Drop wpca as a dependency in favor of a simply NumPy-based replacement.

1 files changed, 10 insertions, 6 deletions

diff --git a/otsu2018.py b/otsu2018.py
index 6533822..713da1d 100644
--- a/otsu2018.py
+++ b/otsu2018.py
@@ -1,6 +1,5 @@
 import numpy as np
 import matplotlib.pyplot as plt
-import sklearn.decomposition
 
 from colour import (SpectralDistribution, STANDARD_OBSERVER_CMFS,
                     ILLUMINANT_SDS, sd_to_XYZ, XYZ_to_xy)
@@ -207,10 +206,14 @@ class Node:
         if not self.leaf:
             raise RuntimeError('Node.PCA called for a node that is not a leaf')
 
-        pca = sklearn.decomposition.PCA(3)
-        pca.fit(self.colours.reflectances)
-        self.basis_functions = pca.components_
-        self.mean = pca.mean_
+        # https://dev.to/akaame/implementing-simple-pca-using-numpy-3k0a
+        self.mean = np.mean(self.colours.reflectances, axis=0)
+        data = self.colours.reflectances - self.mean
+        cov = np.cov(data.T) / data.shape[0]
+        v, w = np.linalg.eig(cov)
+        idx = v.argsort()[::-1]
+        w = w[:,idx]
+        self.basis_functions = np.real(w[:, :3].T)
 
         # TODO: better names
         M = np.empty((3, 3))
@@ -241,6 +244,7 @@ class Node:
 
         weights = np.dot(self.M_inverse, XYZ - self.XYZ_mu)
         reflectance = np.dot(weights, self.basis_functions) + self.mean
+        reflectance = np.clip(reflectance, 0, 1)
         return SpectralDistribution(reflectance, self.clustering.wl)
 
     def reconstruction_error(self):
@@ -313,7 +317,7 @@ class Node:
         axis = self.colours.xy[i, x_or_y]
         partition = self.colours.partition(x_or_y, axis)
 
-        if len(partition[0]) <= 5 or len(partition[1]) <= 5:
+        if len(partition[0]) < 3 or len(partition[1]) < 3:
             raise ClusteringError('partition created parts that are too small')
 
         lesser = Node(self.clustering, partition[0])