Perform pca

src/main.py

@@ -62,8 +62,9 @@ if __name__ == '__main__':
             imm_points.append(imm.get_points())
             # imm.show()
 
+        imm_points = np.array(imm_points)
         mean_values = build_mean_aam(np.array(imm_points))
-        pca(imm_points, mean_values)
+        V, S = pca(imm_points, mean_values)
 
         # show immpoints
         imm = IMMPoints(points=mean_values)

src/pca.py

-def pca(data, mean_values):
-    print mean_values
+import numpy as np
+
+
+def preprocess(data):
+    flattened = []
+
+    y, x, dim = data.shape
+
+    for i in range(y):
+        flattened.append(np.ndarray.flatten(data[i]))
+
+    return np.array(flattened)
+
+
+def pca(data, mean_values, n_components):
+    # subtract mean
+    zero_mean = data - mean_values
+    X = preprocess(zero_mean)
+
+    observations, dims = X.shape
+
+    U, S, V = np.linalg.svd(X)
+
+    return V[:n_components], S[:n_components]