def testRunningStat(self):
for shp in ((), (3,), (3, 4)):
li = []
rs = RunningStat(shp)
for _ in range(5):
val = np.random.randn(*shp)
rs.push(val)
li.append(val)
m = np.mean(li, axis=0)
self.assertTrue(np.allclose(rs.mean, m))
v = np.square(m) if (len(li) == 1) else np.var(li, ddof=1, axis=0)
self.assertTrue(np.allclose(rs.var, v))
def testRunningStat(self):
for shp in ((), (3, ), (3, 4)):
li = []
rs = RunningStat(shp)
for _ in range(5):
val = np.random.randn(*shp)
rs.push(val)
li.append(val)
m = np.mean(li, axis=0)
self.assertTrue(np.allclose(rs.mean, m))
v = (np.square(m)
if (len(li) == 1) else np.var(li, ddof=1, axis=0))
self.assertTrue(np.allclose(rs.var, v))
def testCombiningStat(self):
for shape in [(), (3, ), (3, 4)]:
li = []
rs1 = RunningStat(shape)
rs2 = RunningStat(shape)
rs = RunningStat(shape)
for _ in range(5):
val = np.random.randn(*shape)
rs1.push(val)
rs.push(val)
li.append(val)
for _ in range(9):
rs2.push(val)
rs.push(val)
li.append(val)
rs1.update(rs2)
assert np.allclose(rs.mean, rs1.mean)
assert np.allclose(rs.std, rs1.std)
def testCombiningStat(self):
for shape in [(), (3, ), (3, 4)]:
li = []
rs1 = RunningStat(shape)
rs2 = RunningStat(shape)
rs = RunningStat(shape)
for _ in range(5):
val = np.random.randn(*shape)
rs1.push(val)
rs.push(val)
li.append(val)
for _ in range(9):
rs2.push(val)
rs.push(val)
li.append(val)
rs1.update(rs2)
assert np.allclose(rs.mean, rs1.mean)
assert np.allclose(rs.std, rs1.std)
