features.py 文件源码

def corrsort(features, use_tsp=False):
    """
    Given a features array, one feature per axis=0 entry, return axis=0 indices
    such that adjacent indices correspond to features that are correlated.

    cf. Traveling Salesman Problem. Not an optimal solution.

    use_tsp:
    Use tsp solver. See tsp_solver.greedy module that is used for this.
    Slows run-time considerably: O(N^4) computation, O(N^2) memory.

    Without use_tsp, both computation and memory are O(N^2).
    """

    correlations = np.ma.corrcoef(arrays.plane(features))
    if use_tsp: return tsp.solve_tsp(-correlations)

    size = features.shape[0]    
    correlations.mask[np.diag_indices(size)] = True

    # initialize results with the pair with the highest correlations.
    largest = np.argmax(correlations)
    results = [int(largest / size), largest % size]
    correlations.mask[:, results[0]] = True

    while len(results) < size:
        correlations.mask[:, results[-1]] = True
        results.append(np.argmax(correlations[results[-1]]))

    return results