onepl_jml

def onepl_jml(dataset, options=None):
    """ Estimates parameters in an 1PL IRT Model.

    Args:
        dataset: [items x participants] matrix of True/False Values
        options: dictionary with updates to default options

    Returns:
        discrimination: (float) estimate of test discrimination
        difficulty: (1d array) estimates of item diffiulties

    Options:
        * max_iteration: int
"""
    options = validate_estimation_options(options)

    # Defines item parameter update function
    def _item_min_func(n_items, alphas, thetas,
                       betas, the_sign, counts):
        # pylint: disable=cell-var-from-loop

        def _alpha_min(estimate):
            # Initialize cost evaluation to zero
            cost = 0
            for ndx in range(n_items):
                # pylint: disable=cell-var-from-loop
                scalar = the_sign[ndx, :] * estimate

                def _beta_min(beta):
                    otpt = 1.0 / (1.0 + np.exp(scalar *
                                               (thetas - beta)))
                    return -np.log(otpt).dot(counts)

                # Solves for the difficulty parameter for a given item at
                # a specific discrimination parameter
                betas[ndx] = fminbound(_beta_min, -6, 6)
                cost += _beta_min(betas[ndx])

            return cost

        min_alpha = fminbound(_alpha_min, 0.25, 5)
        alphas[:] = min_alpha

        return alphas, betas

    result = _jml_abstract(dataset, _item_min_func, discrimination=1,
                           max_iter=options['max_iteration'])

    return result[0][0], result[1]