first trivial model touches

GPy/examples/dimensionality_reduction.py

@@ -6,6 +6,7 @@ import pylab as pb
 from matplotlib import pyplot as plt
 
 import GPy
+from GPy.models.mrd import MRD
 
 default_seed = np.random.seed(123344)
 
@@ -99,6 +100,36 @@ def oil_100():
     # m.plot_latent(labels=data['Y'].argmax(axis=1))
     return m
 
+def mrd_simulation():
+    num = 2
+    ard1 = np.array([1., 1, 0, 0], dtype=float)
+    ard2 = np.array([0., 1, 1, 0], dtype=float)
+    ard1[ard1 == 0] = 1E+10
+    ard2[ard2 == 0] = 1E+10
+
+    make_params = lambda ard: np.hstack([[1], ard, [1, .3]])
+
+    D1, D2, N, M, Q = 50, 100, 150, 15, 4
+    X = np.random.randn(N, Q)
+
+    k = GPy.kern.rbf(Q, ARD=True, lengthscale=ard1) + GPy.kern.bias(Q, 1) + GPy.kern.white(Q, 0.0001)
+    Y1 = np.random.multivariate_normal(np.zeros(N), k.K(X), D1).T
+    Y1 -= Y1.mean(0)
+
+    k = GPy.kern.rbf(Q, ARD=True, lengthscale=ard2) + GPy.kern.bias(Q, 1) + GPy.kern.white(Q, 0.0001)
+    Y2 = np.random.multivariate_normal(np.zeros(N), k.K(X), D2).T
+    Y2 -= Y2.mean(0)
+
+    k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q, 1.0)
+
+    m = MRD(Y1, Y2, Q=Q, M=M, kernel=k, _debug=False)
+    m.ensure_default_constraints()
+
+    m.optimize(messages=1, max_f_eval=5000)
+
+    import ipdb;ipdb.set_trace()
+    return m
+
 def brendan_faces():
     data = GPy.util.datasets.brendan_faces()
     Y = data['Y'][0:-1:10, :]

GPy/models/GPLVM.py

@@ -55,7 +55,7 @@ class GPLVM(GP):
 
     def plot(self):
         assert self.likelihood.Y.shape[1]==2
-        pb.scatter(self.likelihood.Y[:,0],self.likelihood.Y[:,1],40,self.X[:,0].copy(),linewidth=0,cmap=pb.cm.jet)
+        pb.scatter(self.likelihood.Y[:,0],self.likelihood.Y[:,1],40,self.X[:,0].copy(),linewidth=0,cmap=pb.cm.jet)  # @UndefinedVariable
         Xnew = np.linspace(self.X.min(),self.X.max(),200)[:,None]
         mu, var, upper, lower = self.predict(Xnew)
         pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
@@ -90,7 +90,7 @@ class GPLVM(GP):
         Xtest_full[:, :2] = Xtest
         mu, var, low, up = self.predict(Xtest_full)
         var = var.mean(axis=1) # this was var[:, :2] edit by Neil
-        pb.imshow(var.reshape(resolution,resolution).T[::-1,:],extent=[xmin[0],xmax[0],xmin[1],xmax[1]],cmap=pb.cm.binary,interpolation='bilinear')
+        pb.imshow(var.reshape(resolution,resolution).T[::-1,:],extent=[xmin[0],xmax[0],xmin[1],xmax[1]],cmap=pb.cm.binary,interpolation='bilinear')  # @UndefinedVariable
 
 
         for i,ul in enumerate(np.unique(labels)):

GPy/models/__init__.py

@@ -11,4 +11,7 @@ from warped_GP import warpedGP
 from sparse_GPLVM import sparse_GPLVM
 from uncollapsed_sparse_GP import uncollapsed_sparse_GP
 from Bayesian_GPLVM import Bayesian_GPLVM
+import mrd
+MRD = mrd.MRD
+del mrd
 from generalized_FITC import generalized_FITC