[SPARK-4596][MLLib] Refactorize Normalizer to make code cleaner

In this refactoring, the performance will be slightly increased due to removing
the overhead from breeze vector. The bottleneck is still in breeze norm
which is implemented by activeIterator.

This inefficiency of breeze norm will be addressed in next PR. At least,
this PR makes the code more consistent in the codebase.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #3446 from dbtsai/normalizer and squashes the following commits:

e20a2b9 [DB Tsai] first commit

(cherry picked from commit 89f912264603741c7d980135c26102d63e11791f)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
[SPARK-4526][MLLIB]GradientDescent get a wrong gradient value according to the gradient formula.

This is caused by the miniBatchSize parameter.The number of `RDD.sample` returns is not fixed.
cc mengxr

Author: GuoQiang Li <witgo@qq.com>

Closes #3399 from witgo/GradientDescent and squashes the following commits:

13cb228 [GuoQiang Li] review commit
668ab66 [GuoQiang Li] Double to Long
b6aa11a [GuoQiang Li] Check miniBatchSize is greater than 0
0b5c3e3 [GuoQiang Li] Minor fix
12e7424 [GuoQiang Li] GradientDescent get a wrong gradient value according to the gradient formula, which is caused by the miniBatchSize parameter.

(cherry picked from commit f515f9432b05f7e090b651c5536aa706d1cde487)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
[SPARK-4535][Streaming] Fix the error in comments

change `NetworkInputDStream` to `ReceiverInputDStream`
change `ReceiverInputTracker` to `ReceiverTracker`

Author: q00251598 <qiyadong@huawei.com>

Closes #3400 from watermen/fix-comments and squashes the following commits:

75d795c [q00251598] change 'NetworkInputDStream' to 'ReceiverInputDStream' && change 'ReceiverInputTracker' to 'ReceiverTracker'

(cherry picked from commit a51118a34a4617c07373480c4b021e53124c3c00)
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>

Conflicts:
	examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
[SPARK-4381][Streaming]Add warning log when user set spark.master to local in Spark Streaming and there's no job executed

Author: jerryshao <saisai.shao@intel.com>

Closes #3244 from jerryshao/SPARK-4381 and squashes the following commits:

d2486c7 [jerryshao] Improve the warning log
d726e85 [jerryshao] Add local[1] to the filter condition
eca428b [jerryshao] Add warning log

(cherry picked from commit fef27b29431c2adadc17580f26c23afa6a3bd1d2)
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
[SPARK-4344][DOCS] adding documentation on spark.yarn.user.classpath.first

The documentation for the two parameters is the same with a pointer from the standalone parameter to the yarn parameter

Author: arahuja <aahuja11@gmail.com>

Closes #3209 from arahuja/yarn-classpath-first-param and squashes the following commits:

51cb9b2 [arahuja] [SPARK-4344][DOCS] adding documentation for YARN on userClassPathFirst

(cherry picked from commit d240760191f692ee7b88dfc82f06a31a340a88a2)
Signed-off-by: Thomas Graves <tgraves@apache.org>
[SPARK-4601][Streaming] Set correct call site for streaming jobs so that it is displayed correctly on the Spark UI

When running the NetworkWordCount, the description of the word count jobs are set as "getCallsite at DStream:xxx" . This should be set to the line number of the streaming application that has the output operation that led to the job being created. This is because the callsite is incorrectly set in the thread launching the jobs. This PR fixes that.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #3455 from tdas/streaming-callsite-fix and squashes the following commits:

69fc26f [Tathagata Das] Set correct call site for streaming jobs so that it is displayed correctly on the Spark UI

(cherry picked from commit 69cd53eae205eb10d52eaf38466db58a23b6ae81)
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
[SPARK-4581][MLlib] Refactorize StandardScaler to improve the transformation performance

The following optimizations are done to improve the StandardScaler model
transformation performance.

1) Covert Breeze dense vector to primitive vector to reduce the overhead.
2) Since mean can be potentially a sparse vector, we explicitly convert it to dense primitive vector.
3) Have a local reference to `shift` and `factor` array so JVM can locate the value with one operation call.
4) In pattern matching part, we use the mllib SparseVector/DenseVector instead of breeze's vector to
make the codebase cleaner.

Benchmark with mnist8m dataset:

Before,
DenseVector withMean and withStd: 50.97secs
DenseVector withMean and withoutStd: 42.11secs
DenseVector withoutMean and withStd: 8.75secs
SparseVector withoutMean and withStd: 5.437secs

With this PR,
DenseVector withMean and withStd: 5.76secs
DenseVector withMean and withoutStd: 5.28secs
DenseVector withoutMean and withStd: 5.30secs
SparseVector withoutMean and withStd: 1.27secs

Note that without the local reference copy of `factor` and `shift` arrays,
the runtime is almost three time slower.

DenseVector withMean and withStd: 18.15secs
DenseVector withMean and withoutStd: 18.05secs
DenseVector withoutMean and withStd: 18.54secs
SparseVector withoutMean and withStd: 2.01secs

The following code,
```scala
while (i < size) {
   values(i) = (values(i) - shift(i)) * factor(i)
   i += 1
}
```
will generate the bytecode
```
   L13
    LINENUMBER 106 L13
   FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I] []
    ILOAD 7
    ILOAD 6
    IF_ICMPGE L14
   L15
    LINENUMBER 107 L15
    ALOAD 5
    ILOAD 7
    ALOAD 5
    ILOAD 7
    DALOAD
    ALOAD 0
    INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.shift ()[D
    ILOAD 7
    DALOAD
    DSUB
    ALOAD 0
    INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D
    ILOAD 7
    DALOAD
    DMUL
    DASTORE
   L16
    LINENUMBER 108 L16
    ILOAD 7
    ICONST_1
    IADD
    ISTORE 7
    GOTO L13
```
, while with the local reference of the `shift` and `factor` arrays, the bytecode will be
```
   L14
    LINENUMBER 107 L14
    ALOAD 0
    INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D
    ASTORE 9
   L15
    LINENUMBER 108 L15
   FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector [D org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I [D] []
    ILOAD 8
    ILOAD 7
    IF_ICMPGE L16
   L17
    LINENUMBER 109 L17
    ALOAD 6
    ILOAD 8
    ALOAD 6
    ILOAD 8
    DALOAD
    ALOAD 2
    ILOAD 8
    DALOAD
    DSUB
    ALOAD 9
    ILOAD 8
    DALOAD
    DMUL
    DASTORE
   L18
    LINENUMBER 110 L18
    ILOAD 8
    ICONST_1
    IADD
    ISTORE 8
    GOTO L15
```

You can see that with local reference, the both of the arrays will be in the stack, so JVM can access the value without calling `INVOKESPECIAL`.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #3435 from dbtsai/standardscaler and squashes the following commits:

85885a9 [DB Tsai] revert to have lazy in shift array.
daf2b06 [DB Tsai] Address the feedback
cdb5cef [DB Tsai] small change
9c51eef [DB Tsai] style
fc795e4 [DB Tsai] update
5bffd3d [DB Tsai] first commit

(cherry picked from commit bf1a6aaac577757a293a573fe8eae9669697310a)
Signed-off-by: Xiangrui Meng <meng@databricks.com>