--- a
+++ b/ecg_classification/meter.py
@@ -0,0 +1,49 @@
+import os
+import itertools
+import time
+import random
+
+import numpy as np 
+import torch
+from sklearn.metrics import accuracy_score, auc, f1_score, precision_score, recall_score
+
+
+class Meter:
+    def __init__(self, n_classes=5):
+        self.metrics = {}
+        self.confusion = torch.zeros((n_classes, n_classes))
+    
+    def update(self, x, y, loss):
+        x = np.argmax(x.detach().cpu().numpy(), axis=1)
+        y = y.detach().cpu().numpy()
+        self.metrics['loss'] += loss
+        self.metrics['accuracy'] += accuracy_score(x,y)
+        self.metrics['f1'] += f1_score(x,y,average='macro')
+        self.metrics['precision'] += precision_score(x, y, average='macro', zero_division=1)
+        self.metrics['recall'] += recall_score(x,y, average='macro', zero_division=1)
+        
+        self._compute_cm(x, y)
+        
+    def _compute_cm(self, x, y):
+        for prob, target in zip(x, y):
+            if prob == target:
+                self.confusion[target][target] += 1
+            else:
+                self.confusion[target][prob] += 1
+    
+    def init_metrics(self):
+        self.metrics['loss'] = 0
+        self.metrics['accuracy'] = 0
+        self.metrics['f1'] = 0
+        self.metrics['precision'] = 0
+        self.metrics['recall'] = 0
+        
+    def get_metrics(self):
+        return self.metrics
+    
+    def get_confusion_matrix(self):
+        return self.confusion
+
+   
+if __name__ == '__main__':
+    meter = Meter()