--- a
+++ b/brainDecode/towardMoabbIntegration/utils/TrainingViewer.py
@@ -0,0 +1,121 @@
+
+
+# TODO, get this to work:
+
+print(max(accuracy_rec[:,1]))
+
+def smoothing(record, smoothingRadius):
+ if record.shape[0] > 2 * smoothingRadius :
+   record_smooth = np.zeros((record.shape[0] - (2 * smoothingRadius), record.shape[1]))
+   
+   for i in range(record_smooth.shape[0]):
+     for j in range(record_smooth.shape[1]):
+       record_smooth[i,j] = record[i:i+2*smoothingRadius,j].mean()
+ 
+ return record_smooth
+
+
+# Define smoothing radius here
+smoothingRadius = 10
+
+population_loss_rec = loss_rec
+population_accuracy_rec = accuracy_rec
+population_loss_smooth = smoothing(loss_rec, smoothingRadius)
+population_accuracy_smooth = smoothing(accuracy_rec, smoothingRadius)
+
+
+individual_loss_rec = loss_rec
+individual_accuracy_rec = accuracy_rec
+individual_loss_smooth = smoothing(loss_rec, smoothingRadius)
+individual_accuracy_smooth = smoothing(accuracy_rec, smoothingRadius)
+
+import matplotlib.pyplot as plt
+
+
+plt.figure(figsize=(12,8))
+plt.title('First training Training')
+plt.subplot(221)
+handles = plt.plot(population_loss_rec)
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.subplot(222)
+handles = plt.plot(population_loss_smooth)
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.subplot(223)
+handles = plt.plot(population_accuracy_rec)
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.subplot(224)
+handles = plt.plot(population_accuracy_smooth)
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.show()
+
+plt.figure(figsize=(12,8))
+plt.title('Classifier re-training Training')
+plt.subplot(221)
+handles = plt.plot(individual_loss_rec)
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.subplot(222)
+handles = plt.plot(individual_loss_smooth)
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.subplot(223)
+handles = plt.plot(individual_accuracy_rec)
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.subplot(224)
+handles = plt.plot(individual_accuracy_smooth)
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.show()
+
+
+
+
+
+
+plt.figure(figsize=(12,8))
+plt.subplot(221)
+handles = plt.plot(population_loss_rec)
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.subplot(222)
+handles = plt.plot(population_loss_smooth)
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.subplot(223)
+handles = plt.plot(population_accuracy_rec)
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.subplot(224)
+handles = plt.plot(population_accuracy_smooth)
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(handles, ['train', 'test'])
+
+plt.show()