Header fix

exercises/02450Toolbox_Python/Scripts/ex2_2_1.py

-# exercise 3.3.1
+# exercise 2.2.1
 
 import importlib_resources
 import matplotlib.pyplot as plt
@@ -18,60 +18,68 @@ similarity_measure = "SMC"
 # Load Matlab data file to python dict structure
 X = loadmat(filename)["X"]
 # You can also try the CBCL faces dataset (remember to change 'transpose')
-#X = loadmat('../Data/wildfaces_grayscale.mat')['X']
+# X = loadmat('../Data/wildfaces_grayscale.mat')['X']
 N, M = X.shape
-transpose = False # should the plotted images be transposed? 
+transpose = False  # should the plotted images be transposed?
 
 
-# Search the face database for similar faces 
+# Search the face database for similar faces
 # Index of all other images than i
-noti = list(range(0,i)) + list(range(i+1,N)) 
+noti = list(range(0, i)) + list(range(i + 1, N))
 # Compute similarity between image i and all others
-sim = similarity(X[i,:], X[noti,:], similarity_measure)
+sim = similarity(X[i, :], X[noti, :], similarity_measure)
 sim = sim.tolist()[0]
 # Tuples of sorted similarities and their indices
-sim_to_index = sorted(zip(sim,noti))
+sim_to_index = sorted(zip(sim, noti))
 
 
 # Visualize query image and 5 most/least similar images
-plt.figure(figsize=(12,8))
-plt.subplot(3,1,1)
+plt.figure(figsize=(12, 8))
+plt.subplot(3, 1, 1)
 
 img_hw = int(np.sqrt(len(X[0])))
-img = np.reshape(X[i], (img_hw,img_hw))
-if transpose: img = img.T
+img = np.reshape(X[i], (img_hw, img_hw))
+if transpose:
+    img = img.T
 plt.imshow(img, cmap=plt.cm.gray)
-plt.xticks([]); plt.yticks([])
-plt.title('Query image')
-plt.ylabel('image #{0}'.format(i))
+plt.xticks([])
+plt.yticks([])
+plt.title("Query image")
+plt.ylabel("image #{0}".format(i))
 
 
 for ms in range(5):
 
     # 5 most similar images found
-    plt.subplot(3,5,6+ms)
-    im_id = sim_to_index[-ms-1][1]
-    im_sim = sim_to_index[-ms-1][0]
-    img = np.reshape(X[im_id],(img_hw,img_hw))
-    if transpose: img = img.T
+    plt.subplot(3, 5, 6 + ms)
+    im_id = sim_to_index[-ms - 1][1]
+    im_sim = sim_to_index[-ms - 1][0]
+    img = np.reshape(X[im_id], (img_hw, img_hw))
+    if transpose:
+        img = img.T
     plt.imshow(img, cmap=plt.cm.gray)
-    plt.xlabel('sim={0:.3f}'.format(im_sim))
-    plt.ylabel('image #{0}'.format(im_id))
-    plt.xticks([]); plt.yticks([])
-    if ms==2: plt.title('Most similar images')
+    plt.xlabel("sim={0:.3f}".format(im_sim))
+    plt.ylabel("image #{0}".format(im_id))
+    plt.xticks([])
+    plt.yticks([])
+    if ms == 2:
+        plt.title("Most similar images")
 
     # 5 least similar images found
-    plt.subplot(3,5,11+ms)
+    plt.subplot(3, 5, 11 + ms)
     im_id = sim_to_index[ms][1]
     im_sim = sim_to_index[ms][0]
-    img = np.reshape(X[im_id],(img_hw,img_hw))
-    if transpose: img = img.T
+    img = np.reshape(X[im_id], (img_hw, img_hw))
+    if transpose:
+        img = img.T
     plt.imshow(img, cmap=plt.cm.gray)
-    plt.xlabel('sim={0:.3f}'.format(im_sim))
-    plt.ylabel('image #{0}'.format(im_id))
-    plt.xticks([]); plt.yticks([])
-    if ms==2: plt.title('Least similar images')
-    
+    plt.xlabel("sim={0:.3f}".format(im_sim))
+    plt.ylabel("image #{0}".format(im_id))
+    plt.xticks([])
+    plt.yticks([])
+    if ms == 2:
+        plt.title("Least similar images")
+
 plt.show()
 
-print('Ran Exercise 2.2.1')
+print("Ran Exercise 2.2.1")

exercises/02450Toolbox_Python/Scripts/ex2_2_2.py

-# exercise 3.2.2
+# exercise 2.2.2
 
 import numpy as np
 

exercises/02450Toolbox_Python/Scripts/ex3_1_4.py

 # exercise 3.1.4
-# (requires data structures from ex. 2.2.1 and 2.2.3)
+# (requires data structures from ex. 3.1.1)
 from ex3_1_1 import *
 from matplotlib.pyplot import figure, legend, plot, show, title, xlabel, ylabel
 from scipy.linalg import svd

exercises/02450Toolbox_Python/Scripts/ex3_1_5.py

 # exercise 3.1.5
 
-# (requires data structures from ex. 2.2.1)
+# (requires data structures from ex. 3.1.1)
 import matplotlib.pyplot as plt
 from ex3_1_1 import *
 from scipy.linalg import svd

exercises/02450Toolbox_Python/Scripts/ex4_1_1.py

@@ -28,5 +28,3 @@ plot(X, ".")
 subplot(1, 3, 3)
 hist(X, bins=nbins)
 show()
-
-