[SPARK-4789] [mllib] Standardize ML Prediction APIs

This is part (1) of the updates from the WIP PR in [https://github.com/apache/spark/pull/3427]

Abstract classes for learning algorithms:
* Classifier
* Regressor
* Predictor

Traits for learning algorithms
* ProbabilisticClassificationModel

Concrete classes: learning algorithms
* LinearRegression
* LogisticRegression (updated to use new abstract classes)

Concrete classes: other
* LabeledPoint (adding weight to the old LabeledPoint)

Other updates:
* Modified ParamMap to sort parameters in toString

Test Suites:
* LabeledPointSuite
* LinearRegressionSuite
* LogisticRegressionSuite
* + Java versions of above suites

CC: @mengxr  @etrain  @shivaram 

The main pros & cons I see for having continuous & categorical types for labels & features are:

Pros:
* Type safety.

Cons:
* Algorithms may need to make copies of the data, depending on how much we expose internals of a features type.
* Users may have to worry more about types.
  * For labels, if we load data from a file without metadata (like libsvm), we may need to assume that everything is continuous.  Users will have to explicitly cast labels to categorical for classification.
  * For features, strong typing implies a stronger contract, where the assumption is that users specify the correct types.  I've been wondering about having more "best effort" APIs, where we take suggestions from users (like DecisionTree's categoricalFeaturesInfo) but otherwise try to infer the best types to use under the hood.

These lists started out much more balanced, but I guess I'm voting for the old system where everything is a Double.