def mnp_var(x):
a = mnp.std(x)
b = mnp.std(x, axis=0)
c = mnp.std(x, axis=(0))
d = mnp.std(x, axis=(0, 1, 2))
e = mnp.std(x, axis=(-1, 1, 2), ddof=1, keepdims=True)
return a, b, c, d, e
def mnp_std(x):
a = mnp.std(x)
b = mnp.std(x, axis=None)
c = mnp.std(x, axis=0)
d = mnp.std(x, axis=1)
e = mnp.std(x, axis=(-1, 1))
f = mnp.std(x, axis=(0, 1, 2))
g = mnp.std(x, axis=None, ddof=1, keepdims=True)
h = mnp.std(x, axis=0, ddof=1, keepdims=True)
i = mnp.std(x, axis=(2), ddof=1, keepdims=True)
return a, b, c, d, e, f, g, h, i
def __init__(self,
data,
normalize=False,
mean=None,
std=None,
shuffle=False,
train_size=0.8,
train=True,
random_seed=42):
self.random = np.random.RandomState(random_seed)
shuffle_idx = np.arange(data.shape[0])
if shuffle:
self.random.shuffle(shuffle_idx)
if isinstance(train_size, float):
train_samples = int(data.shape[0] * train_size)
else:
raise TypeError("The param train_size must be float < 1")
if train:
data = data[shuffle_idx[:train_samples]]
else:
data = data[shuffle_idx[train_samples:]]
# as tensor
self.data_set = MsTensor(data).astype(dtype=mstype.float32)
# Normalize data
self.mean, self.std = mean, std
if normalize:
if mean is None or std is None:
self.mean = msnp.mean(self.data_set, 0, keepdims=True)
self.std = msnp.std(self.data_set, 0, keepdims=True)
self.data_set = (self.data_set - self.mean) / self.std
self.n_samples = self.data_set.shape[0]