[pca] pca -> PCA

GPy/util/initialization.py

@@ -5,12 +5,12 @@ Created on 24 Feb 2014
 '''
 
 import numpy as np
-from GPy.util.pca import pca
+from GPy.util.pca import PCA
 
 def initialize_latent(init, input_dim, Y):
     Xr = np.asfortranarray(np.random.randn(Y.shape[0], input_dim))
     if init == 'PCA':
-        p = pca(Y)
+        p = PCA(Y)
         PC = p.project(Y, min(input_dim, Y.shape[1]))
         Xr[:PC.shape[0], :PC.shape[1]] = PC
         var = .1*p.fracs[:input_dim]

GPy/util/pca.py

@@ -14,9 +14,9 @@ from numpy.linalg.linalg import LinAlgError
 from operator import setitem
 import itertools
 
-class pca(object):
+class PCA(object):
     """
-    pca module with automatic primal/dual determination.
+    PCA module with automatic primal/dual determination.
     """
     def __init__(self, X):
         self.mu = None
@@ -39,7 +39,7 @@ class pca(object):
 
     def center(self, X):
         """
-        Center `X` in pca space.
+        Center `X` in PCA space.
         """
         X = X.copy()
         inan = numpy.isnan(X)
@@ -66,7 +66,7 @@ class pca(object):
 
     def project(self, X, Q=None):
         """
-        Project X into pca space, defined by the Q highest eigenvalues.
+        Project X into PCA space, defined by the Q highest eigenvalues.
         Y = X dot V
         """
         if Q is None:
@@ -80,13 +80,16 @@ class pca(object):
         """
         Plot fractions of Eigenvalues sorted in descending order.
         """
+        from GPy.plotting.matplot_dep import Tango
+        Tango.reset()
+        col = Tango.nextMedium()
         if ax is None:
             fig = pylab.figure(fignum)
             ax = fig.add_subplot(111)
         if Q is None:
             Q = self.Q
         ticks = numpy.arange(Q)
-        bar = ax.bar(ticks - .4, self.fracs[:Q])
+        bar = ax.bar(ticks - .4, self.fracs[:Q], color=col)
         ax.set_xticks(ticks, map(lambda x: r"${}$".format(x), ticks + 1))
         ax.set_ylabel("Eigenvalue fraction")
         ax.set_xlabel("PC")