principal_components now also accepts a GaussianStats object.

Author: tboggs

spectral/algorithms/algorithms.py

@@ -448,7 +448,7 @@ def principal_components(image):
 
     Arguments:
 
-        `image` (ndarray or :class:`spectral.Image`):
+        `image` (ndarray, :class:`spectral.Image`, :class:`GaussianStats`):
 
             An `MxNxB` image
 
@@ -478,9 +478,10 @@ def principal_components(image):
     '''
     from numpy import sqrt, sum
 
-    (M, N, B) = image.shape
-
-    stats = calc_stats(image)
+    if isinstance(image, GaussianStats):
+        stats = image
+    else:
+        stats = calc_stats(image)
 
     (L, V) = numpy.linalg.eig(stats.cov)
 

spectral/tests/dimensionality.py

@@ -88,6 +88,17 @@ def test_ppi_centered(self):
         p2 = spy.ppi(data_centered, 4)
         np.all(p == p2)
 
+    def test_pca_runs(self):
+        '''Should be able to compute PCs and transform data.'''
+        data = self.data
+        xdata = spy.principal_components(data).transform(data)
+
+    def test_pca_runs_from_stats(self):
+        '''Should be able to pass image stats to PCA function.'''
+        data = self.data
+        stats = spy.calc_stats(data)
+        xdata = spy.principal_components(stats).transform(data)
+
 def run():
     print('\n' + '-' * 72)
     print('Running dimensionality tests.')