尝试用随机梯度就没有问题

>>> multi_class_data = [
...     LabeledPoint(0.0, [0.0, 1.0, 0.0]),
...     LabeledPoint(1.0, [1.0, 0.0, 0.0]),
...     LabeledPoint(2.0, [0.0, 0.0, 1.0])
... ]
>>> data = sc.parallelize(multi_class_data)
>>> mcm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3)

=================================

这里会报这个错
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/spark-current/python/pyspark/mllib/classification.py", line 398, in train
    return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
  File "/usr/lib/spark-current/python/pyspark/mllib/regression.py", line 216, in _regression_train_wrapper
    return modelClass(weights, intercept, numFeatures, numClasses)
  File "/usr/lib/spark-current/python/pyspark/mllib/classification.py", line 176, in __init__
    self._dataWithBiasSize)
TypeError: 'float' object cannot be interpreted as an integer

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