import numpy as N


def bootstrap_resample(X, n=None):
    """ Bootstrap resample an array_like
    Parameters
    ----------
    X : array_like
      data to resample
    n : int, optional
      length of resampled array, equal to len(X) if n==None
    Results
    -------
    returns X_resamples
    """
    if n == None:
        n = len(X)
    resample_i = N.floor(N.random.rand(n) * len(X)).astype(int)
    X_resample = X[resample_i]
    return X_resample

2bab5a4f72f2b028e8ba84b770bc0d05fc25e1c0
<mask token>

<mask token>


def bootstrap_resample(X, n=None):
    """ Bootstrap resample an array_like
    Parameters
    ----------
    X : array_like
      data to resample
    n : int, optional
      length of resampled array, equal to len(X) if n==None
    Results
    -------
    returns X_resamples
    """
    if n == None:
        n = len(X)
    resample_i = N.floor(N.random.rand(n) * len(X)).astype(int)
    X_resample = X[resample_i]
    return X_resample

import numpy as N


def bootstrap_resample(X, n=None):
    """ Bootstrap resample an array_like
    Parameters
    ----------
    X : array_like
      data to resample
    n : int, optional
      length of resampled array, equal to len(X) if n==None
    Results
    -------
    returns X_resamples
    """
    if n == None:
        n = len(X)
    resample_i = N.floor(N.random.rand(n) * len(X)).astype(int)
    X_resample = X[resample_i]
    return X_resample
