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Further edits on visualization code for faces example.

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Neil Lawrence 2013-04-02 02:20:53 +02:00
parent 3fd0672092
commit fce4dd7fde
9 changed files with 151 additions and 80 deletions
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45
GPy/util/datasets.py
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@ -15,12 +15,12 @@ def sample_class(f):
return c
def della_gatta_TRP63_gene_expression(gene_number=None):
matData = scipy.io.loadmat(os.path.join(data_path, 'DellaGattadata.mat'))
X = np.double(matData['timepoints'])
mat_data = scipy.io.loadmat(os.path.join(data_path, 'DellaGattadata.mat'))
X = np.double(mat_data['timepoints'])
if gene_number == None:
Y = matData['exprs_tp53_RMA']
Y = mat_data['exprs_tp53_RMA']
else:
Y = matData['exprs_tp53_RMA'][:, gene_number]
Y = mat_data['exprs_tp53_RMA'][:, gene_number]
if len(Y.shape) == 1:
Y = Y[:, None]
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."}
@ -60,28 +60,42 @@ def pumadyn(seed=default_seed):
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."}
def brendan_faces():
mat_data = scipy.io.loadmat(os.path.join(data_path, 'frey_rawface.mat'))
Y = mat_data['ff'].T
return {'Y': Y, 'info': "Face data made available by Brendan Frey"}
def silhouette():
# Ankur Agarwal and Bill Trigg's silhoutte data.
matData = scipy.io.loadmat(os.path.join(data_path, 'mocap', 'ankur', 'ankurDataPoseSilhouette.mat'))
inMean = np.mean(matData['Y'])
inScales = np.sqrt(np.var(matData['Y']))
X = matData['Y'] - inMean
mat_data = scipy.io.loadmat(os.path.join(data_path, 'mocap', 'ankur', 'ankurDataPoseSilhouette.mat'))
inMean = np.mean(mat_data['Y'])
inScales = np.sqrt(np.var(mat_data['Y']))
X = mat_data['Y'] - inMean
X = X/inScales
Xtest = matData['Y_test'] - inMean
Xtest = mat_data['Y_test'] - inMean
Xtest = Xtest/inScales
Y = matData['Z']
Ytest = matData['Z_test']
Y = mat_data['Z']
Ytest = mat_data['Z_test']
return {'X': X, 'Y': Y, 'Xtest': Xtest, 'Ytest': Ytest, 'info': "Artificial silhouette simulation data developed from Agarwal and Triggs (2004)."}
def stick():
Y, connect = GPy.util.mocap.load_text_data('run1', data_path)
Y = Y[0:-1:4, :]
lbls = 'connect'
return {'Y': Y, 'connect' : connect, 'info': "Stick man data from Ohio."}
def swiss_roll_1000():
matData = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data'))
Y = matData['X_data'][:, 0:1000].transpose()
mat_data = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data'))
Y = mat_data['X_data'][:, 0:1000].transpose()
return {'Y': Y, 'info': "Subsample of the swiss roll data extracting only the first 1000 values."}
def swiss_roll():
matData = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data.mat'))
Y = matData['X_data'][:, 0:3000].transpose()
mat_data = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data.mat'))
Y = mat_data['X_data'][:, 0:3000].transpose()
return {'Y': Y, 'info': "The first 3,000 points from the swiss roll data of Tennenbaum, de Silva and Langford (2001)."}
def toy_rbf_1d(seed=default_seed):
@ -202,3 +216,4 @@ def creep_data():
features.extend(range(2, 31))
X = all_data[:,features].copy()
return {'X': X, 'y' : y}