def cov(self, other=None, pairwise=None, bias=False, **kwargs):
"""
Exponential weighted sample covariance.
"""
if other is None:
other = self._selected_obj
# only default unset
pairwise = True if pairwise is None else pairwise
other = self._shallow_copy(other)
def _get_cov(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
cov = libwindow.ewmcov(
X._prep_values(),
Y._prep_values(),
self.com,
int(self.adjust),
int(self.ignore_na),
int(self.min_periods),
int(bias),
)
return X._wrap_result(cov)
return _flex_binary_moment(self._selected_obj,
other._selected_obj,
_get_cov,
pairwise=bool(pairwise))
def corr(
self,
other: Optional[Union[np.ndarray, FrameOrSeries]] = None,
pairwise: Optional[bool] = None,
**kwargs,
):
"""
Exponential weighted sample correlation.
Parameters
----------
other : Series, DataFrame, or ndarray, optional
If not supplied then will default to self and produce pairwise
output.
pairwise : bool, default None
If False then only matching columns between self and other will be
used and the output will be a DataFrame.
If True then all pairwise combinations will be calculated and the
output will be a MultiIndex DataFrame in the case of DataFrame
inputs. In the case of missing elements, only complete pairwise
observations will be used.
**kwargs
Keyword arguments to be passed into func.
"""
if other is None:
other = self._selected_obj
# only default unset
pairwise = True if pairwise is None else pairwise
other = self._shallow_copy(other)
def _get_corr(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
def _cov(x, y):
return window_aggregations.ewmcov(
x,
y,
self.com,
self.adjust,
self.ignore_na,
self.min_periods,
1,
)
x_values = X._prep_values()
y_values = Y._prep_values()
with np.errstate(all="ignore"):
cov = _cov(x_values, y_values)
x_var = _cov(x_values, x_values)
y_var = _cov(y_values, y_values)
corr = cov / zsqrt(x_var * y_var)
return X._wrap_result(corr)
return _flex_binary_moment(self._selected_obj,
other._selected_obj,
_get_corr,
pairwise=bool(pairwise))
def cov(
self,
other: Optional[Union[np.ndarray, FrameOrSeries]] = None,
pairwise: Optional[bool] = None,
bias: bool = False,
**kwargs,
):
"""
Exponential weighted sample covariance.
Parameters
----------
other : Series, DataFrame, or ndarray, optional
If not supplied then will default to self and produce pairwise
output.
pairwise : bool, default None
If False then only matching columns between self and other will be
used and the output will be a DataFrame.
If True then all pairwise combinations will be calculated and the
output will be a MultiIndex DataFrame in the case of DataFrame
inputs. In the case of missing elements, only complete pairwise
observations will be used.
bias : bool, default False
Use a standard estimation bias correction.
**kwargs
Keyword arguments to be passed into func.
"""
if other is None:
other = self._selected_obj
# only default unset
pairwise = True if pairwise is None else pairwise
other = self._shallow_copy(other)
def _get_cov(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
cov = window_aggregations.ewmcov(
X._prep_values(),
Y._prep_values(),
self.com,
self.adjust,
self.ignore_na,
self.min_periods,
bias,
)
return X._wrap_result(cov)
return _flex_binary_moment(self._selected_obj,
other._selected_obj,
_get_cov,
pairwise=bool(pairwise))
def corr(self, other=None, pairwise=None, **kwargs):
"""
Exponential weighted sample correlation.
"""
if other is None:
other = self._selected_obj
# only default unset
pairwise = True if pairwise is None else pairwise
other = self._shallow_copy(other)
def _get_corr(X, Y):
X = self._shallow_copy(X)
Y = self._shallow_copy(Y)
def _cov(x, y):
return libwindow.ewmcov(
x,
y,
self.com,
int(self.adjust),
int(self.ignore_na),
int(self.min_periods),
1,
)
x_values = X._prep_values()
y_values = Y._prep_values()
with np.errstate(all="ignore"):
cov = _cov(x_values, y_values)
x_var = _cov(x_values, x_values)
y_var = _cov(y_values, y_values)
corr = cov / _zsqrt(x_var * y_var)
return X._wrap_result(corr)
return _flex_binary_moment(self._selected_obj,
other._selected_obj,
_get_corr,
pairwise=bool(pairwise))
