update plot code

classification.py

@@ -257,10 +257,12 @@ if __name__ == '__main__':
         sns.pairplot(train_glcm, hue='class')
         # save the plot to a file
         plt.savefig('train_glcm_distribution.png')
+        plt.close()
 
         sns.pairplot(train_lbp, hue='class')
         # save the plot to a file
         plt.savefig('train_lbp_distribution.png')
+        plt.close()
 
         #Use plots to visualize feature distributions and decision boundaries of the classifiers clf_glcm, clf_lbp using t-sne
         from sklearn.manifold import TSNE
@@ -275,10 +277,12 @@ if __name__ == '__main__':
         plt.title('GLCM features')
         # save the plot to a file
         plt.savefig('train_glcm_tsne.png')
+        plt.close()
 
         plt.scatter(X_train_lbp_tsne[y_train_lbp == 'grass', 0], X_train_lbp_tsne[y_train_lbp == 'grass', 1], color='red', label='grass')
         plt.scatter(X_train_lbp_tsne[y_train_lbp == 'wood', 0], X_train_lbp_tsne[y_train_lbp == 'wood', 1], color='blue', label='wood')
         plt.legend()
         plt.title('LBP features')
         # save the plot to a file
-        plt.savefig('train_lbp_tsne.png')
+        plt.savefig('train_lbp_tsne.png')
+        plt.close()