--- a
+++ b/Results.py
@@ -0,0 +1,41 @@
+import scipy.io as sio
+import matplotlib.pyplot as plt
+
+import os
+import glob
+
+import numpy as np
+
+Patient_id = np.unique(sio.loadmat("Sample Data/trials_subNums.mat")['subjectNum'][0]) #corresponding to the patient id
+dir = "Results/"
+models = ["Basic", "LSTM", "MaxCNN", "Mix", "TempCNN"]
+
+Results = np.zeros((len(Patient_id), len(models),20))
+
+inc = 0
+for model in models:
+    inc_patient = 0
+    for patient in Patient_id:
+        doc = glob.glob(dir+"*"+model+"*"+"t"+str(patient)+".mat")[0]
+        file = sio.loadmat(doc)['res']
+        Results[inc_patient,inc, :] = file[:,3]
+        inc_patient += 1
+    inc += 1
+
+fig = plt.figure()
+
+for i in range(len(models)):
+    a = 5
+    plt.plot(np.max(Results[:,i,:],axis=1), '.-', label = models[i])
+
+plt.legend()
+#plt.boxplot(Results[:,0,:])
+#plt.boxplot(Results[:,1,:])
+
+
+
+lstm = sio.loadmat("Results/result_LSTM.mat")['vacc']
+plt.plot(np.mean(lstm, axis=0))
+
+
+plt.show()