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Complete this python program:
def fastica(X, n_components=None, algorithm='parallel', whiten=True, fun='logcosh', fun_args=None, max_iter=200, tol=0.0001, w_init=None, random_state=None, return_X_mean=False, compute_sources=True, return_n_iter=False):
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#Here is the Python code you requested.

    """Perform Fast Independent Component Analysis.
    Read more in the :ref:`User Guide <ICA>`.
    Parameters
    X : array-like, shape (n_samples, n_features)
    Training vector, where n_samples is the number of samples and
    n_features is the number of features.
    n_components : int, optional
    Number of components to extract. If None no dimension reduction
    is performed.
    algorithm : {\parallel\, \deflation\}, optional
    Apply a parallel or deflational FASTICA algorithm.
    whiten : boolean, optional
    If True perform an initial whitening of the data.
    If False, the data is assumed to have already been
    preprocessed: it should be centered, normed and white.
    Otherwise you will get incorrect results.
    In this case the parameter n_components will be ignored.
    fun : string or function, optional. Default: \logcosh\
    The functional form of the G function used in the
    approximation to neg-entropy. Could be either \logcosh\, \exp\,
    or \cube\.
    You can also provide your own function. It should return a tuple
    containing the value of the function, and of its derivative, in the
    point. Example:
    def my_g(x):
    return x ** 3, 3 * x ** 2
    fun_args : dictionary, optional
    Arguments to send to the functional form.
    If empty or None and if fun=\logcosh\, fun_args will take value
    {\alpha\ : 1.0}
    max_iter : int, optional
    Maximum number of iterations to perform.
    tol : float, optional
    A positive scalar giving the tolerance at which the
    un-mixing matrix is considered to have converged.
    w_init : (n_components, n_components) array, optional
    Initial un-mixing array of dimension (n.comp,n.comp).
    If None (default) then an array of normal r.v.\s is used.
    random_state : int, RandomState instance or None, optional (default=None)
    If int, random_state is the seed used by the random number generator;
    If RandomState instance, random_state is the random number generator;
    If None, the random number generator is the RandomState..."""
    random_state = check_random_state(random_state)
    fun_args = ({} if (fun_args is None) else fun_args)
    X = check_array(X, copy=whiten, dtype=FLOAT_DTYPES).T
    alpha = fun_args.get('alpha', 1.0)
    if (not (1 <= alpha <= 2)):
        raise ValueError('alpha    must    be    in    [1,2]')
    if (fun == 'logcosh'):
        g = _logcosh
    elif (fun == 'exp'):
        g = _exp
    elif (fun == 'cube'):
        g = _cube
    elif callable(fun):
        def g(x, fun_args):
            return fun(x, **fun_args)
    else:
        exc = (ValueError if isinstance(fun, six.string_types) else TypeError)
        raise exc(("Unknown    function    %r;    should    be    one    of    'logcosh',    'exp',    'cube'    or    callable" % fun))
    (n, p) = X.shape
    if ((not whiten) and (n_components is not None)):
        n_components = None
        warnings.warn('Ignoring    n_components    with    whiten=False.')
    if (n_components is None):
        n_components = min(n, p)
    if (n_components > min(n, p)):
        n_components = min(n, p)
        warnings.warn(('n_components    is    too    large:    it    will    be    set    to    %s' % n_components))
    if whiten:
        X_mean = X.mean(axis=(-1))
        X -= X_mean[:, np.newaxis]
        (u, d, _) = linalg.svd(X, full_matrices=False)
        del _
        K = (u / d).T[:n_components]
        del u, d
        X1 = np.dot(K, X)
        X1 *= np.sqrt(p)
    else:
        X1 = as_float_array(X, copy=False)
    if (w_init is None):
        w_init = np.asarray(random_state.normal(size=(n_components, n_components)), dtype=X1.dtype)
    else:
        w_init = np.asarray(w_init)
        if (w_init.shape != (n_components, n_components)):
            raise ValueError(('w_init    has    invalid    shape    --    should    be    %(shape)s' % {'shape': (n_components, n_components)}))
    kwargs = {'tol': tol, 'g': g, 'fun_args': fun_args, 'max_iter': max_iter, 'w_init': w_init}
    if (algorithm == 'parallel'):
        (W, n_iter) = _ica_par(X1, **kwargs)
   ...