
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# # optimize
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# m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
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# optimize
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m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
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# m.ensure_default_constraints()
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# m.optimize('scg', messages=1, max_f_eval=10000)
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m.ensure_default_constraints()
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m.optimize('scg', messages=1, max_f_eval=10000)
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# ax = m.plot_latent(which_indices=(0, 1))
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# y = m.likelihood.Y[0, :]
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# data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
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# lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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# raw_input('Press enter to finish')
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# plt.close('all')
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ax = m.plot_latent(which_indices=(0, 1))
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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lvm_visualizer.close()
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# return m
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return m
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# # Commented out because dataset is missing
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# def stick():
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# data = GPy.util.datasets.stick()
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# m = GPy.models.GPLVM(data['Y'], 2)
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def stick():
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data = GPy.util.datasets.stick()
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m = GPy.models.GPLVM(data['Y'], 2)
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# # optimize
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# m.ensure_default_constraints()
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# m.optimize(messages=1, max_f_eval=10000)
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# m._set_params(m._get_params())
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=10000)
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m._set_params(m._get_params())
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# ax = m.plot_latent()
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# y = m.likelihood.Y[0, :]
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# data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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# lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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# raw_input('Press enter to finish')
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# plt.close('all')
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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lvm_visualizer.close()
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# return m
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return m
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# # Commented out because dataset is missing
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# def cmu_mocap(subject='35', motion=['01'], in_place=True):
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def cmu_mocap(subject='35', motion=['01'], in_place=True):
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# data = GPy.util.datasets.cmu_mocap(subject, motion)
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# Y = data['Y']
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# if in_place:
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# # Make figure move in place.
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# data['Y'][:, 0:3] = 0.0
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# m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
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data = GPy.util.datasets.cmu_mocap(subject, motion)
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Y = data['Y']
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if in_place:
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# Make figure move in place.
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data['Y'][:, 0:3] = 0.0
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m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
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# # optimize
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# m.ensure_default_constraints()
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# m.optimize(messages=1, max_f_eval=10000)
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=10000)
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# ax = m.plot_latent()
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# y = m.likelihood.Y[0, :]
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# data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
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# lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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# raw_input('Press enter to finish')
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# plt.close('all')
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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lvm_visualizer.close()
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# return m
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return m
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# def BGPLVM_oil():
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# data = GPy.util.datasets.oil()
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@ -9,6 +9,61 @@ import urllib2 as url
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data_path = os.path.join(os.path.dirname(__file__), 'datasets')
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default_seed = 10000
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neil_url = 'http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/'
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def prompt_user():
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# raw_input returns the empty string for "enter"
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yes = set(['yes', 'y'])
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no = set(['no','n'])
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choice = raw_input().lower()
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if choice in yes:
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return True
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elif choice in no:
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return False
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else:
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sys.stdout.write("Please respond with 'yes', 'y' or 'no', 'n'")
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return prompt_user()
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def download_data(dataset_name=None):
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"""Helper function which contains the resource locations for each data set in one place"""
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# Note: there may be a better way of doing this. One of the pythonistas will need to take a look. Neil
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data_resources = {'oil': {'urls' : [neil_url + 'oil_data/'],
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'files' : [['DataTrnLbls.txt', 'DataTrn.txt']],
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'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',
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'details' : """The three phase oil data used initially for demonstrating the Generative Topographic mapping.""",
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'agreement' : None},
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'brendan_faces' : {'url' : ['http://www.cs.nyu.edu/~roweis/data/'],
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'files': [['frey_rawface.mat']],
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'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.',
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'details' : """A video of Brendan Frey's face popularized as a benchmark for visualization by the Locally Linear Embedding.""",
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'agreement': None}
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}
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print('Acquiring resource: ' + dataset_name)
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# TODO, check resource is in dictionary!
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dr = data_resources[dataset_name]
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print('Details of data: ')
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print(dr['details'])
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if dr['citation']:
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print('Please cite:')
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print(dr['citation'])
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if dr['agreement']:
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print('You must also agree to the following:')
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print(dr['agreement'])
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print('Do you wish to proceed with the download? [yes/no]')
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if prompt_user()==False:
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return False
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for url, files in zip(dr['urls'], dr['files']):
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for file in files:
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download_resource(url + file)
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return True
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# Some general utilities.
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def sample_class(f):
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@ -17,7 +72,7 @@ def sample_class(f):
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c = np.where(c, 1, -1)
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return c
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def fetch_dataset(resource, save_name = None, save_file = True, messages = True):
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def download_resource(resource, save_name = None, save_file = True, messages = True):
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if messages:
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print "Downloading resource: " , resource, " ... ",
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response = url.urlopen(resource)
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@ -57,14 +112,19 @@ def simulation_BGPLVM():
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# The data sets
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def oil():
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fid = open(os.path.join(data_path, 'oil', 'DataTrn.txt'))
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#if download_data('oil'):
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oil_train_file = os.path.join(data_path, 'oil', 'DataTrn.txt')
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oil_trainlbls_file = os.path.join(data_path, 'oil', 'DataTrnLbls.txt')
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fid = open(oil_train_file)
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X = np.fromfile(fid, sep='\t').reshape((-1, 12))
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fid.close()
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fid = open(os.path.join(data_path, 'oil', 'DataTrnLbls.txt'))
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fid = open(oil_trainlbls_file)
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Y = np.fromfile(fid, sep='\t').reshape((-1, 3)) * 2. - 1.
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fid.close()
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return {'X': X, 'Y': Y, 'info': "The oil data from Bishop and James (1993)."}
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#else:
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# throw an error
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def oil_100(seed=default_seed):
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np.random.seed(seed=seed)
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data = oil()
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@ -111,10 +171,13 @@ def silhouette():
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return {'X': X, 'Y': Y, 'Xtest': Xtest, 'Ytest': Ytest, 'info': "Artificial silhouette simulation data developed from Agarwal and Triggs (2004)."}
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def stick():
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#if download_data('stick'):
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Y, connect = GPy.util.mocap.load_text_data('run1', data_path)
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Y = Y[0:-1:4, :]
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lbls = 'connect'
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return {'Y': Y, 'connect' : connect, 'info': "Stick man data from Ohio."}
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# else:
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# throw an error.
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def swiss_roll_generated(N=1000, sigma=0.0):
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with open(os.path.join(data_path, 'swiss_roll.pickle')) as f:
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@ -283,6 +346,10 @@ def cmu_mocap(subject, train_motions, test_motions=[], sample_every=4):
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# Load in subject skeleton.
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subject_dir = os.path.join(data_path, 'mocap', 'cmu', subject)
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# Make sure the data is downloaded.
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mocap.fetch_cmu(([subject], [train_motions]), skel_store_dir=subject_dir,motion_store_dir=subject_dir)
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skel = GPy.util.mocap.acclaim_skeleton(os.path.join(subject_dir, subject + '.asf'))
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# Set up labels for each sequence
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@ -693,7 +693,7 @@ skel = acclaim_skeleton()
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def fetch_data(base_url = 'http://mocap.cs.cmu.edu:8080/subjects', skel_store_dir = '.', motion_store_dir = '.', subj_motions = None, store_motions = True, return_motions = True, messages = True):
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def fetch_cmu(subj_motions, base_url = 'http://mocap.cs.cmu.edu:8080/subjects', skel_store_dir = '.', motion_store_dir = '.', store_motions = True, return_motions = True, messages = True):
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'''
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Download and store the skel. and motions indicated in a tuple (A,B) where A is a list of skeletons and B
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the corresponding 2-D list of motions, ie B_ij is the j-th motion to download for skeleton A_i
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@ -702,9 +702,9 @@ def fetch_data(base_url = 'http://mocap.cs.cmu.edu:8080/subjects', skel_store_di
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e.g.
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# Download the data, do not return anything
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GPy.util.mocap.fetch_data(subj_motions = ([35],[[1,2,3]]), return_motions = False)
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GPy.util.mocap.fetch_cmu(subj_motions = ([35],[[1,2,3]]), return_motions = False)
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# Fetch and return the data in a list. Do not store them anywhere
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GPy.util.mocap.fetch_data(subj_motions = ([35],[[1,2,3]]), return_motions = True, store_motions = False)
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GPy.util.mocap.fetch_cmu(subj_motions = ([35],[[1,2,3]]), return_motions = True, store_motions = False)
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In both cases above, if the data do exist in the given skel_store_dir and motion_store_dir, they are just loaded from there.
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'''
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@ -752,7 +752,7 @@ def fetch_data(base_url = 'http://mocap.cs.cmu.edu:8080/subjects', skel_store_di
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os.mkdir(cur_skel_dir)
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if not os.path.isdir(motion_store_dir + cur_skel_suffix):
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os.mkdir(motion_store_dir + cur_skel_suffix)
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cur_skel_data = dat.fetch_dataset(cur_skel_url, cur_skel_file, store_motions, messages)
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cur_skel_data = dat.download_resource(cur_skel_url, cur_skel_file, store_motions, messages)
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if return_motions:
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all_skels.append(cur_skel_data)
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@ -765,7 +765,7 @@ def fetch_data(base_url = 'http://mocap.cs.cmu.edu:8080/subjects', skel_store_di
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if return_motions:
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cur_motion_data = f.read()
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else:
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cur_motion_data = dat.fetch_dataset(cur_motion_url, cur_motion_file, store_motions, messages)
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cur_motion_data = dat.download_resource(cur_motion_url, cur_motion_file, store_motions, messages)
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if return_motions:
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all_motions[i].append(cur_motion_data)
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@ -5,34 +5,69 @@ import numpy as np
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import matplotlib as mpl
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import time
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import Image
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import visual
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class data_show:
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"""
|
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The data show class is a base class which describes how to visualize a
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The data_show class is a base class which describes how to visualize a
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particular data set. For example, motion capture data can be plotted as a
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stick figure, or images are shown using imshow. This class enables latent
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to data visualizations for the GP-LVM.
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"""
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def __init__(self, vals, axes=None):
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def __init__(self, vals):
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self.vals = vals.copy()
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# If no axes are defined, create some.
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def modify(self, vals):
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raise NotImplementedError, "this needs to be implemented to use the data_show class"
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def close(self):
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raise NotImplementedError, "this needs to be implemented to use the data_show class"
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class vpython_show(data_show):
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"""
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the vpython_show class is a base class for all visualization methods that use vpython to display. It is initialized with a scene. If the scene is set to None it creates a scene window.
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"""
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def __init__(self, vals, scene=None):
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data_show.__init__(self, vals)
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# If no axes are defined, create some.
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if scene==None:
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self.scene = visual.display(title='Data Visualization')
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else:
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self.scene = scene
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def close(self):
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self.scene.exit()
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class matplotlib_show(data_show):
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"""
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the matplotlib_show class is a base class for all visualization methods that use matplotlib. It is initialized with an axis. If the axis is set to None it creates a figure window.
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"""
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def __init__(self, vals, axes=None):
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data_show.__init__(self, vals)
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# If no axes are defined, create some.
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if axes==None:
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fig = plt.figure()
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self.axes = fig.add_subplot(111)
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else:
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self.axes = axes
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def modify(self, vals):
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raise NotImplementedError, "this needs to be implemented to use the data_show class"
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def close(self):
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plt.close(self.axes.get_figure())
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class vector_show(data_show):
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class vector_show(matplotlib_show):
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"""
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A base visualization class that just shows a data vector as a plot of
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vector elements alongside their indices.
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"""
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def __init__(self, vals, axes=None):
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data_show.__init__(self, vals, axes)
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matplotlib_show.__init__(self, vals, axes)
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self.handle = self.axes.plot(np.arange(0, len(vals))[:, None], self.vals.T)[0]
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def modify(self, vals):
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@ -42,7 +77,7 @@ class vector_show(data_show):
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self.axes.figure.canvas.draw()
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class lvm(data_show):
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class lvm(matplotlib_show):
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def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0,1]):
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"""Visualize a latent variable model
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@ -54,7 +89,7 @@ class lvm(data_show):
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if vals == None:
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vals = model.X[0]
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data_show.__init__(self, vals, axes=latent_axes)
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matplotlib_show.__init__(self, vals, axes=latent_axes)
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if isinstance(latent_axes,mpl.axes.Axes):
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self.cid = latent_axes.figure.canvas.mpl_connect('button_press_event', self.on_click)
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|
|
@ -204,10 +239,10 @@ class lvm_dimselect(lvm):
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|
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|
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class image_show(data_show):
|
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class image_show(matplotlib_show):
|
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"""Show a data vector as an image."""
|
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def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False, palette=[], presetMean = 0., presetSTD = -1., selectImage=0):
|
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data_show.__init__(self, vals, axes)
|
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matplotlib_show.__init__(self, vals, axes)
|
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self.dimensions = dimensions
|
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self.transpose = transpose
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self.invert = invert
|
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|
|
@ -266,14 +301,72 @@ class image_show(data_show):
|
|||
self.vals = Image.fromarray(self.vals.astype('uint8'))
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self.vals.putpalette(self.palette) # palette is a list, must be loaded before calling this function
|
||||
|
||||
class mocap_data_show(data_show):
|
||||
class mocap_data_show_vpython(vpython_show):
|
||||
"""Base class for visualizing motion capture data using visual module."""
|
||||
|
||||
def __init__(self, vals, scene=None, connect=None, radius=0.1):
|
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vpython_show.__init__(self, vals, scene)
|
||||
self.radius = radius
|
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self.connect = connect
|
||||
self.process_values()
|
||||
self.draw_edges()
|
||||
self.draw_vertices()
|
||||
|
||||
def draw_vertices(self):
|
||||
self.spheres = []
|
||||
for i in range(self.vals.shape[0]):
|
||||
self.spheres.append(visual.sphere(pos=(self.vals[i, 0], self.vals[i, 2], self.vals[i, 1]), radius=self.radius))
|
||||
self.scene.visible=True
|
||||
|
||||
def draw_edges(self):
|
||||
self.rods = []
|
||||
self.line_handle = []
|
||||
if not self.connect==None:
|
||||
self.I, self.J = np.nonzero(self.connect)
|
||||
for i, j in zip(self.I, self.J):
|
||||
pos, axis = self.pos_axis(i, j)
|
||||
self.rods.append(visual.cylinder(pos=pos, axis=axis, radius=self.radius))
|
||||
|
||||
def modify_vertices(self):
|
||||
for i in range(self.vals.shape[0]):
|
||||
self.spheres[i].pos = (self.vals[i, 0], self.vals[i, 2], self.vals[i, 1])
|
||||
|
||||
def modify_edges(self):
|
||||
self.line_handle = []
|
||||
if not self.connect==None:
|
||||
self.I, self.J = np.nonzero(self.connect)
|
||||
for rod, i, j in zip(self.rods, self.I, self.J):
|
||||
rod.pos, rod.axis = self.pos_axis(i, j)
|
||||
|
||||
def pos_axis(self, i, j):
|
||||
pos = []
|
||||
axis = []
|
||||
pos.append(self.vals[i, 0])
|
||||
axis.append(self.vals[j, 0]-self.vals[i,0])
|
||||
pos.append(self.vals[i, 2])
|
||||
axis.append(self.vals[j, 2]-self.vals[i,2])
|
||||
pos.append(self.vals[i, 1])
|
||||
axis.append(self.vals[j, 1]-self.vals[i,1])
|
||||
return pos, axis
|
||||
|
||||
def modify(self, vals):
|
||||
self.vals = vals.copy()
|
||||
self.process_values()
|
||||
self.modify_edges()
|
||||
self.modify_vertices()
|
||||
|
||||
def process_values(self):
|
||||
raise NotImplementedError, "this needs to be implemented to use the data_show class"
|
||||
|
||||
|
||||
class mocap_data_show(matplotlib_show):
|
||||
"""Base class for visualizing motion capture data."""
|
||||
|
||||
def __init__(self, vals, axes=None, connect=None):
|
||||
if axes==None:
|
||||
fig = plt.figure()
|
||||
axes = fig.add_subplot(111, projection='3d')
|
||||
data_show.__init__(self, vals, axes)
|
||||
matplotlib_show.__init__(self, vals, axes)
|
||||
|
||||
self.connect = connect
|
||||
self.process_values()
|
||||
|
|
@ -342,17 +435,17 @@ class mocap_data_show(data_show):
|
|||
self.axes.set_zlim(self.z_lim)
|
||||
|
||||
|
||||
class stick_show(mocap_data_show):
|
||||
class stick_show(mocap_data_show_vpython):
|
||||
"""Show a three dimensional point cloud as a figure. Connect elements of the figure together using the matrix connect."""
|
||||
def __init__(self, vals, axes=None, connect=None):
|
||||
mocap_data_show.__init__(self, vals, axes, connect)
|
||||
def __init__(self, vals, connect=None, scene=None):
|
||||
mocap_data_show_vpython.__init__(self, vals, scene=scene, connect=connect, radius=0.04)
|
||||
|
||||
def process_values(self):
|
||||
self.vals = self.vals.reshape((3, self.vals.shape[1]/3)).T
|
||||
|
||||
class skeleton_show(mocap_data_show):
|
||||
class skeleton_show(mocap_data_show_vpython):
|
||||
"""data_show class for visualizing motion capture data encoded as a skeleton with angles."""
|
||||
def __init__(self, vals, skel, padding=0, axes=None):
|
||||
def __init__(self, vals, skel, scene=None, padding=0):
|
||||
"""data_show class for visualizing motion capture data encoded as a skeleton with angles.
|
||||
:param vals: set of modeled angles to use for printing in the axis when it's first created.
|
||||
:type vals: np.array
|
||||
|
|
@ -364,8 +457,7 @@ class skeleton_show(mocap_data_show):
|
|||
self.skel = skel
|
||||
self.padding = padding
|
||||
connect = skel.connection_matrix()
|
||||
mocap_data_show.__init__(self, vals, axes, connect)
|
||||
|
||||
mocap_data_show_vpython.__init__(self, vals, scene=scene, connect=connect, radius=0.4)
|
||||
def process_values(self):
|
||||
"""Takes a set of angles and converts them to the x,y,z coordinates in the internal prepresentation of the class, ready for plotting.
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue