def fit(self, x: np.ndarray, groups: np.ndarray = None):
if groups is not None:
group_index = group_mapping(groups)
self.mean = aggregate(group_index, x, 'mean')
self.std = aggregate(group_index, x, 'std', self.ddof)
self.labels = np.unique(groups)
else:
self.mean = simple_mean(x, axis=0)
self.std = simple_std(x, axis=0, ddof=self.ddof)
def simple_settle(weights: np.ndarray,
ret_series: np.ndarray,
groups: np.ndarray = None) -> np.ndarray:
if ret_series.ndim == 1:
ret_series = ret_series.reshape((-1, 1))
ret_mat = weights * ret_series
if groups is not None:
groups = group_mapping(groups)
return aggregate(groups, ret_mat, 'sum')
else:
if ret_mat.ndim == 1:
ret_mat = ret_mat.reshape((-1, 1))
return simple_sum(ret_mat, axis=0)
def fit(self, x: np.ndarray):
raw_groups = x[:, 0].astype(int)
groups = group_mapping(raw_groups)
self.mean_ = aggregate(groups, x[:, 1:], 'mean')
self.std_ = aggregate(groups, x[:, 1:], 'std', self.ddof_)
self.labels_ = np.unique(raw_groups)