mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-05 14:55:15 +02:00
Further edits on visualization code for faces example.
This commit is contained in:
Neil Lawrence
parent
3fd0672092
commit
fce4dd7fde
9 changed files with 151 additions and 80 deletions
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@ -23,13 +23,13 @@ class model(parameterised):
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self._set_params(self._get_params())
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self.preferred_optimizer = 'tnc'
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def _get_params(self):
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raise NotImplementedError, "this needs to be implemented to utilise the model class"
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raise NotImplementedError, "this needs to be implemented to use the model class"
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def _set_params(self,x):
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raise NotImplementedError, "this needs to be implemented to utilise the model class"
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raise NotImplementedError, "this needs to be implemented to use the model class"
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def log_likelihood(self):
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raise NotImplementedError, "this needs to be implemented to utilise the model class"
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raise NotImplementedError, "this needs to be implemented to use the model class"
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def _log_likelihood_gradients(self):
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raise NotImplementedError, "this needs to be implemented to utilise the model class"
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raise NotImplementedError, "this needs to be implemented to use the model class"
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def set_prior(self,which,what):
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"""
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@ -3,6 +3,8 @@
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import numpy as np
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import pylab as pb
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from matplotlib import pyplot as plt
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import GPy
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default_seed = np.random.seed(123344)
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@ -55,3 +57,51 @@ def GPLVM_oil_100():
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print(m)
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m.plot_latent(labels=data['Y'].argmax(axis=1))
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return m
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def oil_100():
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data = GPy.util.datasets.oil_100()
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m = GPy.models.GPLVM(data['X'], 2)
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_iters=2)
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# plot
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print(m)
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#m.plot_latent(labels=data['Y'].argmax(axis=1))
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return m
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def brendan_faces():
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data = GPy.util.datasets.brendan_faces()
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Y = data['Y'][0:500, :]
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m = GPy.models.GPLVM(Y, 2, init='rand')
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=40)
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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.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
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lvm_visualizer = GPy.util.visualize.lvm(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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return m
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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, init='rand')
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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.stick_show(y[None, :], connect=data['connect'])
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lvm_visualizer = GPy.util.visualize.lvm(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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return m
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@ -73,7 +73,7 @@ def silhouette():
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def coregionalisation_toy2():
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"""
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A simple demonstration of coregionalisation on two sinusoidal functions
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A simple demonstration of coregionalisation on two sinusoidal functions.
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"""
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X1 = np.random.rand(50,1)*8
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X2 = np.random.rand(30,1)*5
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@ -106,7 +106,7 @@ def coregionalisation_toy2():
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def coregionalisation_toy():
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"""
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A simple demonstration of coregionalisation on two sinusoidal functions
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A simple demonstration of coregionalisation on two sinusoidal functions.
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"""
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X1 = np.random.rand(50,1)*8
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X2 = np.random.rand(30,1)*5
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@ -139,7 +139,7 @@ def coregionalisation_toy():
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def coregionalisation_sparse():
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"""
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A simple demonstration of coregionalisation on two sinusoidal functions
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A simple demonstration of coregionalisation on two sinusoidal functions using sparse approximations.
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"""
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X1 = np.random.rand(500,1)*8
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X2 = np.random.rand(300,1)*5
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@ -81,6 +81,7 @@ class GPLVM(GP):
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k = [p for p in self.kern.parts if p.name in ['rbf','linear']]
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if (not len(k)==1) or (not k[0].ARD):
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raise ValueError, "cannot Atomatically determine which dimensions to plot, please pass 'which_indices'"
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input_1, input_2 = self.lengthscale_order()
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k = k[0]
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if k.name=='rbf':
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input_1, input_2 = np.argsort(k.lengthscale)[:2]
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@ -92,7 +93,7 @@ class GPLVM(GP):
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Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
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Xtest_full[:, :2] = Xtest
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mu, var, low, up = self.predict(Xtest_full)
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var = var[:, :2]
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var = var.mean(axis=1) # this was var[:, :2] edit by Neil
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pb.imshow(var.reshape(resolution,resolution).T[::-1,:],extent=[xmin[0],xmax[0],xmin[1],xmax[1]],cmap=pb.cm.binary,interpolation='bilinear')
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@ -122,4 +123,4 @@ class GPLVM(GP):
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pb.xlim(xmin[0],xmax[0])
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pb.ylim(xmin[1],xmax[1])
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return input_1, input_2
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return pb.gca() #input_1, input_2 temporary removal, to return axes.
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@ -10,4 +10,6 @@ import Tango
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import misc
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import warping_functions
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import datasets
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import mocap
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import visualize
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import decorators
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@ -15,12 +15,12 @@ def sample_class(f):
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return c
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def della_gatta_TRP63_gene_expression(gene_number=None):
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matData = scipy.io.loadmat(os.path.join(data_path, 'DellaGattadata.mat'))
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X = np.double(matData['timepoints'])
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mat_data = scipy.io.loadmat(os.path.join(data_path, 'DellaGattadata.mat'))
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X = np.double(mat_data['timepoints'])
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if gene_number == None:
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Y = matData['exprs_tp53_RMA']
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Y = mat_data['exprs_tp53_RMA']
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else:
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Y = matData['exprs_tp53_RMA'][:, gene_number]
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Y = mat_data['exprs_tp53_RMA'][:, gene_number]
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if len(Y.shape) == 1:
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Y = Y[:, None]
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return {'X': X, 'Y': Y, 'info': "The full gene expression data set from della Gatta et al (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2413161/) processed by RMA."}
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@ -60,28 +60,42 @@ def pumadyn(seed=default_seed):
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return {'X': X, 'Y': Y, 'Xtest': Xtest, 'Ytest': Ytest, 'info': "The puma robot arm data with 32 inputs. This data is the non linear case with medium noise (pumadyn-32nm). For training 7,168 examples are sampled without replacement."}
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def brendan_faces():
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mat_data = scipy.io.loadmat(os.path.join(data_path, 'frey_rawface.mat'))
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Y = mat_data['ff'].T
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return {'Y': Y, 'info': "Face data made available by Brendan Frey"}
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def silhouette():
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# Ankur Agarwal and Bill Trigg's silhoutte data.
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matData = scipy.io.loadmat(os.path.join(data_path, 'mocap', 'ankur', 'ankurDataPoseSilhouette.mat'))
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inMean = np.mean(matData['Y'])
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inScales = np.sqrt(np.var(matData['Y']))
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X = matData['Y'] - inMean
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mat_data = scipy.io.loadmat(os.path.join(data_path, 'mocap', 'ankur', 'ankurDataPoseSilhouette.mat'))
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inMean = np.mean(mat_data['Y'])
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inScales = np.sqrt(np.var(mat_data['Y']))
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X = mat_data['Y'] - inMean
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X = X/inScales
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Xtest = matData['Y_test'] - inMean
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Xtest = mat_data['Y_test'] - inMean
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Xtest = Xtest/inScales
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Y = matData['Z']
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Ytest = matData['Z_test']
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Y = mat_data['Z']
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Ytest = mat_data['Z_test']
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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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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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def swiss_roll_1000():
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matData = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data'))
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Y = matData['X_data'][:, 0:1000].transpose()
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mat_data = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data'))
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Y = mat_data['X_data'][:, 0:1000].transpose()
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return {'Y': Y, 'info': "Subsample of the swiss roll data extracting only the first 1000 values."}
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def swiss_roll():
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matData = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data.mat'))
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Y = matData['X_data'][:, 0:3000].transpose()
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mat_data = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data.mat'))
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Y = mat_data['X_data'][:, 0:3000].transpose()
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return {'Y': Y, 'info': "The first 3,000 points from the swiss roll data of Tennenbaum, de Silva and Langford (2001)."}
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def toy_rbf_1d(seed=default_seed):
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@ -202,3 +216,4 @@ def creep_data():
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features.extend(range(2, 31))
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X = all_data[:,features].copy()
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return {'X': X, 'y' : y}
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@ -145,7 +145,8 @@ def PCA(Y, Q):
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"""
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if not np.allclose(Y.mean(axis=0), 0.0):
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print "Y is not zero mean, centering it locally (GPy.util.linalg.PCA)"
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Y -= Y.mean(axis=0)
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Y -= Y.mean(axis=0)
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Z = linalg.svd(Y, full_matrices = False)
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[X, W] = [Z[0][:,0:Q], np.dot(np.diag(Z[1]), Z[2]).T[:,0:Q]]
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@ -8,12 +8,17 @@ def load_text_data(dataset, directory, centre=True):
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# Remove markers where there is a NaN
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present_index = [i for i in range(points[0].shape[1]) if not (np.any(np.isnan(points[0][:, i])) or np.any(np.isnan(points[0][:, i])) or np.any(np.isnan(points[0][:, i])))]
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point_names = point_names[present_index]
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for i in range(3):
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points[i] = points[i][:, present_index]
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if centre:
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points[i] = (points[i].T - points[i].mean(axis=1)).T
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# Concatanate the X, Y and Z markers together
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Y = np.concatenate((points[0][:, present_index], points[1][:, present_index], points[2][:, present_index]), axis=1)
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if centre:
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Y = Y - Y.mean(axis=0)
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Y = np.concatenate((points[0], points[1], points[2]), axis=1)
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Y = Y/400.
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return Y
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connect = read_connections(os.path.join(directory, 'connections.txt'), point_names)
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return Y, connect
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def parse_text(file_name):
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"""Parse data from Ohio State University text mocap files (http://accad.osu.edu/research/mocap/mocap_data.htm)."""
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@ -23,7 +28,7 @@ def parse_text(file_name):
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point_names = np.array(fid.readline().split())[2:-1:3]
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fid.close()
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for i in range(len(point_names)):
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point_names[i] = point_names[i][0:-3]
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point_names[i] = point_names[i][0:-2]
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# Read the matrix data
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S = np.loadtxt(file_name, skiprows=1)
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@ -42,37 +47,28 @@ def parse_text(file_name):
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return points, point_names, times
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#def read_connections():
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def read_connections(file_name, point_names):
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"""Read a file detailing which markers should be connected to which for motion capture data."""
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connections = []
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fid = open(file_name, 'r')
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line=fid.readline()
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while(line):
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connections.append(np.array(line.split(',')))
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connections[-1][0] = connections[-1][0].strip()
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connections[-1][1] = connections[-1][1].strip()
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line = fid.readline()
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connect = np.zeros((len(point_names), len(point_names)),dtype=bool)
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for i in range(len(point_names)):
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for j in range(len(point_names)):
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for k in range(len(connections)):
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if connections[k][0] == point_names[i] and connections[k][1] == point_names[j]:
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connect[i,j]=True
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connect[j,i]=True
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break
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return connect
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# fid = fopen(fileName);
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# i = 1;
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# rem = fgets(fid);
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# while(rem ~= -1)
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# [token, rem] = strtok(rem, ',');
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# connections{i, 1} = fliplr(deblank(fliplr(deblank(token))));
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# [token, rem] = strtok(rem, ',');
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# connections{i, 2} = fliplr(deblank(fliplr(deblank(token))));
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# i = i + 1;
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# rem = fgets(fid);
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# end
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# connect = zeros(length(pointNames));
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# fclose(fid);
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# for i = 1:size(connections, 1);
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# for j = 1:length(pointNames)
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# if strcmp(pointNames{j}, connections{i, 1}) | ...
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# strcmp(pointNames{j}, connections{i, 2})
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# for k = 1:length(pointNames)
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# if k == j
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# break
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# end
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# if strcmp(pointNames{k}, connections{i, 1}) | ...
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# strcmp(pointNames{k}, connections{i, 2})
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# connect(j, k) = 1;
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# end
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# end
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# end
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# end
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# end
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# connect = sparse(connect);
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@ -4,18 +4,18 @@ import GPy
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import numpy as np
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class lvm:
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def __init__(self, model, data_visualize, ax):
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self.cid = ax.figure.canvas.mpl_connect('button_press_event', self.on_click)
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self.cid = ax.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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def __init__(self, model, data_visualize, latent_axis):
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self.cid = latent_axis.figure.canvas.mpl_connect('button_press_event', self.on_click)
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self.cid = latent_axis.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
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self.data_visualize = data_visualize
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self.model = model
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self.ax = ax
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self.latent_axis = latent_axis
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self.called = False
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self.move_on = False
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def on_click(self, event):
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print 'click', event.xdata, event.ydata
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if event.inaxes!=self.ax: return
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if event.inaxes!=self.latent_axis: return
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self.move_on = not self.move_on
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print
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if self.called:
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@ -26,10 +26,10 @@ class lvm:
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else:
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self.xs = [event.xdata]
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self.ys = [event.ydata]
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self.line, = ax.plot(event.xdata, event.ydata)
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self.line, = self.latent_axis.plot(event.xdata, event.ydata)
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self.called = True
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def on_move(self, event):
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if event.inaxes!=self.ax: return
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if event.inaxes!=self.latent_axis: return
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if self.called and self.move_on:
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# Call modify code on move
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#print 'move', event.xdata, event.ydata
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@ -68,28 +68,34 @@ class vector_show(data_show):
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class image_show(data_show):
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"""Show a data vector as an image."""
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def __init__(self, vals, axis=None, dimensions=(16,16), transpose=False, invert=False):
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def __init__(self, vals, axis=None, dimensions=(16,16), transpose=False, invert=False, scale=False):
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data_show.__init__(self, vals, axis)
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self.dimensions = dimensions
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self.fig_display = plt.figure()
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self.set_image(vals)
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self.handle = plt.imshow(self.vals)
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self.transpose = transpose
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self.invert = invert
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self.scale = scale
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self.set_image(vals/255.)
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self.handle = self.axis.imshow(self.vals, interpolation='nearest')
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plt.show()
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def modify(self, vals):
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self.set_image(vals)
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self.set_image(vals/255.)
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#self.handle.remove()
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#self.handle = self.axis.imshow(self.vals)
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self.handle.set_array(self.vals)
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self.axis.figure.canvas.draw()
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#self.axis.figure.canvas.draw()
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plt.show()
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def set_image(self, vals):
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self.vals = np.reshape(vals, self.dimensions)
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self.vals = np.reshape(vals, self.dimensions, order='F')
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if self.transpose:
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self.vals = self.vals.T
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if self.invert:
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self.vals = -self.vals
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if not self.scale:
|
||||
self.vals = self.vals
|
||||
#if self.invert:
|
||||
# self.vals = -self.vals
|
||||
|
||||
class stick_show(data_show):
|
||||
class stick_show(data_show):
|
||||
"""Show a three dimensional point cloud as a figure. Connect elements of the figure together using the matrix connect."""
|
||||
|
||||
def __init__(self, vals, axis=None, connect=None):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue