hselect.py

'''
Functions ported from the R package sm.

Implements different bandwidth selection methods, including:
- Scott's rule of thumb
- Silverman's rule of thumb
- Sheather-Jones estimator
'''

import numpy as np
import distributions as distr


__all__ = ['wmean',
           'wvar',
           'dnorm',
           'hsilverman',
           'hscott',
           'hnorm',
           'hsj']


def wmean(x, w):
    '''
    Weighted mean
    '''
    return sum(x * w) / float(sum(w))


def wvar(x, w):
    '''
    Weighted variance
    '''
    return sum(w * (x - wmean(x, w)) ** 2) / float(sum(w) - 1)


def dnorm(x):
    return distr.normal.pdf(x, 0.0, 1.0)


def bowman(x):
    pass
    # TODO: implement?
    #hx = median(abs(x - median(x))) / 0.6745 * (4 / 3 / r.n) ^ 0.2
    #hy = median(abs(y - median(y))) / 0.6745 * (4 / 3 / r.n) ^ 0.2
    #h = sqrt(hy * hx)


def hsilverman(x, weights=None):
    IQR = np.percentile(x, 75) - np.percentile(x, 25)
    A = min(np.std(x, ddof=1), IQR / 1.349)

    if weights is None:
        weights = np.ones(len(x))
    n = float(sum(weights))

    return 0.9 * A * n ** (-0.2)


def hscott(x, weights=None):

    IQR = np.percentile(x, 75) - np.percentile(x, 25)
    A = min(np.std(x, ddof=1), IQR / 1.349)

    if weights is None:
        weights = np.ones(len(x))
    n = float(sum(weights))

    return 1.059 * A * n ** (-0.2)


def hnorm(x, weights=None):
    '''
    Bandwidth estimate assuming f is normal. See paragraph 2.4.2 of
    Bowman and Azzalini[1]_ for details.

    References
    ----------
    .. [1] Applied Smoothing Techniques for Data Analysis: the
        Kernel Approach with S-Plus Illustrations.
        Bowman, A.W. and Azzalini, A. (1997).
        Oxford University Press, Oxford
    '''

    x = np.asarray(x)

    if weights is None:
        weights = np.ones(len(x))

    n = float(sum(weights))

    if len(x.shape) == 1:
        sd = np.sqrt(wvar(x, weights))
        return sd * (4 / (3 * n)) ** (1 / 5.0)

    # TODO: make this work for more dimensions
    # ((4 / (p + 2) * n)^(1 / (p+4)) * sigma_i
    if len(x.shape) == 2:
        ndim = x.shape[1]
        sd = np.sqrt(np.apply_along_axis(wvar, 1, x, weights))
        return (4.0 / ((ndim + 2.0) * n) ** (1.0 / (ndim + 4.0))) * sd


def hsj(x, weights=None):
    '''
    Sheather-Jones bandwidth estimator [1]_.

    References
    ----------
    .. [1] A reliable data-based bandwidth selection method for kernel
        density estimation. Simon J. Sheather and Michael C. Jones.
        Journal of the Royal Statistical Society, Series B. 1991
    '''

    h0 = hnorm(x)
    v0 = sj(x, h0)

    if v0 > 0:
        hstep = 1.1
    else:
        hstep = 0.9

    h1 = h0 * hstep
    v1 = sj(x, h1)

    while v1 * v0 > 0:
        h0 = h1
        v0 = v1
        h1 = h0 * hstep
        v1 = sj(x, h1)

    return h0 + (h1 - h0) * abs(v0) / (abs(v0) + abs(v1))


def sj(x, h):
    '''
    Equation 12 of Sheather and Jones [1]_

    References
    ----------
    .. [1] A reliable data-based bandwidth selection method for kernel
        density estimation. Simon J. Sheather and Michael C. Jones.
        Journal of the Royal Statistical Society, Series B. 1991
    '''
    phi6 = lambda x: (x ** 6 - 15 * x ** 4 + 45 * x ** 2 - 15) * dnorm(x)
    phi4 = lambda x: (x ** 4 - 6 * x ** 2 + 3) * dnorm(x)

    n = len(x)
    one = np.ones((1, n))

    lam = np.percentile(x, 75) - np.percentile(x, 25)
    a = 0.92 * lam * n ** (-1 / 7.0)
    b = 0.912 * lam * n ** (-1 / 9.0)

    W = np.tile(x, (n, 1))
    W = W - W.T

    W1 = phi6(W / b)
    tdb = np.dot(np.dot(one, W1), one.T)
    tdb = -tdb / (n * (n - 1) * b ** 7)

    W1 = phi4(W / a)
    sda = np.dot(np.dot(one, W1), one.T)
    sda = sda / (n * (n - 1) * a ** 5)

    alpha2 = 1.357 * (abs(sda / tdb)) ** (1 / 7.0) * h ** (5 / 7.0)

    W1 = phi4(W / alpha2)
    sdalpha2 = np.dot(np.dot(one, W1), one.T)
    sdalpha2 = sdalpha2 / (n * (n - 1) * alpha2 ** 5)

    return (distr.normal.pdf(0, 0, np.sqrt(2)) /
            (n * abs(sdalpha2[0, 0]))) ** 0.2 - h