def choice(a, size=1, replace=True, p=None):
    # Format and Verify input
    if isinstance(a, int):
        if a > 0:
            pop_size = a #population size
        else:
            raise ValueError("a must be greater than 0")
    else:
        a = np.array(a, ndmin=1, copy=0)
        if a.ndim != 1:
            raise ValueError("a must be 1-dimensional")
        pop_size = a.size
        if pop_size is 0:
            raise ValueError("a must be non-empty")

    if None != p:
        p = np.array(p, dtype=np.double, ndmin=1, copy=0)
        if p.ndim != 1:
            raise ValueError("p must be 1-dimensional")
        if p.size != pop_size:
            raise ValueError("a and p must have same size")
        if np.any(p < 0):
            raise ValueError("probabilities are not non-negative")
        if not np.allclose(p.sum(), 1):
            raise ValueError("probabilities do not sum to 1")

    # Actual sampling
    if replace:
        if None != p:
            cdf = p.cumsum()
            cdf /= cdf[-1]
            uniform_samples = np.random.random(size)
            idx = cdf.searchsorted(uniform_samples, side='right')
        else:
            idx = self.randint(0, pop_size, size=size)
    else:
        if size > pop_size:
            raise ValueError(''.join(["Cannot take a larger sample than ",
                                      "population when 'replace=False'"]))

        if None != p:
            if np.sum(p > 0) < size:
                raise ValueError("Fewer non-zero entries in p than size")
            n_uniq = 0
            p = p.copy()
            found = np.zeros(size, dtype=np.int)
            while n_uniq < size:
                x = self.rand(size - n_uniq)
                if n_uniq > 0:
                    p[found[0:n_uniq]] = 0
                cdf = np.cumsum(p)
                cdf /= cdf[-1]
                new = cdf.searchsorted(x, side='right')
                new = np.unique(new)
                found[n_uniq:n_uniq + new.size] = new
                n_uniq += new.size
            idx = found
        else:
            idx = self.permutation(pop_size)[:size]

    #Use samples as indices for a if a is array-like
    if isinstance(a, int):
        return idx
    else:
        return a.take(idx)