Improve Scatterplots(3D) and Confusion Matrix

1 files changed, 12 insertions, 2 deletions

diff --git a/train.py b/train.py
index f09f025..5530664 100755
--- a/train.py
+++ b/train.py
@@ -4,6 +4,7 @@
 # EE4 Pattern Recognition coursework
 
 import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
 import sys
 import random
 from random import randint
@@ -64,6 +65,7 @@ parser.add_argument("-2", "--grapheigen", help="Swow 2D graph of targets versus
 parser.add_argument("-p", "--pca", help="Use PCA", action='store_true')
 parser.add_argument("-l", "--lda", help="Use LDA", action='store_true')
 parser.add_argument("-r", "--reconstruct", help="Use PCA reconstruction, specify face NR", type=int, default=0)
+parser.add_argument("-cm", "--conf_mat", help="Show visual confusion matrix", action='store_true')
 
 parser.add_argument("-q", "--pca_r", help="Use Reduced PCA", action='store_true')
 
@@ -123,7 +125,7 @@ if args.pca or args.pca_r:
         plt.show()
 
 if args.lda or (args.pca and args.lda):
-    lda = LinearDiscriminantAnalysis(n_components=M)
+    lda = LinearDiscriminantAnalysis(n_components=M, solver='eigen')
     faces_train = lda.fit_transform(faces_train, target_train)
     faces_test = lda.transform(faces_test)
     class_means = lda.means_
@@ -150,7 +152,9 @@ if args.grapheigen:
     # Colors for distinct individuals
     cols = ['#{:06x}'.format(randint(0, 0xffffff)) for i in range(52)]
     pltCol = [cols[int(k)] for k in target_train]
-    plt.scatter(faces_train[:, 0], faces_train[:, 1], color=pltCol)
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+    ax.scatter(faces_train[:, 0], faces_train[:, 1], faces_train[:, 2], marker='o', color=pltCol)
     plt.show()
 
 classifier = KNeighborsClassifier(n_neighbors=args.neighbors)
@@ -165,4 +169,10 @@ else:
 
 cm = confusion_matrix(target_test, target_pred)  
 print(cm)  
+if (args.conf_mat):
+    plt.matshow(cm, cmap='Blues')
+    plt.colorbar()
+    plt.ylabel('Actual')
+    plt.xlabel('Predicted')
+    plt.show()
 print('Accuracy %fl' % accuracy_score(target_test, target_pred))