Merge pull request #562 from Open-Deep-ML/moe18-patch-8

Fix covariance matrix calculation logic

Author: Open-Deep-ML

questions/10_calculate-covariance-matrix/solution.py

@@ -1,15 +1,15 @@
 import numpy as np
 
 def calculate_covariance_matrix(vectors: list[list[float]]) -> list[list[float]]:
-    n_features = len(vectors)
-    n_observations = len(vectors[0])
-    covariance_matrix = [[0 for _ in range(n_features)] for _ in range(n_features)]
+    n_observations = len(vectors)
+    n_features = len(vectors[0])
+    covariance_matrix = [[0 for _ in range(n_observations)] for _ in range(n_observations)]
 
-    means = [sum(feature) / n_observations for feature in vectors]
+    means = [sum(feature) / n_features for feature in vectors]
 
-    for i in range(n_features):
-        for j in range(i, n_features):
-            covariance = sum((vectors[i][k] - means[i]) * (vectors[j][k] - means[j]) for k in range(n_observations)) / (n_observations - 1)
+    for i in range(n_observations):
+        for j in range(i, n_observations):
+            covariance = sum((vectors[i][k] - means[i]) * (vectors[j][k] - means[j]) for k in range(n_features)) / (n_features - 1)
             covariance_matrix[i][j] = covariance_matrix[j][i] = covariance
 
     return covariance_matrix