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@ -747,6 +747,10 @@ class Kernel(object):
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mu2, R = self.stats()[:2]
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return R / (mu2 ** (2) * n)
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def _get_g(self, k_order_2, psi_order, n, order):
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mu2 = _GAUSS_KERNEL.stats()[0]
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return (-2. * k_order_2 / (mu2 * psi_order * n)) ** (1. / (order+1))
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def hste(self, data, h0=None, inc=128, maxit=100, releps=0.01, abseps=0.0):
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'''HSTE 2-Stage Solve the Equation estimate of smoothing parameter.
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@ -809,8 +813,8 @@ class Kernel(object):
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# Step 2
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k40, k60 = _GAUSS_KERNEL.deriv4_6_8_10(0, numout=2)
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g1 = (-2 * k40 / (mu2 * psi6NS * n)) ** (1.0 / 7)
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g2 = (-2 * k60 / (mu2 * psi8NS * n)) ** (1.0 / 9)
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g1 = self._get_g(k40, psi6NS, n, order=6)
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g2 = self._get_g(k60, psi8NS, n, order=8)
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psi4 = self._estimate_psi(c, xn, g1, n, order=4)
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psi6 = self._estimate_psi(c, xn, g2, n, order=6)
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@ -1114,12 +1118,11 @@ class Kernel(object):
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h = np.zeros(d)
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hvec = hvec * (ste_constant2 / ste_constant) ** (1. / 5.)
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mu2 = _GAUSS_KERNEL.stats()[0]
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k40, k60, k80, k100 = _GAUSS_KERNEL.deriv4_6_8_10(0, numout=4)
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psi8 = 105 / (32 * sqrt(pi))
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psi12 = 3465. / (512 * sqrt(pi))
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g1 = (-2. * k60 / (mu2 * psi8 * n)) ** (1. / 9.)
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g2 = (-2. * k100 / (mu2 * psi12 * n)) ** (1. / 13.)
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g1 = self._get_g(k60, psi8, n, order=8)
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g2 = self._get_g(k100, psi12, n, order=12)
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for dim in range(d):
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s = sigmaA[dim]
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@ -1135,8 +1138,8 @@ class Kernel(object):
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psi6 = self._estimate_psi(c, xn, g1, n, order=6)
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psi10 = self._estimate_psi(c, xn, g2, n, order=10)
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g3 = (-2. * k40 / (mu2 * psi6 * n)) ** (1. / 7.)
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g4 = (-2. * k80 / (mu2 * psi10 * n)) ** (1. / 11.)
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g3 = self._get_g(k40, psi6, n, order=6)
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g4 = self._get_g(k80, psi10, n, order=10)
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psi4 = self._estimate_psi(c, xn, g3, n, order=4)
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psi8 = self._estimate_psi(c, xn, g4, n, order=8)
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