def transform(self,
x: np.ndarray,
groups: np.ndarray = None) -> np.ndarray:
if groups is not None:
index = array_index(self.labels, groups)
return mask_values_2d(x, self.mean[index], self.std[index],
self.num_stds)
else:
return mask_values_1d(x, self.mean, self.std, self.num_stds)
def transform(self,
x: np.ndarray,
groups: np.ndarray = None) -> np.ndarray:
if groups is not None:
index = array_index(self.labels, groups)
if self.method == 'flat':
res = mask_values_2d(x, self.mean[index], self.std[index],
self.num_stds)
else:
res = interp_values_2d(x, groups, self.mean[index],
self.std[index], self.num_stds,
self.interval)
else:
if self.method == 'flat':
res = mask_values_1d(x, self.mean, self.std, self.num_stds)
else:
res = interp_values_1d(x, self.mean, self.std, self.num_stds,
self.interval)
return res
def transform(self, x: np.ndarray) -> np.ndarray:
groups = x[:, 0].astype(int)
index = array_index(self.labels_, groups)
return (x[:, 1:] - self.mean_[index]) / np.maximum(self.std_[index], 1e-8)
def transform(self, x: np.ndarray, groups: np.ndarray=None) -> np.ndarray:
if groups is not None:
index = array_index(self.labels, groups)
return (x - self.mean[index]) / np.maximum(self.std[index], 1e-8)
else:
return (x - self.mean) / np.maximum(self.std, 1e-8)