Adds plots

assignment6/solution.py

@@ -42,7 +42,6 @@ def one_d() -> None:
 	U, S, VT, _ = uz_image.compute_PCA(points)
 
 	uz_image.plot_histogram_pca(U, S, VT)
-
 # U is the matrix of eigenvectors
 # S is the vector of eigenvalues
 
@@ -234,21 +233,24 @@ def three_b():
 
     img2 = imgs[0].copy()
     
-    # Project image into PCA subspace
+    fig, axs = plt.subplots(5, 2)
-    y_i = np.matmul(img2 - mean, U)
+    for i in range(5):
-    y_i[0] = 0
+    
+        # Project image into PCA subspace
+        y_i = np.matmul(img2 - mean, U)
+        y_i[i] = 0
 
-    # Project image back into original subspace
+        # Project image back into original subspace
-    x_i = np.matmul(y_i, U.T) + mean
+        x_i = np.matmul(y_i, U.T) + mean
 
-    # Plot the original image and the projected images
+        # Plot the original image and the projected images
-    diff = (np.sum(np.abs(img - x_i)))
+        diff = (np.sum(np.abs(img - x_i)))
-    fig, axs = plt.subplots(1, 2)
+
-    fig.suptitle(f'Add noise in PCA space, diff: {np.round(diff, 2)}')
+        axs[i, 0].imshow(img2.reshape((96, 84)).copy(), cmap='gray')
-    axs[0].imshow(img2.reshape((96, 84)), cmap='gray')
+        axs[i, 1].imshow(x_i.reshape((96, 84)).copy(), cmap='gray')
-    axs[1].imshow(x_i.reshape((96, 84)), cmap='gray')
+
+        axs[i, 1].set(title=f'Add noise in PCA space y[{i}], diff: {np.round(diff, 2)}')
     plt.show()
-
 def three_c():
     """
     Effect of the number of components on the reconstruction: Take a random
@@ -430,7 +432,7 @@ def main():
     #one_f()
     #two_b()
     #three_a()
-    #three_b()
+    three_b()
 	#three_c()
     #three_d()
     #three_e()