Remove the dependency on matplotlib

GPy/models/bcgplvm.py

@@ -3,8 +3,6 @@
 
 
 import numpy as np
-import pylab as pb
-import sys, pdb
 from ..core import GP
 from ..models import GPLVM
 from ..mappings import *

GPy/models/gplvm.py

@@ -3,7 +3,6 @@
 
 
 import numpy as np
-import pylab as pb
 from .. import kern
 from ..core import GP, Param
 from ..likelihoods import Gaussian
@@ -55,7 +54,7 @@ class GPLVM(GP):
         #J = np.zeros((X.shape[0],X.shape[1],self.output_dim))
         J = self.jacobian(X)
         for i in range(X.shape[0]):
-            target[i]=np.sqrt(pb.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
+            target[i]=np.sqrt(np.linalg.det(np.dot(J[i,:,:],np.transpose(J[i,:,:]))))
         return target
 
     def plot(self):
@@ -63,6 +62,7 @@ class GPLVM(GP):
         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, _ = self.predict(Xnew)
+        import pylab as pb
         pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
 
     def plot_latent(self, labels=None, which_indices=None,

GPy/models/sparse_gplvm.py

@@ -3,13 +3,8 @@
 
 
 import numpy as np
-import pylab as pb
-import sys, pdb
+import sys
 from GPy.models.sparse_gp_regression import SparseGPRegression
-from GPy.models.gplvm import GPLVM
-# from .. import kern
-# from ..core import model
-# from ..util.linalg import pdinv, PCA
 
 class SparseGPLVM(SparseGPRegression):
     """