def update(self, samples) -> None:
"""
Update the online calculation of the mean and variance.
Notes:
Modifies the metrics in place.
Args:
samples: data samples
Returns:
None
"""
n = len(samples)
sample_mean = be.tsum(samples) / n
sample_square = be.tsum(be.square(samples - sample_mean))
delta = sample_mean - self.mean
new_num = self.num + n
correction = n * self.num * delta**2 / max(new_num, 1)
self.square += sample_square + correction
self.var = self.square / max(new_num - 1, 1)
self.mean = (self.num * self.mean + n * sample_mean) / max(new_num, 1)
self.num = new_num
def update(self, samples, axis=0) -> None:
"""
Update the online calculation of the mean and variance.
Notes:
Modifies the metrics in place.
Args:
samples: data samples
Returns:
None
"""
# compute the sample size and sample mean
n = len(samples)
sample_mean = be.tsum(samples, axis=axis) / max(n, 1)
sample_square = be.tsum(be.square(be.subtract(sample_mean, samples)),
axis=axis)
if self.mean is None:
self.mean = be.zeros_like(sample_mean)
self.square = be.zeros_like(sample_square)
self.var = be.zeros_like(sample_square)
self.num = 0
delta = sample_mean - self.mean
new_num = self.num + n
correction = n * self.num * be.square(delta) / max(new_num, 1)
self.square += sample_square + correction
self.var = self.square / max(new_num - 1, 1)
self.mean = (self.num * self.mean + n * sample_mean) / max(new_num, 1)
self.num = new_num
def update(self, samples, axis=0) -> None:
"""
Update the online calculation of the mean.
Notes:
Modifies the metrics in place.
Args:
samples: data samples
Returns:
None
"""
# compute the sample size and sample mean
n = len(samples)
sample_mean = be.tsum(samples, axis=axis) / n
# initialize the num and mean attributes if necessary
if self.mean is None:
self.mean = be.zeros_like(sample_mean)
self.num = 0
# update the num and mean attributes
tmp = self.num * self.mean + n * sample_mean
self.num += n
self.mean = tmp / self.num
def update(self, samples) -> None:
"""
Update the online calculation of the mean.
Notes:
Modifies the metrics in place.
Args:
samples: data samples
Returns:
None
"""
n = len(samples)
sample_mean = be.tsum(samples) / n
self.num += n
self.mean += (sample_mean - self.mean) * n / max(self.num, 1)
def test_l1_normalize():
result_pre = [
be.tsum(pre.l1_normalize(tensor), axis=1) for tensor in tensors
]
result_ref = [be.ones((len(tensor), )) for tensor in tensors]
assert compare_lists(result_pre, result_ref)