# Buggy code
def affinity_propagation(
    S,
    preference=None,
    convergence_iter=15,
    max_iter=200,
    damping=0.5,
    copy=True,
    verbose=False,
    return_n_iter=False,
):
    """Perform Affinity Propagation Clustering of data

    Read more in the :ref:`User Guide <affinity_propagation>`.

    Parameters
    ----------

    S : array-like, shape (n_samples, n_samples)
        Matrix of similarities between points

    preference : array-like, shape (n_samples,) or float, optional
        Preferences for each point - points with larger values of
        preferences are more likely to be chosen as exemplars. The number of
        exemplars, i.e. of clusters, is influenced by the input preferences
        value. If the preferences are not passed as arguments, they will be
        set to the median of the input similarities (resulting in a moderate
        number of clusters). For a smaller amount of clusters, this can be set
        to the minimum value of the similarities.

    convergence_iter : int, optional, default: 15
        Number of iterations with no change in the number
        of estimated clusters that stops the convergence.

    max_iter : int, optional, default: 200
        Maximum number of iterations

    damping : float, optional, default: 0.5
        Damping factor between 0.5 and 1.

    copy : boolean, optional, default: True
        If copy is False, the affinity matrix is modified inplace by the
        algorithm, for memory efficiency

    verbose : boolean, optional, default: False
        The verbosity level

    return_n_iter : bool, default False
        Whether or not to return the number of iterations.

    Returns
    -------

    cluster_centers_indices : array, shape (n_clusters,)
        index of clusters centers

    labels : array, shape (n_samples,)
        cluster labels for each point

    n_iter : int
        number of iterations run. Returned only if `return_n_iter` is
        set to True.

    Notes
    -----
    For an example, see :ref:`examples/cluster/plot_affinity_propagation.py
    <sphx_glr_auto_examples_cluster_plot_affinity_propagation.py>`.

    References
    ----------
    Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
    Between Data Points", Science Feb. 2007
    """
    S = as_float_array(S, copy=copy)
    n_samples = S.shape[0]

    if S.shape[0] != S.shape[1]:
        raise ValueError("S must be a square array (shape=%s)" % repr(S.shape))

    if preference is None:
        preference = np.median(S)
    if damping < 0.5 or damping >= 1:
        raise ValueError("damping must be >= 0.5 and < 1")

    random_state = np.random.RandomState(0)

    # Place preference on the diagonal of S
    S.flat[:: (n_samples + 1)] = preference

    A = np.zeros((n_samples, n_samples))
    R = np.zeros((n_samples, n_samples))  # Initialize messages
    # Intermediate results
    tmp = np.zeros((n_samples, n_samples))

    # Remove degeneracies
    S += (
        np.finfo(np.double).eps * S + np.finfo(np.double).tiny * 100
    ) * random_state.randn(n_samples, n_samples)

    # Execute parallel affinity propagation updates
    e = np.zeros((n_samples, convergence_iter))

    ind = np.arange(n_samples)

    for it in range(max_iter):
        # tmp = A + S; compute responsibilities
        np.add(A, S, tmp)
        I = np.argmax(tmp, axis=1)
        Y = tmp[ind, I]  # np.max(A + S, axis=1)
        tmp[ind, I] = -np.inf
        Y2 = np.max(tmp, axis=1)

        # tmp = Rnew
        np.subtract(S, Y[:, None], tmp)
        tmp[ind, I] = S[ind, I] - Y2

        # Damping
        tmp *= 1 - damping
        R *= damping
        R += tmp

        # tmp = Rp; compute availabilities
        np.maximum(R, 0, tmp)
        tmp.flat[:: n_samples + 1] = R.flat[:: n_samples + 1]

        # tmp = -Anew
        tmp -= np.sum(tmp, axis=0)
        dA = np.diag(tmp).copy()
        tmp.clip(0, np.inf, tmp)
        tmp.flat[:: n_samples + 1] = dA

        # Damping
        tmp *= 1 - damping
        A *= damping
        A -= tmp

        # Check for convergence
        E = (np.diag(A) + np.diag(R)) > 0
        e[:, it % convergence_iter] = E
        K = np.sum(E, axis=0)

        if it >= convergence_iter:
            se = np.sum(e, axis=1)
            unconverged = np.sum((se == convergence_iter) + (se == 0)) != n_samples
            if (not unconverged and (K > 0)) or (it == max_iter):
                if verbose:
                    print("Converged after %d iterations." % it)
                break
    else:
        if verbose:
            print("Did not converge")

    I = np.flatnonzero(E)
    K = I.size  # Identify exemplars

    if K > 0:
        c = np.argmax(S[:, I], axis=1)
        c[I] = np.arange(K)  # Identify clusters
        # Refine the final set of exemplars and clusters and return results
        for k in range(K):
            ii = np.where(c == k)[0]
            j = np.argmax(np.sum(S[ii[:, np.newaxis], ii], axis=0))
            I[k] = ii[j]

        c = np.argmax(S[:, I], axis=1)
        c[I] = np.arange(K)
        labels = I[c]
        # Reduce labels to a sorted, gapless, list
        cluster_centers_indices = np.unique(labels)
        labels = np.searchsorted(cluster_centers_indices, labels)
    else:
        labels = np.empty((n_samples, 1))
        cluster_centers_indices = None
        labels.fill(np.nan)

    if return_n_iter:
        return cluster_centers_indices, labels, it + 1
    else:
        return cluster_centers_indices, labels


# Traceback (ValueError)
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/cluster/affinity_propagation_.py", line 324, in predict
return pairwise_distances_argmin(X, self.cluster_centers_)
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/metrics/pairwise.py", line 464, in pairwise_distances_argmin
metric_kwargs)[0]
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/metrics/pairwise.py", line 339, in pairwise_distances_argmin_min
X, Y = check_pairwise_arrays(X, Y)
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/metrics/pairwise.py", line 111, in check_pairwise_arrays
warn_on_dtype=warn_on_dtype, estimator=estimator)
File "/Users/jsamoocha/.virtualenvs/coach/lib/python2.7/site-packages/sklearn/utils/validation.py", line 405, in check_array
% (array.ndim, estimator_name))
ValueError: Found array with dim 3. check_pairwise_arrays expected <= 2.

# Fixed code
def affinity_propagation(
    S,
    preference=None,
    convergence_iter=15,
    max_iter=200,
    damping=0.5,
    copy=True,
    verbose=False,
    return_n_iter=False,
):
    """Perform Affinity Propagation Clustering of data

    Read more in the :ref:`User Guide <affinity_propagation>`.

    Parameters
    ----------

    S : array-like, shape (n_samples, n_samples)
        Matrix of similarities between points

    preference : array-like, shape (n_samples,) or float, optional
        Preferences for each point - points with larger values of
        preferences are more likely to be chosen as exemplars. The number of
        exemplars, i.e. of clusters, is influenced by the input preferences
        value. If the preferences are not passed as arguments, they will be
        set to the median of the input similarities (resulting in a moderate
        number of clusters). For a smaller amount of clusters, this can be set
        to the minimum value of the similarities.

    convergence_iter : int, optional, default: 15
        Number of iterations with no change in the number
        of estimated clusters that stops the convergence.

    max_iter : int, optional, default: 200
        Maximum number of iterations

    damping : float, optional, default: 0.5
        Damping factor between 0.5 and 1.

    copy : boolean, optional, default: True
        If copy is False, the affinity matrix is modified inplace by the
        algorithm, for memory efficiency

    verbose : boolean, optional, default: False
        The verbosity level

    return_n_iter : bool, default False
        Whether or not to return the number of iterations.

    Returns
    -------

    cluster_centers_indices : array, shape (n_clusters,)
        index of clusters centers

    labels : array, shape (n_samples,)
        cluster labels for each point

    n_iter : int
        number of iterations run. Returned only if `return_n_iter` is
        set to True.

    Notes
    -----
    For an example, see :ref:`examples/cluster/plot_affinity_propagation.py
    <sphx_glr_auto_examples_cluster_plot_affinity_propagation.py>`.

    When the algorithm does not converge, it returns an empty array as
    ``cluster_center_indices`` and ``-1`` as label for each training sample.

    When all training samples have equal similarities and equal preferences,
    the assignment of cluster centers and labels depends on the preference.
    If the preference is smaller than the similarities, a single cluster center
    and label ``0`` for every sample will be returned. Otherwise, every
    training sample becomes its own cluster center and is assigned a unique
    label.

    References
    ----------
    Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
    Between Data Points", Science Feb. 2007
    """
    S = as_float_array(S, copy=copy)
    n_samples = S.shape[0]

    if S.shape[0] != S.shape[1]:
        raise ValueError("S must be a square array (shape=%s)" % repr(S.shape))

    if preference is None:
        preference = np.median(S)
    if damping < 0.5 or damping >= 1:
        raise ValueError("damping must be >= 0.5 and < 1")

    preference = np.array(preference)

    if n_samples == 1 or _equal_similarities_and_preferences(S, preference):
        # It makes no sense to run the algorithm in this case, so return 1 or
        # n_samples clusters, depending on preferences
        warnings.warn(
            "All samples have mutually equal similarities. "
            "Returning arbitrary cluster center(s)."
        )
        if preference.flat[0] >= S.flat[n_samples - 1]:
            return (
                (np.arange(n_samples), np.arange(n_samples), 0)
                if return_n_iter
                else (np.arange(n_samples), np.arange(n_samples))
            )
        else:
            return (
                (np.array([0]), np.array([0] * n_samples), 0)
                if return_n_iter
                else (np.array([0]), np.array([0] * n_samples))
            )

    random_state = np.random.RandomState(0)

    # Place preference on the diagonal of S
    S.flat[:: (n_samples + 1)] = preference

    A = np.zeros((n_samples, n_samples))
    R = np.zeros((n_samples, n_samples))  # Initialize messages
    # Intermediate results
    tmp = np.zeros((n_samples, n_samples))

    # Remove degeneracies
    S += (
        np.finfo(np.double).eps * S + np.finfo(np.double).tiny * 100
    ) * random_state.randn(n_samples, n_samples)

    # Execute parallel affinity propagation updates
    e = np.zeros((n_samples, convergence_iter))

    ind = np.arange(n_samples)

    for it in range(max_iter):
        # tmp = A + S; compute responsibilities
        np.add(A, S, tmp)
        I = np.argmax(tmp, axis=1)
        Y = tmp[ind, I]  # np.max(A + S, axis=1)
        tmp[ind, I] = -np.inf
        Y2 = np.max(tmp, axis=1)

        # tmp = Rnew
        np.subtract(S, Y[:, None], tmp)
        tmp[ind, I] = S[ind, I] - Y2

        # Damping
        tmp *= 1 - damping
        R *= damping
        R += tmp

        # tmp = Rp; compute availabilities
        np.maximum(R, 0, tmp)
        tmp.flat[:: n_samples + 1] = R.flat[:: n_samples + 1]

        # tmp = -Anew
        tmp -= np.sum(tmp, axis=0)
        dA = np.diag(tmp).copy()
        tmp.clip(0, np.inf, tmp)
        tmp.flat[:: n_samples + 1] = dA

        # Damping
        tmp *= 1 - damping
        A *= damping
        A -= tmp

        # Check for convergence
        E = (np.diag(A) + np.diag(R)) > 0
        e[:, it % convergence_iter] = E
        K = np.sum(E, axis=0)

        if it >= convergence_iter:
            se = np.sum(e, axis=1)
            unconverged = np.sum((se == convergence_iter) + (se == 0)) != n_samples
            if (not unconverged and (K > 0)) or (it == max_iter):
                if verbose:
                    print("Converged after %d iterations." % it)
                break
    else:
        if verbose:
            print("Did not converge")

    I = np.flatnonzero(E)
    K = I.size  # Identify exemplars

    if K > 0:
        c = np.argmax(S[:, I], axis=1)
        c[I] = np.arange(K)  # Identify clusters
        # Refine the final set of exemplars and clusters and return results
        for k in range(K):
            ii = np.where(c == k)[0]
            j = np.argmax(np.sum(S[ii[:, np.newaxis], ii], axis=0))
            I[k] = ii[j]

        c = np.argmax(S[:, I], axis=1)
        c[I] = np.arange(K)
        labels = I[c]
        # Reduce labels to a sorted, gapless, list
        cluster_centers_indices = np.unique(labels)
        labels = np.searchsorted(cluster_centers_indices, labels)
    else:
        warnings.warn(
            "Affinity propagation did not converge, this model "
            "will not have any cluster centers.",
            ConvergenceWarning,
        )
        labels = np.array([-1] * n_samples)
        cluster_centers_indices = []

    if return_n_iter:
        return cluster_centers_indices, labels, it + 1
    else:
        return cluster_centers_indices, labels