Simplified _hmns_scale

master
Per A Brodtkorb 8 years ago
parent 669fadc9a5
commit b0ad9c7438

@ -673,24 +673,19 @@ class Kernel(object):
return self.hns(data) / 0.93 return self.hns(data) / 0.93
def _hmns_scale(self, d): def _hmns_scale(self, d):
name = self.name[:4].lower() name = self.name
if name == 'epan': # Epanechnikov kernel short_name = name[:4].lower()
a = (8.0 * (d + 4.0) * (2 * sqrt(pi)) ** d / scale_dict = dict(epan=(8.0 * (d + 4.0) * (2 * sqrt(pi)) ** d /
sphere_volume(d)) ** (1. / (4.0 + d)) sphere_volume(d)) ** (1. / (4.0 + d)),
elif name == 'biwe': # Bi-weight kernel biwe=2.7779, triw=3.12,
a = 2.7779 gaus=(4.0 / (d + 2.0)) ** (1. / (d + 4.0)))
if d > 2: if short_name not in scale_dict:
raise NotImplementedError('Not implemented for d>2')
elif name == 'triw': # Triweight
a = 3.12
if d > 2:
raise NotImplementedError('not implemented for d>2')
elif name == 'gaus': # Gaussian kernel
a = (4.0 / (d + 2.0)) ** (1. / (d + 4.0))
else:
raise NotImplementedError('Hmns bandwidth not implemented for ' raise NotImplementedError('Hmns bandwidth not implemented for '
'kernel {}.'.format(name)) 'kernel {}.'.format(name))
return a if d > 2 and short_name in ['biwe', 'triw']:
raise NotImplementedError('Not implemented for d>2 and '
'kernel {}'.format(name))
return scale_dict[short_name]
def hmns(self, data): def hmns(self, data):
"""Returns Multivariate Normal Scale Estimate of Smoothing Parameter. """Returns Multivariate Normal Scale Estimate of Smoothing Parameter.
@ -865,8 +860,7 @@ class Kernel(object):
# Kernel other than Gaussian scale bandwidth # Kernel other than Gaussian scale bandwidth
h1 = h1 * (ste_constant / ste_constant2) ** (1.0 / 5) h1 = h1 * (ste_constant / ste_constant2) ** (1.0 / 5)
if count >= maxit: _assert_warn(count < maxit, 'The obtained value did not converge.')
warnings.warn('The obtained value did not converge.')
h[dim] = h1 h[dim] = h1
# end for dim loop # end for dim loop

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