Question

我试图将GEV分布与温度数据相匹配，以帮助识别极值。我有不同地区的数据集 - 对于某些地区，适合工作正常，但对于其他地区，它会发生故障。它似乎将位置参数设置为接近分布范围的最大值。所有数据集都很大，大小相同，完整且没有特别奇怪的值。

请问您可以建议可能发生的事情，或者我如何调查genextreme函数流程以找出问题所在？

这里是相关的代码位（从NetCDF中读取值没有任何问题）：

Traceback (most recent call last):
  File "D:\Program Files\JetBrains\PyCharm 2017.1.1\helpers\pydev\pydevd.py", line 1578, in <module>
    globals = debugger.run(setup['file'], None, None, is_module)
  File "D:\Program Files\JetBrains\PyCharm 2017.1.1\helpers\pydev\pydevd.py", line 1015, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "D:\Program Files\JetBrains\PyCharm 2017.1.1\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "E:/Work/Lib/tensorflow/models/object_detection/train.py", line 198, in <module>
    tf.app.run()
  File "D:\Program Files\Python\Python35\lib\site-packages\tensorflow\python\platform\app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "E:/Work/Lib/tensorflow/models/object_detection/train.py", line 194, in main
    worker_job_name, is_chief, FLAGS.train_dir)
  File "E:\Work\Lib\tensorflow\models\object_detection\trainer.py", line 184, in train
    data_augmentation_options)
  File "E:\Work\Lib\tensorflow\models\object_detection\trainer.py", line 77, in _create_input_queue
    prefetch_queue_capacity=prefetch_queue_capacity)
  File "E:\Work\Lib\tensorflow\models\object_detection\core\batcher.py", line 93, in __init__
    num_threads=num_batch_queue_threads)
  File "D:\Program Files\Python\Python35\lib\site-packages\tensorflow\python\training\input.py", line 919, in batch
    name=name)
  File "D:\Program Files\Python\Python35\lib\site-packages\tensorflow\python\training\input.py", line 697, in _batch
    tensor_list = _as_tensor_list(tensors)
  File "D:\Program Files\Python\Python35\lib\site-packages\tensorflow\python\training\input.py", line 385, in _as_tensor_list
    return [tensors[k] for k in sorted(tensors)]
TypeError: unorderable types: str() < tuple()

以下是来自不同地区的成果的两个例子，成功与否：

Successful fit

Unsuccessful fit

成功拟合分布的平均位置参数为1.066，而数据均值为2.395。失败者计算的位置参数为12.202，而数据平均值为2.138。

提前感谢您的帮助！

Genextreme适用于某些数据集

0 个答案: