massive merge of the debug branch

GPy/examples/oil_flow_demo.py

@@ -0,0 +1,55 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import cPickle as pickle
+import numpy as np
+import pylab as pb
+import GPy
+import pylab as plt
+np.random.seed(1)
+
+def plot_oil(X, theta, labels, label):
+    plt.figure()
+    X = X[:,np.argsort(theta)[:2]]
+    flow_type = (X[labels[:,0]==1])
+    plt.plot(flow_type[:,0], flow_type[:,1], 'rx')
+    flow_type = (X[labels[:,1]==1])
+    plt.plot(flow_type[:,0], flow_type[:,1], 'gx')
+    flow_type = (X[labels[:,2]==1])
+    plt.plot(flow_type[:,0], flow_type[:,1], 'bx')
+    plt.title(label)
+
+data = pickle.load(open('../../../GPy_assembla/datasets/oil_flow_3classes.pickle', 'r'))
+
+Y = data['DataTrn']
+N, D = Y.shape
+selected = np.random.permutation(N)#[:200]
+labels = data['DataTrnLbls'][selected]
+Y = Y[selected]
+N, D = Y.shape
+Y -= Y.mean(axis=0)
+#Y /= Y.std(axis=0)
+
+Q = 10
+k = GPy.kern.rbf_ARD(Q) + GPy.kern.white(Q)
+m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M = 12)
+m.constrain_positive('(rbf|bias|S|white|noise)')
+# m.constrain_bounded('white', 1e-6, 100.0)
+# m.constrain_bounded('noise', 1e-4, 1000.0)
+
+plot_oil(m.X, np.array([1,1]), labels, 'PCA initialization')
+# m.optimize(messages = True)
+m.optimize('tnc', messages = True)
+plot_oil(m.X, m.kern.parts[0].lengthscales, labels, 'B-GPLVM')
+# pb.figure()
+# m.plot()
+# pb.title('PCA initialisation')
+# pb.figure()
+# m.optimize(messages = 1)
+# m.plot()
+# pb.title('After optimisation')
+# m = GPy.models.GPLVM(Y, Q)
+# m.constrain_positive('(white|rbf|bias|noise)')
+# m.optimize()
+# plot_oil(m.X, np.array([1,1]), labels, 'GPLVM')

GPy/examples/sparse_GPLVM_demo.py

@@ -9,19 +9,17 @@ np.random.seed(1)
 print "sparse GPLVM with RBF kernel"
 
 N = 100
-M = 4
-Q = 2
+M = 8
+Q = 1
 D = 2
 #generate GPLVM-like data
 X = np.random.rand(N, Q)
-k = GPy.kern.rbf(Q,1.,2*np.ones((1,))) + GPy.kern.white(Q, 0.00001)
+k = GPy.kern.rbf(Q, 1.0, 2.0) + GPy.kern.white(Q, 0.00001)
 K = k.K(X)
 Y = np.random.multivariate_normal(np.zeros(N),K,D).T
 
 m = GPy.models.sparse_GPLVM(Y, Q, M=M)
-m.constrain_positive('(rbf|bias|noise)')
-m.constrain_bounded('white', 1e-3, 0.1)
-# m.plot()
+m.constrain_positive('(rbf|bias|noise|white)')
 
 pb.figure()
 m.plot()

GPy/examples/sparse_GP_regression_demo.py

@@ -11,7 +11,7 @@ import numpy as np
 import GPy
 np.random.seed(2)
 pb.ion()
-N = 500
+N = 400
 M = 5
 
 ######################################
@@ -27,20 +27,13 @@ noise = GPy.kern.white(1)
 kernel = rbf + noise
 
 # create simple GP model
-m1 = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
+m = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
 
-# contrain all parameters to be positive
-m1.constrain_positive('(variance|lengthscale|precision)')
-#m1.constrain_positive('(variance|lengthscale)')
-#m1.constrain_fixed('prec',10.)
+m.constrain_positive('(variance|lengthscale|precision)')
 
-
-#check gradient FIXME unit test please
-m1.checkgrad()
-# optimize and plot
-m1.optimize('tnc', messages = 1)
-m1.plot()
-# print(m1)
+m.checkgrad(verbose=1)
+m.optimize('tnc', messages = 1)
+m.plot()
 
 ######################################
 ## 2 dimensional example