Rename Clustering to Tree and make it a subclass of Node.

1 files changed, 12 insertions, 13 deletions

diff --git a/demo.py b/demo.py
index eb02202..3c4b933 100644
--- a/demo.py
+++ b/demo.py
@@ -1,34 +1,33 @@
 import os
 import matplotlib.pyplot as plt
 
-from colour import (
-    SpectralShape, COLOURCHECKER_SDS, ILLUMINANT_SDS, sd_to_XYZ)
+from colour import SpectralShape, COLOURCHECKER_SDS, sd_to_XYZ
 
-from otsu2018 import load_Otsu2018_spectra, Clustering
+from otsu2018 import load_Otsu2018_spectra, Tree
 
 
 if __name__ == '__main__':
     print('Loading spectral data...')
-    sds = load_Otsu2018_spectra('CommonData/spectrum_m.csv', every_nth=1)
+    sds = load_Otsu2018_spectra('CommonData/spectrum_m.csv', every_nth=50)
     shape = SpectralShape(380, 730, 10)
 
-    print('Initializing the clustering...')
-    clustering = Clustering(sds, shape)
+    print('Initializing the tree...')
+    tree = Tree(sds, shape)
 
-    print('Clustering...')
-    before = clustering.root.total_reconstruction_error()
-    clustering.optimise()
-    after = clustering.root.total_reconstruction_error()
+    print('Tree...')
+    before = tree.total_reconstruction_error()
+    tree.optimise()
+    after = tree.total_reconstruction_error()
 
     print('Error before: %g' % before)
     print('Error after:  %g' % after)
 
     print('Saving the dataset...')
     os.makedirs('datasets', exist_ok=True)
-    clustering.write_python_dataset('datasets/otsu2018.py')
+    tree.write_python_dataset('datasets/otsu2018.py')
 
     print('Plotting...')
-    clustering.root.visualise()
+    tree.visualise()
 
     plt.figure()
 
@@ -40,7 +39,7 @@ if __name__ == '__main__':
         plt.plot(sd.wavelengths, sd.values, label='Original')
 
         XYZ = sd_to_XYZ(sd) / 100
-        recovered_sd = clustering.reconstruct(XYZ)
+        recovered_sd = tree.reconstruct(XYZ)
         plt.plot(recovered_sd.wavelengths, recovered_sd.values,
                  label='Recovered')