def _test_u_distance_correlation_vector_generic(self,
vector_type=None,
type_cov=None,
type_cor=None):
"""
Auxiliar function for testing U-distance correlation in vectors.
This function is provided to check that the results are the
same with different dtypes, but that the dtype of the result is
the right one.
"""
if type_cov is None:
type_cov = vector_type
if type_cor is None:
type_cor = vector_type
arr1 = np.array([
vector_type(1),
vector_type(2),
vector_type(3),
vector_type(4),
vector_type(5),
vector_type(6)
])
arr2 = np.array([
vector_type(1),
vector_type(7),
vector_type(5),
vector_type(5),
vector_type(6),
vector_type(2)
])
covariance = dcor.u_distance_covariance_sqr(arr1, arr2)
self.assertIsInstance(covariance, type_cov)
self.assertAlmostEqual(covariance, type_cov(-0.88889), places=5)
correlation = dcor.u_distance_correlation_sqr(arr1, arr2)
self.assertIsInstance(correlation, type_cor)
self.assertAlmostEqual(correlation, type_cor(-0.41613), places=5)
covariance = dcor.u_distance_covariance_sqr(arr1, arr1)
self.assertIsInstance(covariance, type_cov)
self.assertAlmostEqual(covariance, type_cov(1.5556), places=4)
correlation = dcor.u_distance_correlation_sqr(arr1, arr1)
self.assertIsInstance(correlation, type_cor)
self.assertAlmostEqual(correlation, type_cor(1), places=5)
def test_u_distance_covariance_avl_overflow(self):
"""Test potential overflow in fast distance correlation"""
arr1 = np.concatenate((np.zeros(500, dtype=int),
np.ones(500, dtype=int)))
covariance = dcor.u_distance_covariance_sqr(
arr1, arr1,
method='avl',
compile_mode=dcor.CompileMode.NO_COMPILE)
self.assertAlmostEqual(covariance, 0.25050, places=5)
def fit(self, x, **kwargs):
if 'trimming' not in kwargs:
trimming = 0
else:
trimming = kwargs.get('trimming')
if 'biascorr' not in kwargs:
biascorr = False
else:
biascorr = kwargs.get('biascorr')
if 'alpha' not in kwargs:
alpha = 1
else:
alpha = kwargs.get('alpha')
if 'dmetric' not in kwargs:
dmetric = 'euclidean'
else:
dmetric = kwargs.get('dmetric')
if 'calcmode' not in kwargs:
calcmode = 'fast'
else:
calcmode = kwargs.get('calcmode')
if 'order' not in kwargs:
order = 2
else:
order = kwargs.get('order')
if self.mode == 'var':
mode = 'mom'
order = 2
elif self.mode == 'cov':
mode = 'com'
order = 2
elif self.mode == 'std':
self.center = 'mean'
mode = 'mom'
order = 2
num_power = 0.5
elif self.mode == 'skew':
self.center = 'mean'
mode = 'mom'
order = 3
num_power = 1.5
elif self.mode == 'kurt':
self.center = 'mean'
mode = 'mom'
order = 4
num_power = 2
if 'Fisher' not in kwargs:
Fisher = True
else:
Fisher = kwargs.get('Fisher')
elif self.mode == 'cos':
self.center = 'mean'
mode = 'com'
order = 3
if 'standardized' not in kwargs:
standardized = True
else:
standardized = kwargs.get('standardized')
elif self.mode == 'M3':
self.center = 'mean'
mode = 'com'
order = 3
if 'standardized' not in kwargs:
standardized = False
else:
standardized = kwargs.get('standardized')
elif self.mode == 'c*k':
self.center = 'mean'
mode = 'com'
order = 4
if 'standardized' not in kwargs:
standardized = True
else:
standardized = kwargs.get('standardized')
if 'Fisher' not in kwargs:
Fisher = True
else:
Fisher = kwargs.get('Fisher')
else:
mode = self.mode
if order > 2:
if 'option' not in kwargs:
option = 1
else:
option = kwargs.get('option')
else:
option = 0
n = len(x)
ntrim = round(n * (1 - trimming))
if len(x.shape) == 1:
x = np.matrix(x).reshape((n, 1))
if mode == 'corr':
alpha = 1
if n == 0:
raise (MyException('Please feed data with length > 0'))
if self.center == 'median':
locest = np.median
else:
locest = trim_mean
# Classical variance, covariance and continuum as well as robust alternatives
if self.est == 'arithmetic':
# Variance
if mode != 'com':
# if self.center=='mean':
# xvar = trimvar(x,trimming)*ntrim/(ntrim-1)
# elif self.center=='median':
# xvar = srs.mad(x)**2
xmom = trim_mom(x, x, locest, order, trimming, option,
biascorr)
self.x_moment_ = xmom
if self.mode in ('std', 'skew', 'kurt'):
x2mom = trim_mom(x, x, locest, 2, trimming, option, False)
xmom /= (x2mom**num_power)
if biascorr:
if self.mode == 'skew':
xmom *= (ntrim - 1)**2
xmom /= np.sqrt(ntrim**2 - ntrim)
elif self.mode == 'kurt':
xmom = xmom * ntrim - xmom / ntrim
xmom -= 3 * (ntrim - 1)**2.0 / ((ntrim - 2) *
(ntrim - 3))
if not Fisher:
xmom += 3
if mode == 'mom':
self.moment_ = xmom
# Covariance or continuum
if mode != 'mom':
if 'y' not in kwargs:
raise (MyException('Please supply second data vector'))
else:
y = kwargs.get('y')
n1 = len(y)
if n1 == 0:
raise (MyException('Please feed data with length > 0'))
if n1 != n:
raise (MyException(
'Please feed x and y data of equal length'))
if len(y.shape) == 1:
y = np.matrix(y).reshape((n, 1))
como = trim_mom(x, y, locest, order, trimming, option,
biascorr)
self.co_moment_ = como
if (biascorr and (order > 2)):
como *= ntrim
if mode == 'com':
self.moment_ = como
if self.mode in ('c*k', 'cos'):
x2sd = np.sqrt(
trim_mom(x, x, locest, 2, trimming, option, biascorr))
y2sd = np.sqrt(
trim_mom(y, y, locest, 2, trimming, option, biascorr))
if ((self.mode == 'c*k') and biascorr
): # biascorr is only exact if standardized
como -= como / (ntrim**2)
como -= 3 * (ntrim - 1)**2.0 / ((ntrim - 2) *
(ntrim - 3))
if mode in ['corr', 'continuum']:
# if self.center=='mean':
# yvar = trimvar(y,trimming)*ntrim/(ntrim-1)
#
# elif self.center=='median':
# yvar = srs.mad(y)
ymom = trim_mom(y, y, locest, order, trimming, option,
biascorr)
self.y_moment_ = ymom
# Distance based metrics
elif self.est == 'distance':
if 'dmetric' not in kwargs:
dmetric = 'euclidean'
else:
dmetric = kwargs.get('dmetric')
if (mode in ['ballcov', 'mdd', 'mdc']):
if 'y' not in kwargs:
raise (MyException('Please supply second data vector'))
else:
y = kwargs.get('y')
n1 = len(y)
if n1 == 0:
raise (MyException('Please feed data with length > 0'))
if n1 != n:
raise (MyException(
'Please feed x and y data of equal length'))
if (mode in ['mdd', 'mdc']):
como = np.sqrt(
difference_divergence(x,
y,
center=self.center,
trimming=trimming,
biascorr=biascorr))
self.co_moment_ = como
if mode == 'mdd':
self.moment_ = como
else:
xmom = difference_divergence(x,
x,
center=self.center,
trimming=trimming,
biascorr=biascorr)
ymom = difference_divergence(y,
y,
center=self.center,
trimming=trimming,
biascorr=biascorr)
self.x_moment_ = xmom
self.y_moment_ = ymom
mode = 'corr'
else:
dmy, n2 = distance_matrix_centered(y,
biascorr=biascorr,
trimming=trimming,
center=self.center,
dmetric=dmetric)
bcov_res = Ball.bcov_test(x, y, num_permutations=0)[0]
self.moment_ = bcov_res
elif (calcmode == 'fast' and self.center == 'mean'
and trimming == 0 and order == 2):
if mode != 'com':
if biascorr:
xmom = np.sqrt(dc.u_distance_covariance_sqr(x, x))
else:
xmom = np.sqrt(dc.distance_covariance_sqr(x, x))
self.moment_ = xmom
if mode != 'mom':
if 'y' not in kwargs:
raise (MyException('Please supply second data vector'))
else:
y = kwargs.get('y')
n1 = len(y)
if biascorr:
como = np.sqrt(dc.u_distance_covariance_sqr(x, y))
else:
como = np.sqrt(dc.distance_covariance_sqr(x, y))
if mode == 'com':
self.co_moment_ = como
self.moment_ = como
elif mode in ['corr', 'continuum', 'cos', 'c*k']:
if biascorr:
ymom = np.sqrt(dc.u_distance_covariance_sqr(y, y))
else:
ymom = np.sqrt(dc.distance_covariance_sqr(y, y))
self.y_moment_ = ymom
else:
dmx, n1 = distance_matrix_centered(x,
biascorr=biascorr,
trimming=trimming,
center=self.center,
dmetric=dmetric)
# Variance
if mode != 'com':
xmom = distance_moment(dmx,
dmx,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=order,
option=option)
self.x_moment_ = xmom
if self.mode in ('std', 'skew', 'kurt'):
x2mom = distance_moment(dmx,
dmx,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=2,
option=option)
xmom /= x2mom**num_power
if mode == 'mom':
self.moment_ = xmom
if mode != 'mom':
if 'y' not in kwargs:
raise (MyException('Please supply second data vector'))
else:
y = kwargs.get('y')
n1 = len(y)
if n1 == 0:
raise (MyException('Please feed data with length > 0'))
if n1 != n:
raise (MyException(
'Please feed x and y data of equal length'))
dmy, n2 = distance_matrix_centered(y,
biascorr=biascorr,
trimming=trimming,
center=self.center,
dmetric=dmetric)
como = distance_moment(dmx,
dmy,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=order,
option=option)
self.co_moment_ = como
if mode == 'com':
self.moment_ = como
if self.mode in ('c*k', 'cos'):
x2sd = distance_moment(dmx,
dmx,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=2,
option=option)
x2sd = np.sqrt(x2sd)
y2sd = distance_moment(dmy,
dmy,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=2,
option=option)
y2sd = np.sqrt(y2sd)
if mode in ['corr', 'continuum', 'c*k', 'cos']:
ymom = distance_moment(dmy,
dmy,
n1=n,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=order,
option=option)
self.y_moment_ = ymom
if mode == 'corr':
como /= (np.sqrt(xmom) * np.sqrt(ymom))
self.moment_ = como
elif mode == 'continuum':
como *= como * (np.sqrt(xmom)**(alpha - 1))
self.moment_ = como
if (self.mode in ('c*k', 'cos') and standardized):
iter_stop_2 = option
iter_stop_1 = order - option
como /= np.power(x2sd, iter_stop_1)
como /= np.power(y2sd, iter_stop_2)
if ((self.mode == 'c*k')
and not Fisher): # Not very meaningful for co-moment
como += 3
self.moment_ = como
if type(self.moment_) == np.ndarray:
self.moment_ = self.moment_[0]
return (self.moment_)
def test_distance_correlation_comparison(self):
"""
Compare all implementations of the distance covariance and correlation.
"""
arr1 = np.array(((1.,), (2.,), (3.,), (4.,), (5.,), (6.,)))
arr2 = np.array(((1.,), (7.,), (5.,), (5.,), (6.,), (2.,)))
for method in dcor.DistanceCovarianceMethod:
with self.subTest(method=method):
compile_modes = [dcor.CompileMode.AUTO,
dcor.CompileMode.COMPILE_CPU,
dcor.CompileMode.NO_COMPILE]
if method is not dcor.DistanceCovarianceMethod.NAIVE:
compile_modes += [dcor.CompileMode.COMPILE_CPU]
for compile_mode in compile_modes:
with self.subTest(compile_mode=compile_mode):
# Unbiased versions
covariance = dcor.u_distance_covariance_sqr(
arr1, arr2, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(covariance, -0.88889, places=5)
correlation = dcor.u_distance_correlation_sqr(
arr1, arr2, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(correlation, -0.41613, places=5)
covariance = dcor.u_distance_covariance_sqr(
arr1, arr1, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(covariance, 1.55556, places=5)
correlation = dcor.u_distance_correlation_sqr(
arr1, arr1, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(correlation, 1, places=5)
covariance = dcor.u_distance_covariance_sqr(
arr2, arr2, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(covariance, 2.93333, places=5)
correlation = dcor.u_distance_correlation_sqr(
arr2, arr2, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(correlation, 1, places=5)
stats = dcor.u_distance_stats_sqr(
arr1, arr2, method=method,
compile_mode=compile_mode)
np.testing.assert_allclose(
stats, (-0.88889, -0.41613, 1.55556, 2.93333),
rtol=1e-4)
# Biased
covariance = dcor.distance_covariance_sqr(
arr1, arr2, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(covariance, 0.68519, places=5)
correlation = dcor.distance_correlation_sqr(
arr1, arr2, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(correlation, 0.30661, places=5)
covariance = dcor.distance_covariance_sqr(
arr1, arr1, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(covariance, 1.70679, places=5)
correlation = dcor.distance_correlation_sqr(
arr1, arr1, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(correlation, 1, places=5)
covariance = dcor.distance_covariance_sqr(
arr2, arr2, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(covariance, 2.92593, places=5)
correlation = dcor.distance_correlation_sqr(
arr2, arr2, method=method,
compile_mode=compile_mode)
self.assertAlmostEqual(correlation, 1, places=5)
stats = dcor.distance_stats_sqr(
arr1, arr2, method=method,
compile_mode=compile_mode)
np.testing.assert_allclose(
stats, (0.68519, 0.30661, 1.70679, 2.92593),
rtol=1e-4)
def __call__(self, *, selected_variable, y, **kwargs):
bound = self.chi_bound(selected_variable, y, self.significance)
return dcor.u_distance_covariance_sqr(selected_variable, y) < bound
def fit(self, x, **kwargs):
"""
Fit a dicomo model
Parameters
------------
X : numpy array or pandas DataFrame
input data
Remarks:
The `fit` function takes several optional input arguments. These are options that
apply to individual settings:
`biascorr`, Bool, when `True`, correct for bias. For classical product-moment statistics, this
is the small sample correction. For energy statistics, this leads to the estimates
that are unbiased in high dimension
(but not preferred in low dimension).
`alpha`, float, parameter for continuum association. Has no effect for other options.
`option`, int, determines which higher order co-moment to calculate,
e.g. for co-skewness, `option=1` calculates CoS(x,x,y)
`order`, int, which order (co-)moment to calculate. Can be overruled by `mode`,
e.g. if `mode='var'`, `order` is set to 2.
`calcmode`, str, to use the efficient or naive algorithm to calculate distance statistics. Defaults to `fast` when available.
"""
if 'trimming' not in kwargs:
trimming = 0
else:
trimming = kwargs.get('trimming')
if 'biascorr' not in kwargs:
biascorr = False
else:
biascorr = kwargs.get('biascorr')
if 'alpha' not in kwargs:
alpha = 1
else:
alpha = kwargs.get('alpha')
if 'dmetric' not in kwargs:
dmetric = 'euclidean'
else:
dmetric = kwargs.get('dmetric')
if 'calcmode' not in kwargs:
calcmode = 'fast'
else:
calcmode = kwargs.get('calcmode')
if 'order' not in kwargs:
order = 2
else:
order = kwargs.get('order')
if self.mode == 'var':
mode = 'mom'
order = 2
elif self.mode == 'cov':
mode = 'com'
order = 2
elif self.mode == 'std':
self.center = 'mean'
mode = 'mom'
order = 2
num_power = 0.5
elif self.mode == 'skew':
self.center = 'mean'
mode = 'mom'
order = 3
num_power = 1.5
elif self.mode == 'kurt':
self.center = 'mean'
mode = 'mom'
order = 4
num_power = 2
if 'Fisher' not in kwargs:
Fisher = True
else:
Fisher = kwargs.get('Fisher')
elif self.mode == 'cos':
self.center = 'mean'
mode = 'com'
order = 3
if 'standardized' not in kwargs:
standardized = True
else:
standardized = kwargs.get('standardized')
elif self.mode == 'M3':
self.center = 'mean'
mode = 'com'
order = 3
if 'standardized' not in kwargs:
standardized = False
else:
standardized = kwargs.get('standardized')
elif self.mode == 'c*k':
self.center = 'mean'
mode = 'com'
order = 4
if 'standardized' not in kwargs:
standardized = True
else:
standardized = kwargs.get('standardized')
if 'Fisher' not in kwargs:
Fisher = True
else:
Fisher = kwargs.get('Fisher')
else:
mode = self.mode
if order > 2:
if 'option' not in kwargs:
option = 1
else:
option = kwargs.get('option')
else:
option = 0
n = len(x)
ntrim = round(n * (1 - trimming))
if len(x.shape) == 1:
x = np.matrix(x).reshape((n, 1))
if mode == 'corr':
alpha = 1
if n == 0:
raise (MyException('Please feed data with length > 0'))
if self.center == 'median':
locest = np.median
else:
locest = trim_mean
# Classical variance, covariance and continuum as well as robust alternatives
if self.est == 'arithmetic':
# Variance
if mode != 'com':
# if self.center=='mean':
# xvar = trimvar(x,trimming)*ntrim/(ntrim-1)
# elif self.center=='median':
# xvar = srs.mad(x)**2
xmom = trim_mom(x, x, locest, order, trimming, option,
biascorr)
self.x_moment_ = xmom
if self.mode in ('std', 'skew', 'kurt'):
x2mom = trim_mom(x, x, locest, 2, trimming, option, False)
xmom /= (x2mom**num_power)
if biascorr:
if self.mode == 'skew':
xmom *= (ntrim - 1)**2
xmom /= np.sqrt(ntrim**2 - ntrim)
elif self.mode == 'kurt':
xmom = xmom * ntrim - xmom / ntrim
xmom -= 3 * (ntrim - 1)**2.0 / ((ntrim - 2) *
(ntrim - 3))
if not Fisher:
xmom += 3
if mode == 'mom':
self.moment_ = xmom
# Covariance or continuum
if mode != 'mom':
if 'y' not in kwargs:
raise (MyException('Please supply second data vector'))
else:
y = kwargs.get('y')
n1 = len(y)
if n1 == 0:
raise (MyException('Please feed data with length > 0'))
if n1 != n:
raise (MyException(
'Please feed x and y data of equal length'))
if len(y.shape) == 1:
y = np.matrix(y).reshape((n, 1))
como = trim_mom(x, y, locest, order, trimming, option,
biascorr)
self.co_moment_ = como
if (biascorr and (order > 2)):
como *= ntrim
if mode == 'com':
self.moment_ = como
if self.mode in ('c*k', 'cos'):
x2sd = np.sqrt(
trim_mom(x, x, locest, 2, trimming, option, biascorr))
y2sd = np.sqrt(
trim_mom(y, y, locest, 2, trimming, option, biascorr))
if ((self.mode == 'c*k') and biascorr
): # biascorr is only exact if standardized
como -= como / (ntrim**2)
como -= 3 * (ntrim - 1)**2.0 / ((ntrim - 2) *
(ntrim - 3))
if mode in ['corr', 'continuum']:
# if self.center=='mean':
# yvar = trimvar(y,trimming)*ntrim/(ntrim-1)
#
# elif self.center=='median':
# yvar = srs.mad(y)
ymom = trim_mom(y, y, locest, order, trimming, option,
biascorr)
self.y_moment_ = ymom
# Distance based metrics
elif self.est == 'distance':
if 'dmetric' not in kwargs:
dmetric = 'euclidean'
else:
dmetric = kwargs.get('dmetric')
if (mode in ['ballcov', 'mdd', 'mdc']):
if 'y' not in kwargs:
raise (MyException('Please supply second data vector'))
else:
y = kwargs.get('y')
n1 = len(y)
if n1 == 0:
raise (MyException('Please feed data with length > 0'))
if n1 != n:
raise (MyException(
'Please feed x and y data of equal length'))
if (mode in ['mdd', 'mdc']):
como = np.sqrt(
difference_divergence(x,
y,
center=self.center,
trimming=trimming,
biascorr=biascorr))
self.co_moment_ = como
if mode == 'mdd':
self.moment_ = como
else:
xmom = difference_divergence(x,
x,
center=self.center,
trimming=trimming,
biascorr=biascorr)
ymom = difference_divergence(y,
y,
center=self.center,
trimming=trimming,
biascorr=biascorr)
self.x_moment_ = xmom
self.y_moment_ = ymom
mode = 'corr'
else:
dmy, n2 = distance_matrix_centered(y,
biascorr=biascorr,
trimming=trimming,
center=self.center,
dmetric=dmetric)
bcov_res = Ball.bcov_test(x, y, num_permutations=0)[0]
self.moment_ = bcov_res
elif (calcmode == 'fast' and self.center == 'mean'
and trimming == 0 and order == 2):
if mode != 'com':
if biascorr:
xmom = np.sqrt(dc.u_distance_covariance_sqr(x, x))
else:
xmom = np.sqrt(dc.distance_covariance_sqr(x, x))
self.moment_ = xmom
if mode != 'mom':
if 'y' not in kwargs:
raise (MyException('Please supply second data vector'))
else:
y = kwargs.get('y')
n1 = len(y)
if biascorr:
como = np.sqrt(dc.u_distance_covariance_sqr(x, y))
else:
como = np.sqrt(dc.distance_covariance_sqr(x, y))
if mode == 'com':
self.co_moment_ = como
self.moment_ = como
elif mode in ['corr', 'continuum', 'cos', 'c*k']:
if biascorr:
ymom = np.sqrt(dc.u_distance_covariance_sqr(y, y))
else:
ymom = np.sqrt(dc.distance_covariance_sqr(y, y))
self.y_moment_ = ymom
else:
dmx, n1 = distance_matrix_centered(x,
biascorr=biascorr,
trimming=trimming,
center=self.center,
dmetric=dmetric)
# Variance
if mode != 'com':
xmom = distance_moment(dmx,
dmx,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=order,
option=option)
self.x_moment_ = xmom
if self.mode in ('std', 'skew', 'kurt'):
x2mom = distance_moment(dmx,
dmx,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=2,
option=option)
xmom /= x2mom**num_power
if mode == 'mom':
self.moment_ = xmom
if mode != 'mom':
if 'y' not in kwargs:
raise (MyException('Please supply second data vector'))
else:
y = kwargs.get('y')
n1 = len(y)
if n1 == 0:
raise (MyException('Please feed data with length > 0'))
if n1 != n:
raise (MyException(
'Please feed x and y data of equal length'))
dmy, n2 = distance_matrix_centered(y,
biascorr=biascorr,
trimming=trimming,
center=self.center,
dmetric=dmetric)
como = distance_moment(dmx,
dmy,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=order,
option=option)
self.co_moment_ = como
if mode == 'com':
self.moment_ = como
if self.mode in ('c*k', 'cos'):
x2sd = distance_moment(dmx,
dmx,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=2,
option=option)
x2sd = np.sqrt(x2sd)
y2sd = distance_moment(dmy,
dmy,
n1=n1,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=2,
option=option)
y2sd = np.sqrt(y2sd)
if mode in ['corr', 'continuum', 'c*k', 'cos']:
ymom = distance_moment(dmy,
dmy,
n1=n,
biascorr=biascorr,
center=self.center,
trimming=trimming,
order=order,
option=option)
self.y_moment_ = ymom
if mode == 'corr':
como /= (np.sqrt(xmom) * np.sqrt(ymom))
self.moment_ = como
elif mode == 'continuum':
como *= como * (np.sqrt(xmom)**(alpha - 1))
self.moment_ = como
if (self.mode in ('c*k', 'cos') and standardized):
iter_stop_2 = option
iter_stop_1 = order - option
como /= np.power(x2sd, iter_stop_1)
como /= np.power(y2sd, iter_stop_2)
if ((self.mode == 'c*k')
and not Fisher): # Not very meaningful for co-moment
como += 3
self.moment_ = como
if type(self.moment_) == np.ndarray:
self.moment_ = self.moment_[0]
return (self.moment_)
