Changed all M's for num_inducing

GPy/models/bayesian_gplvm.py

@@ -23,7 +23,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
     :type init: 'PCA'|'random'
 
     """
-    def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', M=10,
+    def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
                  Z=None, kernel=None, oldpsave=10, _debug=False,
                  **kwargs):
         if type(likelihood_or_Y) is np.ndarray:
@@ -39,7 +39,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
             X_variance = np.clip((np.ones_like(X) * 0.5) + .01 * np.random.randn(*X.shape), 0.001, 1)
 
         if Z is None:
-            Z = np.random.permutation(X.copy())[:M]
+            Z = np.random.permutation(X.copy())[:num_inducing]
         assert Z.shape[1] == X.shape[1]
 
         if kernel is None:

GPy/models/mrd.py

@@ -33,7 +33,7 @@ class MRD(model):
     :param X_variance:
         Initial latent space variance
     :param init: [cooncat|single|random]
-        initialization method to use: 
+        initialization method to use:
             *concat: PCA on concatenated outputs
             *single: PCA on each output
             *random: random
@@ -44,7 +44,7 @@ class MRD(model):
     :param kernels: list of kernels or kernel shared for all BGPLVMS
     :type kernels: [GPy.kern.kern] | GPy.kern.kern | None (default)
     """
-    def __init__(self, likelihood_or_Y_list, input_dim, M=10, names=None,
+    def __init__(self, likelihood_or_Y_list, input_dim, num_inducing=10, names=None,
                  kernels=None, initx='PCA',
                  initz='permute', _debug=False, **kw):
         if names is None:
@@ -61,13 +61,13 @@ class MRD(model):
         assert not ('kernel' in kw), "pass kernels through `kernels` argument"
 
         self.input_dim = input_dim
-        self.num_inducing = M
+        self.num_inducing = num_inducing
         self._debug = _debug
 
         self._init = True
         X = self._init_X(initx, likelihood_or_Y_list)
         Z = self._init_Z(initz, X)
-        self.bgplvms = [BayesianGPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, M=self.num_inducing, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
+        self.bgplvms = [BayesianGPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, num_inducing=self.num_inducing, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
         del self._init
 
         self.gref = self.bgplvms[0]

GPy/models/sparse_gp_classification.py

@@ -26,7 +26,7 @@ class SparseGPClassification(SparseGP):
 
     """
 
-    def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10):
+    def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
         if kernel is None:
             kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
 
@@ -38,7 +38,7 @@ class SparseGPClassification(SparseGP):
                 raise Warning, 'likelihood.data and Y are different.'
 
         if Z is None:
-            i = np.random.permutation(X.shape[0])[:M]
+            i = np.random.permutation(X.shape[0])[:num_inducing]
             Z = X[i].copy()
         else:
             assert Z.shape[1]==X.shape[1]

GPy/models/sparse_gp_regression.py

@@ -26,14 +26,14 @@ class SparseGPRegression(SparseGP):
 
     """
 
-    def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10, X_variance=None):
+    def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10, X_variance=None):
         # kern defaults to rbf (plus white for stability)
         if kernel is None:
             kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1], 1e-3)
 
         # Z defaults to a subset of the data
         if Z is None:
-            i = np.random.permutation(X.shape[0])[:M]
+            i = np.random.permutation(X.shape[0])[:num_inducing]
             Z = X[i].copy()
         else:
             assert Z.shape[1] == X.shape[1]

GPy/models/sparse_gplvm.py

@@ -23,9 +23,9 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
     :type init: 'PCA'|'random'
 
     """
-    def __init__(self, Y, input_dim, kernel=None, init='PCA', M=10):
+    def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10):
         X = self.initialise_latent(init, input_dim, Y)
-        SparseGPRegression.__init__(self, X, Y, kernel=kernel, M=M)
+        SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
 
     def _get_param_names(self):
         return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])