first trivial model touches

GPy/examples/dimensionality_reduction.py

@@ -6,26 +6,27 @@ import pylab as pb
 from matplotlib import pyplot as plt
 
 import GPy
+from GPy.models.mrd import MRD
 
 default_seed = np.random.seed(123344)
 
-def BGPLVM(seed = default_seed):
+def BGPLVM(seed=default_seed):
     N = 10
     M = 3
     Q = 2
     D = 4
-    #generate GPLVM-like data
+    # generate GPLVM-like data
     X = np.random.rand(N, Q)
     k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
     K = k.K(X)
-    Y = np.random.multivariate_normal(np.zeros(N),K,D).T
+    Y = np.random.multivariate_normal(np.zeros(N), K, D).T
 
-    k = GPy.kern.linear(Q, ARD = True) + GPy.kern.white(Q)
+    k = GPy.kern.linear(Q, ARD=True) + GPy.kern.white(Q)
     # k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
     # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
     # k = GPy.kern.rbf(Q, ARD = False)  + GPy.kern.white(Q, 0.00001)
 
-    m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k,  M=M)
+    m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
     m.constrain_positive('(rbf|bias|noise|white|S)')
     # m.constrain_fixed('S', 1)
 
@@ -38,44 +39,44 @@ def BGPLVM(seed = default_seed):
     # pb.title('After optimisation')
     m.ensure_default_constraints()
     m.randomize()
-    m.checkgrad(verbose = 1)
+    m.checkgrad(verbose=1)
 
     return m
 
-def GPLVM_oil_100(optimize=True,M=15):
+def GPLVM_oil_100(optimize=True, M=15):
     data = GPy.util.datasets.oil_100()
 
     # create simple GP model
-    kernel = GPy.kern.rbf(6, ARD = True) + GPy.kern.bias(6)
+    kernel = GPy.kern.rbf(6, ARD=True) + GPy.kern.bias(6)
     m = GPy.models.GPLVM(data['X'], 6, kernel=kernel, M=M)
     m.data_labels = data['Y'].argmax(axis=1)
 
     # optimize
     m.ensure_default_constraints()
     if optimize:
-        m.optimize('scg',messages=1)
+        m.optimize('scg', messages=1)
 
     # plot
     print(m)
     m.plot_latent(labels=m.data_labels)
     return m
 
-def BGPLVM_oil(optimize=True,N=100,Q=10,M=15):
+def BGPLVM_oil(optimize=True, N=100, Q=10, M=15):
     data = GPy.util.datasets.oil()
 
     # create simple GP model
-    kernel = GPy.kern.rbf(Q, ARD = True) + GPy.kern.bias(Q) + GPy.kern.white(Q,0.001)
+    kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.001)
-    m = GPy.models.Bayesian_GPLVM(data['X'][:N], Q, kernel = kernel,M=M)
+    m = GPy.models.Bayesian_GPLVM(data['X'][:N], Q, kernel=kernel, M=M)
     m.data_labels = data['Y'][:N].argmax(axis=1)
 
     # optimize
     if optimize:
-        m.constrain_fixed('noise',0.05)
+        m.constrain_fixed('noise', 0.05)
         m.ensure_default_constraints()
-        m.optimize('scg',messages=1)
+        m.optimize('scg', messages=1)
         m.unconstrain('noise')
         m.constrain_positive('noise')
-        m.optimize('scg',messages=1)
+        m.optimize('scg', messages=1)
     else:
         m.ensure_default_constraints()
 
@@ -83,7 +84,7 @@ def BGPLVM_oil(optimize=True,N=100,Q=10,M=15):
     print(m)
     m.plot_latent(labels=m.data_labels)
     pb.figure()
-    pb.bar(np.arange(m.kern.D),1./m.input_sensitivity())
+    pb.bar(np.arange(m.kern.D), 1. / m.input_sensitivity())
     return m
 
 def oil_100():
@@ -96,7 +97,37 @@ def oil_100():
 
     # plot
     print(m)
-    #m.plot_latent(labels=data['Y'].argmax(axis=1))
+    # 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():
@@ -109,7 +140,7 @@ def brendan_faces():
     m.optimize(messages=1, max_f_eval=10000)
 
     ax = m.plot_latent()
-    y = m.likelihood.Y[0,:]
+    y = m.likelihood.Y[0, :]
     data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
     lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
     raw_input('Press enter to finish')
@@ -120,13 +151,13 @@ def brendan_faces():
 def stick():
     data = GPy.util.datasets.stick()
     m = GPy.models.GPLVM(data['Y'], 2)
-    
+
     # optimize
     m.ensure_default_constraints()
     m.optimize(messages=1, max_f_eval=10000)
 
     ax = m.plot_latent()
-    y = m.likelihood.Y[0,:]
+    y = m.likelihood.Y[0, :]
     data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
     lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
     raw_input('Press enter to finish')

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