def step_maximization(X,weights,means,variances,n_compenents,pi):
    """M Step

    Parameters
    ----------
    X : array-like, shape (n_samples,)
        The data.
    weights : array-like, shape (n_components,n_samples)
        initilized weights array
    means : array-like, shape (n_components,)
        The means of each mixture component.
    variances : array-like, shape (n_components,)
        The variances of each mixture component.
    n_components : int
        The number of clusters
    pi: array-like (n_components,)
        mixture component weights

    Returns
    -------
    means : array-like, shape (n_components,)
        The means of each mixture component.
    variances : array-like, shape (n_components,)
        The variances of each mixture component.
    """
    r = []
    for j in range(n_compenents):  

        r.append((weights[j] * pi[j]) / (np.sum([weights[i] * pi[i] for i in range(n_compenents)], axis=0)))

        #5th equation above
        means[j] = np.sum(r[j] * X) / (np.sum(r[j]))

        #6th equation above
        variances[j] = np.sum(r[j] * np.square(X - means[j])) / (np.sum(r[j]))

        #4th equation above
        pi[j] = np.mean(r[j])

    return variances,means,pi