(weights, n_epochs)
return result
Given a set of weights and number of epochs generate the number of
    epochs per sample for each weight.

    Parameters
    ----------
    weights: array of shape (n_1_simplices)
        The weights ofhow much we wish to sample each 1-simplex.

    n_epochs: int
        The total number of epochs we want to train for.

    Returns
    -------
    An array of number of epochs per sample, one for each 1-simplex.
Given a set of weights and number of epochs generate the number of
    epochs per sample for each weight.
Given a set of weights and number of epochs generate the number of epochs per sample for each weight .
def make_epochs_per_sample(weights, n_epochs):
    """Given a set of weights and number of epochs generate the number of
    epochs per sample for each weight.

    Parameters
    ----------
    weights: array of shape (n_1_simplices)
        The weights ofhow much we wish to sample each 1-simplex.

    n_epochs: int
        The total number of epochs we want to train for.

    Returns
    -------
    An array of number of epochs per sample, one for each 1-simplex.
    """
    result = -1.0 * np.ones(weights.shape[0], dtype=np.float64)
    n_samples = n_epochs * (weights / weights.max())
    result[n_samples > 0] = float(n_epochs) / n_samples[n_samples > 0]
    return result
def make_epochs_per_sample ( weights , n_epochs ) : result = - 1.0 * np . ones ( weights . shape [ 0 ] , dtype = np . float64 ) n_samples = n_epochs * ( weights / weights . max ( ) ) result [ n_samples > 0 ] = float ( n_epochs ) / n_samples [ n_samples > 0 ] return result