Sampling_multi.py

# uncompyle6 version 3.5.0
# Python bytecode 2.7 (62211)
# Decompiled from: Python 2.7.5 (default, Aug  7 2019, 00:51:29) 
# [GCC 4.8.5 20150623 (Red Hat 4.8.5-39)]
# Embedded file name: C:\Users\depila\Desktop\Graduate Research\GeneticAlgorithims\CreateNSGA2\NSGAOutline\Sampling_multi.py
# Compiled at: 2021-03-27 01:29:52
"""!
@file src/Sampling.py
@package Gnowee

@defgroup Sampling Sampling

@brief Different methods to perform phase space sampling and random walks.

Design of experiment and phase space sampling methods.  Includes some
vizualization tools.

Dependencies on pyDOE.

@author James Bevins

@date 23May17

@copyright <a href='../../licensing/COPYRIGHT'>&copy; 2017 UC
            Berkeley Copyright and Disclaimer Notice</a>
@license <a href='../../licensing/LICENSE'>GNU GPLv3.0+ </a>
"""
import math, random, bisect, matplotlib.pyplot as plt, numpy as np
from scipy.special import gamma
from pyDOE import lhs
from numpy.random import rand, randn, choice
from GnoweeUtilities_multi import Switch

def initial_samples(lb, ub, method, numSamp):
    r"""!
    @ingroup Sampling

    Generate a set of samples in a given phase space. The current methods
    available are 'random', 'nolh', 'nolh-rp', 'nolh-cdr', 'lhc', or
    'rand-wor'.

    @param lb: \e array 

        The lower bounds of the design variable(s). 

    @param ub: \e array 

        The upper bounds of the design variable(s). 

    @param  method: \e string 

        String representing the chosen sampling method. Valid options are:
        'random', 'nolh', 'nolh-rp', 'nolh-cdr', 'lhc', 'random-wor'. 

    @param numSamp: \e integer 

        The number of samples to be generated.  Ignored for nolh algorithms. 

    @return \e array: The list of coordinates for the sampled phase space. 

    """
    if not len(lb) == len(ub):
        raise AssertionError('Lower and upper bounds have different #s of design variables in initial_samples function.')
        assert method == 'random' or method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr' or method == 'lhc' or method == 'rand-wor', 'An invalid method was specified for the initial_samples.'
        assert (method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr') and len(ub) >= 2 and len(ub) <= 29, 'The Phase space dimensions are outside of the bounds for initial_samples.'
    for case in Switch(method):
        if case('random'):
            s = np.zeros((numSamp, len(lb)))
            for i in range(0, numSamp, 1):
                s[i, :] = lb + (ub - lb) * rand(len(lb))

            break
        if case('rand-wor'):
            s = np.zeros((numSamp, len(lb)))
            for i in range(0, numSamp, 1):
                s[i, :] = choice(len(ub), size=len(ub), replace=False)

            break
        if case('nolh'):
            dim = len(ub)
            m, q, r = params(dim)
            conf = range(q)
            if r != 0:
                remove = range(dim - r, dim)
                nolh = NOLH(conf, remove)
            else:
                nolh = NOLH(conf)
            s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))
                         ])
            break
        if case('nolh-rp'):
            dim = len(ub)
            m, q, r = params(dim)
            conf = random.sample(range(q), q)
            if r != 0:
                remove = random.sample(range(q - 1), r)
                nolh = NOLH(conf, remove)
            else:
                nolh = NOLH(conf)
            s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))
                         ])
            break
        if case('nolh-cdr'):
            dim = len(ub)
            m, q, r = params(dim)
            conf, remove = get_cdr_permutations(len(ub))
            if remove != []:
                nolh = NOLH(conf, remove)
            else:
                nolh = NOLH(conf)
            s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))
                         ])
            break
        if case('lhc'):
            tmp = lhs(len(lb), samples=numSamp, criterion='center')
            s = np.array([ list(lb + (ub - lb) * tmp[i, :]) for i in range(len(tmp[:, 0]))
                         ])
            break
        if case():
            print 'Somehow you evaded my assert statement - good job!',
            print ' However, you still need to use a valid method string.'

    return s


def plot_samples(s):
    r"""!
    @ingroup Sampling

    Plot the first 2 and 3 dimensions on the sample distribution. Can't plot
    the full hyperspace yet.  Produces a very simple plot for visualizing the
    difference in the sampling methods.

    @param s: \e array 

        The list of coordinates for the sampled phase space. 

    """
    assert len(s[0, :]) >= 2, ('The Phase space dimensions are less than two.', ' Need at least two to plot.')
    fig = plt.figure(1)
    if len(s[0, :]) >= 3:
        ax = fig.add_subplot(111, projection='3d')
        ax.scatter(s[:, 0], s[:, 1], s[:, 2])
    fig = plt.figure(2)
    plt.scatter(s[:, 0], s[:, 1])
    plt.show()


def levy(nc, nr=0, alpha=1.5, gam=1, n=1):
    """!
    @ingroup Sampling

    Sample the Levy distribution given by

    \x0c$ L_{\x07lpha,\\gamma}(z)=\x0crac{1}{\\pi}\\int \\limits_{0}^{+\\infty}
        e^{-\\gamma q^{\x07lpha}} \\cos(qz) dq \x0c$

    using the Mantegna algoritm outlined in "Fast, Accurate Algorithm for
    Numerical Simulation of Levy Stable Stochastic Processes."

    @param nc: \\e integer 

        The number of columns of Levy values for the return array.
    @param nr \\e integer 

        The number of rows of Levy values for the return array. 

    @param alpha \\e float 

        Levy exponent - defines the index of the distribution and controls
        scale properties of the stochastic process. 

    @param gam: \\e float 

        Gamma - Scale unit of process for Levy flights. 

    @param n: \\e integer 

        Number of independent variables - can be used to reduce Levy
        flight sampling variance. 

    @return \\e array:  Array representing the levy flights for each nest.
    """
    assert alpha > 0.3 and alpha < 1.99, 'Valid range for alpha is [0.3:1.99].'
    assert gam >= 0, 'Gamma must be positive'
    assert n >= 1, 'n Must be positive'
    invalpha = 1.0 / alpha
    sigx = (gamma(1.0 + alpha) * np.sin(np.pi * alpha / 2.0) / (gamma((1.0 + alpha) / 2) * alpha * 2.0 ** ((alpha - 1.0) / 2.0))) ** invalpha
    if nr != 0:
        v = sigx * randn(n, nr, nc) / abs(randn(n, nr, nc)) ** invalpha
    else:
        v = sigx * randn(n, nc) / abs(randn(n, nc)) ** invalpha
    kappa = alpha * gamma((alpha + 1.0) / (2.0 * alpha)) / gamma(invalpha) * (alpha * gamma((alpha + 1.0) / 2.0) / (gamma(1.0 + alpha) * np.sin(np.pi * alpha / 2.0))) ** invalpha
    p = [-17.7767, 113.3855, -281.5879, 337.5439, -193.5494, 44.8754]
    c = np.polyval(p, alpha)
    w = ((kappa - 1.0) * np.exp(-abs(v) / c) + 1.0) * v
    if n > 1:
        z = 1 / n ** invalpha * sum(w)
    else:
        z = w
    z = gam ** invalpha * z
    if nr != 0:
        z = z.reshape(nr, nc)
    else:
        z = z.reshape(nc)
    return z


def tlf(numRow=1, numCol=1, alpha=1.5, gam=1.0, cutPoint=10.0):
    r"""!
    @ingroup Sampling

    Samples from a truncated Levy flight distribution (TLF) according to
    Manegna, "Stochastic Process  with Ultraslow Convergence to a Gaussian:
    The Truncated Levy Flight" to map a levy distribution onto the interval
    [0,1].

    @param numRow: \e integer 

        Number of rows of Levy flights to sample. 

    @param numCol: \e integer 

        Number of columns of Levy flights to sample. 

    @param alpha: \e float 

        Levy exponent - defines the index of the distribution and controls
        scale properties of the stochastic process. 

    @param gam: \e float 

        Gamma - Scale unit of process for Levy flights. 

    @param cutPoint: \e float 

        Point at which to cut sampled Levy values and resample. 

    @return \e array: Array representing the levy flights on the interval
        (0,1). 

    """
    z = abs(levy(numRow, numCol) / cutPoint).reshape(numRow, numCol)
    for i in range(len(z)):
        for j in range(len(z[i])):
            while z[(i, j)] > 1:
                z[(i, j)] = abs(levy(1, 1, alpha=alpha, gam=gam) / cutPoint).reshape(1)

    return z


def NOLH(conf, remove=None):
    """!
    @ingroup Sampling

    This library allows to generate Nearly Orthogonal Latin Hypercubes (NOLH)
    according to Cioppa (2007) and De Rainville et al. (2012) and reference
    therein.

    https://pypi.python.org/pypi/pynolh

    Constructs a Nearly Orthogonal Latin Hypercube (NOLH) of order *m* from
    a configuration vector *conf*. The configuration vector may contain either
    the numbers in $ [0 q-1] $ or $ [1 q] $ where $ q = 2^{m-1} $.
    The columns to be *removed* are also in $ [0 d-1] $ or $ [1 d] $
    where

    $ d = m + \x08inom{m-1}{2} $

    is the NOLH dimensionality.

    The whole library is incorporated here with minimal modification for
    commonality and consolidation of methods.

    @param conf: \\e array 

        Configuration vector. 

    @param remove: \\e array 

        Array containing the indexes of the colummns to be removed from conf
        vector. 

    @return \\e array: Array containing nearly orthogonal latin hypercube
         sampling. 

    """
    I = np.identity(2, dtype=int)
    R = np.array(((0, 1), (1, 0)), dtype=int)
    if 0 in conf:
        conf = np.array(conf) + 1
        if remove is not None:
            remove = np.array(remove) + 1
    q = len(conf)
    m = math.log(q, 2) + 1
    s = int(m + math.factorial(m - 1) / (2 * math.factorial(m - 3)))
    m = int(m)
    A = np.zeros((q, q, m - 1), dtype=int)
    for i in range(1, m):
        Ai = 1
        for j in range(1, m):
            if j < m - i:
                Ai = np.kron(Ai, I)
            else:
                Ai = np.kron(Ai, R)

        A[:, :, i - 1] = Ai

    M = np.zeros((q, s), dtype=int)
    M[:, 0] = conf
    col = 1
    for i in range(0, m - 1):
        for j in range(i + 1, m):
            if i == 0:
                M[:, col] = np.dot(A[:, :, j - 1], conf)
            else:
                M[:, col] = np.dot(A[:, :, i - 1], np.dot(A[:, :, j - 1], conf))
            col += 1

    S = np.ones((q, s), dtype=int)
    v = 1
    for i in range(1, m):
        for j in range(0, q):
            if j % 2 ** (i - 1) == 0:
                v *= -1
            S[(j, i)] = v

    col = m
    for i in range(1, m - 1):
        for j in range(i + 1, m):
            S[:, col] = S[:, i] * S[:, j]
            col += 1

    T = M * S
    keep = np.ones(s, dtype=bool)
    if remove is not None:
        keep[np.array(remove) - 1] = [
         False] * len(remove)
    return (np.concatenate((T, np.zeros((1, s)), -T), axis=0)[:, keep] + q) / (2.0 * q)


def params(dim):
    r"""!
    @ingroup Sampling

    Returns the NOLH order $m$, the required configuration length $q$
    and the number of columns to remove to obtain the desired dimensionality.

    @param dim: \e integer 

        The dimension of the space. 

    """
    m = 3
    s = 1
    q = 2 ** (m - 1)
    while s < dim:
        m += 1
        s = m + math.factorial(m - 1) / (2 * math.factorial(m - 3))
        q = 2 ** (m - 1)

    return (
     m, q, s - dim)


def get_cdr_permutations(dim):
    r"""!
    @ingroup Sampling

    Generate a set of CDR permulations for NOLH.

    @param dim: \e integer 

        The dimension of the space. 

    @return \e array: A configuration vector. 

    @return \e array: Array containing the indexes of the colummns to be
        removed from conf vector. 

    """
    assert dim >= 2 and dim <= 29, ('The Phase space dimensions are outside ', 'of the bounds for CDR Permutations.')
    C_CONF = {2: (
         [
          1, 2, 8, 4, 5, 6, 7, 3], [1, 3, 4, 6, 7]), 
       3: (
         [
          1, 2, 8, 4, 5, 6, 7, 3], [1, 2, 3, 6]), 
       4: (
         [
          1, 2, 8, 4, 5, 6, 7, 3], [1, 3, 6]), 
       5: (
         [
          1, 2, 8, 4, 5, 6, 7, 3], [1, 6]), 
       6: (
         [
          1, 2, 8, 4, 5, 6, 7, 3], [1]), 
       7: (
         [
          1, 2, 8, 4, 5, 6, 7, 3], [])}
    EA_CONF = {8: (
         [
          4, 14, 1, 2, 16, 13, 5, 8, 12, 9, 6, 7, 11, 3, 15, 10],
         [
          1, 3, 10]), 
       9: (
         [
          4, 14, 1, 2, 16, 13, 5, 8, 12, 9, 6, 7, 11, 3, 15, 10],
         [
          6, 10]), 
       10: (
          [
           4, 14, 1, 2, 16, 13, 5, 8, 12, 9, 6, 7, 11, 3, 15, 10],
          [
           10]), 
       11: (
          [
           4, 14, 1, 2, 16, 13, 5, 8, 12, 9, 6, 7, 11, 3, 15, 10], []), 
       12: (
          [
           5, 13, 19, 23, 28, 10, 12, 32, 17, 2, 30, 15, 6, 31, 21, 8,
           24, 29, 9, 14, 11, 22, 18, 25, 3, 1, 20, 7, 27, 16, 26, 4],
          [
           2, 4, 5, 11]), 
       13: (
          [
           5, 13, 19, 23, 28, 10, 12, 32, 17, 2, 30, 15, 6, 31, 21, 8,
           24, 29, 9, 14, 11, 22, 18, 25, 3, 1, 20, 7, 27, 16, 26, 4],
          [
           3, 6, 14]), 
       14: (
          [
           5, 13, 19, 23, 28, 10, 12, 32, 17, 2, 30, 15, 6, 31, 21, 8, 24,
           29, 9, 14, 11, 22, 18, 25, 3, 1, 20, 7, 27, 16, 26, 4], [4, 5]), 
       15: (
          [
           5, 13, 19, 23, 28, 10, 12, 32, 17, 2, 30, 15, 6, 31, 21, 8, 24,
           29, 9, 14, 11, 22, 18, 25, 3, 1, 20, 7, 27, 16, 26, 4], [6]), 
       16: (
          [
           5, 13, 19, 23, 28, 10, 12, 32, 17, 2, 30, 15, 6, 31, 21, 8, 24,
           29, 9, 14, 11, 22, 18, 25, 3, 1, 20, 7, 27, 16, 26, 4], []), 
       17: (
          [
           7, 8, 51, 3, 40, 44, 29, 19, 61, 43, 26, 48, 20, 52, 4, 49, 2,
           57, 31, 30, 24, 23, 56, 50, 18, 59, 63, 37, 38, 21, 54, 9, 46,
           27, 36, 1, 10, 42, 13, 55, 15, 25, 22, 45, 41, 39, 53, 34, 6, 5,
           2, 58, 16, 28, 64, 14, 47, 33, 12, 35, 62, 17, 11, 60],
          [
           8, 11, 12, 14, 17]), 
       18: (
          [
           7, 8, 51, 3, 40, 44, 29, 19, 61, 43, 26, 48, 20, 52, 4, 49, 2,
           57, 31, 30, 24, 23, 56, 50, 18, 59, 63, 37, 38, 21, 54, 9, 46,
           27, 36, 1, 10, 42, 13, 55, 15, 25, 22, 45, 41, 39, 53, 34, 6, 5,
           2, 58, 16, 28, 64, 14, 47, 33, 12, 35, 62, 17, 11, 60],
          [
           8, 11, 12, 17]), 
       19: (
          [
           7, 8, 51, 3, 40, 44, 29, 19, 61, 43, 26, 48, 20, 52, 4, 49, 2,
           57, 31, 30, 24, 23, 56, 50, 18, 59, 63, 37, 38, 21, 54, 9, 46,
           27, 36, 1, 10, 42, 13, 55, 15, 25, 22, 45, 41, 39, 53, 34, 6, 5,
           2, 58, 16, 28, 64, 14, 47, 33, 12, 35, 62, 17, 11, 60],
          [
           10, 15, 22]), 
       20: (
          [
           7, 8, 51, 3, 40, 44, 29, 19, 61, 43, 26, 48, 20, 52, 4, 49, 2,
           57, 31, 30, 24, 23, 56, 50, 18, 59, 63, 37, 38, 21, 54, 9, 46,
           27, 36, 1, 10, 42, 13, 55, 15, 25, 22, 45, 41, 39, 53, 34, 6, 5,
           2, 58, 16, 28, 64, 14, 47, 33, 12, 35, 62, 17, 11, 60],
          [
           8, 12]), 
       21: (
          [
           7, 8, 51, 3, 40, 44, 29, 19, 61, 43, 26, 48, 20, 52, 4, 49, 2,
           57, 31, 30, 24, 23, 56, 50, 18, 59, 63, 37, 38, 21, 54, 9, 46,
           27, 36, 1, 10, 42, 13, 55, 15, 25, 22, 45, 41, 39, 53, 34, 6, 5,
           2, 58, 16, 28, 64, 14, 47, 33, 12, 35, 62, 17, 11, 60], [15]), 
       22: (
          [
           7, 8, 51, 3, 40, 44, 29, 19, 61, 43, 26, 48, 20, 52, 4, 49, 2,
           57, 31, 30, 24, 23, 56, 50, 18, 59, 63, 37, 38, 21, 54, 9, 46,
           27, 36, 1, 10, 42, 13, 55, 15, 25, 22, 45, 41, 39, 53, 34, 6, 5,
           2, 58, 16, 28, 64, 14, 47, 33, 12, 35, 62, 17, 11, 60], []), 
       23: (
          [
           9, 108, 39, 107, 62, 86, 110, 119, 46, 43, 103, 71, 123, 91, 10,
           13, 126, 63, 83, 47, 100, 54, 23, 16, 124, 45, 27, 4, 93, 74, 76,
           90, 30, 81, 77, 53, 116, 49, 104, 6, 70, 82, 26, 118, 55, 79, 32,
           109, 57, 31, 22, 101, 44, 87, 121, 7, 37, 56, 89, 115, 25, 92,
           85, 20, 58, 52, 3, 11, 106, 17, 117, 38, 78, 28, 59, 96, 18, 97,
           50, 114, 112, 60, 84, 1, 12, 61, 98, 128, 14, 42, 64, 105, 68,
           75, 111, 34, 141, 65, 99, 2, 19, 33, 35, 94, 51, 122, 127, 36,
           125, 80, 73, 8, 24, 21, 88, 48, 69, 66, 40, 15, 29, 113, 72, 5,
           95, 120, 6, 102], [18, 20, 21, 24, 27, 29]), 
       24: (
          [
           9, 108, 39, 107, 62, 86, 110, 119, 46, 43, 103, 71, 123, 91, 10,
           13, 126, 63, 83, 47, 100, 54, 23, 16, 124, 45, 27, 4, 93, 74, 76,
           90, 30, 81, 77, 53, 116, 49, 104, 6, 70, 82, 26, 118, 55, 79, 32,
           109, 57, 31, 22, 101, 44, 87, 121, 7, 37, 56, 89, 115, 25, 92,
           85, 20, 58, 52, 3, 11, 106, 17, 117, 38, 78, 28, 59, 96, 18, 97,
           50, 114, 112, 60, 84, 1, 12, 61, 98, 128, 14, 42, 64, 105, 68,
           75, 111, 34, 141, 65, 99, 2, 19, 33, 35, 94, 51, 122, 127, 36,
           125, 80, 73, 8, 24, 21, 88, 48, 69, 66, 40, 15, 29, 113, 72, 5,
           95, 120, 6, 102], [4, 15, 18, 24, 27]), 
       25: (
          [
           9, 108, 39, 107, 62, 86, 110, 119, 46, 43, 103, 71, 123, 91, 10,
           13, 126, 63, 83, 47, 100, 54, 23, 16, 124, 45, 27, 4, 93, 74, 76,
           90, 30, 81, 77, 53, 116, 49, 104, 6, 70, 82, 26, 118, 55, 79, 32,
           109, 57, 31, 22, 101, 44, 87, 121, 7, 37, 56, 89, 115, 25, 92,
           85, 20, 58, 52, 3, 11, 106, 17, 117, 38, 78, 28, 59, 96, 18, 97,
           50, 114, 112, 60, 84, 1, 12, 61, 98, 128, 14, 42, 64, 105, 68,
           75, 111, 34, 141, 65, 99, 2, 19, 33, 35, 94, 51, 122, 127, 36,
           125, 80, 73, 8, 24, 21, 88, 48, 69, 66, 40, 15, 29, 113, 72, 5,
           95, 120, 6, 102], [21, 26, 27, 29]), 
       26: (
          [
           9, 108, 39, 107, 62, 86, 110, 119, 46, 43, 103, 71, 123, 91, 10,
           13, 126, 63, 83, 47, 100, 54, 23, 16, 124, 45, 27, 4, 93, 74, 76,
           90, 30, 81, 77, 53, 116, 49, 104, 6, 70, 82, 26, 118, 55, 79, 32,
           109, 57, 31, 22, 101, 44, 87, 121, 7, 37, 56, 89, 115, 25, 92,
           85, 20, 58, 52, 3, 11, 106, 17, 117, 38, 78, 28, 59, 96, 18, 97,
           50, 114, 112, 60, 84, 1, 12, 61, 98, 128, 14, 42, 64, 105, 68,
           75, 111, 34, 141, 65, 99, 2, 19, 33, 35, 94, 51, 122, 127, 36,
           125, 80, 73, 8, 24, 21, 88, 48, 69, 66, 40, 15, 29, 113, 72, 5,
           95, 120, 6, 102], [26, 27, 29]), 
       27: (
          [
           9, 108, 39, 107, 62, 86, 110, 119, 46, 43, 103, 71, 123, 91, 10,
           13, 126, 63, 83, 47, 100, 54, 23, 16, 124, 45, 27, 4, 93, 74, 76,
           90, 30, 81, 77, 53, 116, 49, 104, 6, 70, 82, 26, 118, 55, 79, 32,
           109, 57, 31, 22, 101, 44, 87, 121, 7, 37, 56, 89, 115, 25, 92,
           85, 20, 58, 52, 3, 11, 106, 17, 117, 38, 78, 28, 59, 96, 18, 97,
           50, 114, 112, 60, 84, 1, 12, 61, 98, 128, 14, 42, 64, 105, 68,
           75, 111, 34, 141, 65, 99, 2, 19, 33, 35, 94, 51, 122, 127, 36,
           125, 80, 73, 8, 24, 21, 88, 48, 69, 66, 40, 15, 29, 113, 72, 5,
           95, 120, 6, 102], [27, 29]), 
       28: (
          [
           9, 108, 39, 107, 62, 86, 110, 119, 46, 43, 103, 71, 123, 91, 10,
           13, 126, 63, 83, 47, 100, 54, 23, 16, 124, 45, 27, 4, 93, 74, 76,
           90, 30, 81, 77, 53, 116, 49, 104, 6, 70, 82, 26, 118, 55, 79, 32,
           109, 57, 31, 22, 101, 44, 87, 121, 7, 37, 56, 89, 115, 25, 92,
           85, 20, 58, 52, 3, 11, 106, 17, 117, 38, 78, 28, 59, 96, 18, 97,
           50, 114, 112, 60, 84, 1, 12, 61, 98, 128, 14, 42, 64, 105, 68,
           75, 111, 34, 141, 65, 99, 2, 19, 33, 35, 94, 51, 122, 127, 36,
           125, 80, 73, 8, 24, 21, 88, 48, 69, 66, 40, 15, 29, 113, 72, 5,
           95, 120, 6, 102], [20]), 
       29: (
          [
           9, 108, 39, 107, 62, 86, 110, 119, 46, 43, 103, 71, 123, 91, 10,
           13, 126, 63, 83, 47, 100, 54, 23, 16, 124, 45, 27, 4, 93, 74, 76,
           90, 30, 81, 77, 53, 116, 49, 104, 6, 70, 82, 26, 118, 55, 79, 32,
           109, 57, 31, 22, 101, 44, 87, 121, 7, 37, 56, 89, 115, 25, 92,
           85, 20, 58, 52, 3, 11, 106, 17, 117, 38, 78, 28, 59, 96, 18, 97,
           50, 114, 112, 60, 84, 1, 12, 61, 98, 128, 14, 42, 64, 105, 68,
           75, 111, 34, 141, 65, 99, 2, 19, 33, 35, 94, 51, 122, 127, 36,
           125, 80, 73, 8, 24, 21, 88, 48, 69, 66, 40, 15, 29, 113, 72, 5,
           95, 120, 6, 102], [])}
    CONF = dict()
    CONF.update(C_CONF)
    CONF.update(EA_CONF)
    return (
     CONF[dim][0], CONF[dim][1])


class WeightedRandomGenerator(object):
    """!
    @ingroup Sampling
    Defines a class of weights to be used to select based on linear weighting.
    This can be on index or some form of ordinal ranking.
    """

    def __init__(self, weights):
        r"""!
        WeightedRandomGenerator class constructor.

        @param self: <em> pointer </em> 

            The WeightedRandomGenerator pointer. 

        @param weights: \e array 

            The array of weights (Higher = more likely to be selected) 

        """
        self.totals = []
        running_total = 0
        for w in weights:
            running_total += w
            self.totals.append(running_total)

    def next(self):
        r"""!
        Gets the next weight.

        @param self: <em> pointer </em> 

            The WeightedRandomGenerator pointer. 

        @return \e integer: The randomly selected index of the weights array. 

        """
        rnd = rand() * self.totals[(-1)]
        return bisect.bisect_right(self.totals, rnd)

    def __call__(self):
        r"""!
        Gets the next weight.

        @param self: <em> pointer </em> 

            The WeightedRandomGenerator pointer. 

        @return \e integer: The randomly selected index of the weights array. 

        """
        return self.next()