--- a
+++ b/Draw_Photos/Draw_Confusion_Matrix.py
@@ -0,0 +1,34 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from sklearn.metrics import confusion_matrix
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+
+# Read labels and scores
+labels = pd.read_csv('labels.csv', header=None)
+labels = np.array(labels).astype('float32')
+prediction = pd.read_csv('prediction.csv', header=None)
+prediction = np.array(prediction).astype('float32')
+
+# Set photo parameters
+sns.set()
+mpl.rcParams['font.sans-serif'] = 'Times New Roman'
+mpl.rcParams['axes.unicode_minus'] = False
+
+# Draw Confusion Matrix
+f, ax = plt.subplots()
+C2 = confusion_matrix(labels, prediction, labels=[0, 1, 2, 3])
+C2 = np.around(C2/sum(C2), 4)
+# print(C2)
+
+sns.heatmap(C2, annot=True, ax=ax, fmt='.4f')
+ax.set(xticklabels=['L', 'R', 'B', 'F'], yticklabels=['L', 'R', 'B', 'F'])
+ax.set_title('Confusion Matrix of Subject Nine', fontsize=16, fontweight='bold')
+ax.set_xlabel('Predicted Class', fontsize=14, fontweight='bold')
+ax.set_ylabel('True Class', fontsize=14, fontweight='bold')
+plt.savefig('Confusion_Matrix.png', format='png', bbox_inches='tight', transparent=True, dpi=600)
+plt.show()