[SPARK-4306] [MLlib] Python API for LogisticRegressionWithLBFGS

```
class LogisticRegressionWithLBFGS
 |  train(cls, data, iterations=100, initialWeights=None, corrections=10, tolerance=0.0001, regParam=0.01, intercept=False)
 |      Train a logistic regression model on the given data.
 |
 |      :param data:           The training data, an RDD of LabeledPoint.
 |      :param iterations:     The number of iterations (default: 100).
 |      :param initialWeights: The initial weights (default: None).
 |      :param regParam:       The regularizer parameter (default: 0.01).
 |      :param regType:        The type of regularizer used for training
 |                             our model.
 |                             :Allowed values:
 |                               - "l1" for using L1 regularization
 |                               - "l2" for using L2 regularization
 |                               - None for no regularization
 |                               (default: "l2")
 |      :param intercept:      Boolean parameter which indicates the use
 |                             or not of the augmented representation for
 |                             training data (i.e. whether bias features
 |                             are activated or not).
 |      :param corrections:    The number of corrections used in the LBFGS update (default: 10).
 |      :param tolerance:      The convergence tolerance of iterations for L-BFGS (default: 1e-4).
 |
 |      >>> data = [
 |      ...     LabeledPoint(0.0, [0.0, 1.0]),
 |      ...     LabeledPoint(1.0, [1.0, 0.0]),
 |      ... ]
 |      >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data))
 |      >>> lrm.predict([1.0, 0.0])
 |      1
 |      >>> lrm.predict([0.0, 1.0])
 |      0
 |      >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
 |      [1, 0]
```
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