Fixed AverageValueMeter and tests (#124)

test/test_meters.py

@@ -47,7 +47,7 @@ def testAverageValueMeter_n(self):
         """
         m = meter.AverageValueMeter()
         for i in range(1, 11):
-            m.add(i * i, n=i)
+            m.add(i, n=i)
         mean, std = m.value()
         self.assertEqual(mean, 7.0)
         m.reset()

torchnet/meter/averagevaluemeter.py

@@ -11,22 +11,22 @@ def __init__(self):
 
     def add(self, value, n=1):
         self.val = value
-        self.sum += value
-        self.var += value * value
-        self.n += n
-
-        if self.n == 0:
-            self.mean, self.std = np.nan, np.nan
-        elif self.n == 1:
-            self.mean = 0.0 + self.sum  # This is to force a copy in torch/numpy
+        self.sum += value * n
+        if n <= 0:
+            raise ValueError("Cannot use a non-positive weight for the running stat.")
+        elif self.n == 0:
+            self.mean = 0.0 + value  # This is to force a copy in torch/numpy
             self.std = np.inf
             self.mean_old = self.mean
             self.m_s = 0.0
         else:
-            self.mean = self.mean_old + (value - n * self.mean_old) / float(self.n)
-            self.m_s += (value - self.mean_old) * (value - self.mean)
+            self.mean = self.mean_old + n * (value - self.mean_old) / float(self.n + n)
+            self.m_s += n * (value - self.mean_old) * (value - self.mean)
             self.mean_old = self.mean
-            self.std = np.sqrt(self.m_s / (self.n - 1.0))
+            self.std = np.sqrt(self.m_s / (self.n + n - 1.0))
+        self.var = self.std ** 2
+
+        self.n += n
 
     def value(self):
         return self.mean, self.std