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From shiva...@apache.org
Subject spark git commit: [SPARK-18849][ML][SPARKR][DOC] vignettes final check update
Date Thu, 15 Dec 2016 05:52:05 GMT
Repository: spark
Updated Branches:
  refs/heads/branch-2.1 d399a297d -> 2a8de2e11


[SPARK-18849][ML][SPARKR][DOC] vignettes final check update

## What changes were proposed in this pull request?

doc cleanup

## How was this patch tested?

~~vignettes is not building for me. I'm going to kick off a full clean build and try again
and attach output here for review.~~
Output html here: https://felixcheung.github.io/sparkr-vignettes.html

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #16286 from felixcheung/rvignettespass.

(cherry picked from commit 7d858bc5ce870a28a559f4e81dcfc54cbd128cb7)
Signed-off-by: Shivaram Venkataraman <shivaram@cs.berkeley.edu>


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2a8de2e1
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2a8de2e1
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2a8de2e1

Branch: refs/heads/branch-2.1
Commit: 2a8de2e11ebab0cb9056444053127619d8a47d8a
Parents: d399a29
Author: Felix Cheung <felixcheung_m@hotmail.com>
Authored: Wed Dec 14 21:51:52 2016 -0800
Committer: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Committed: Wed Dec 14 21:52:01 2016 -0800

----------------------------------------------------------------------
 R/pkg/vignettes/sparkr-vignettes.Rmd | 38 ++++++++++---------------------
 1 file changed, 12 insertions(+), 26 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/2a8de2e1/R/pkg/vignettes/sparkr-vignettes.Rmd
----------------------------------------------------------------------
diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd b/R/pkg/vignettes/sparkr-vignettes.Rmd
index 8f39922..fa2656c 100644
--- a/R/pkg/vignettes/sparkr-vignettes.Rmd
+++ b/R/pkg/vignettes/sparkr-vignettes.Rmd
@@ -447,33 +447,31 @@ head(teenagers)
 
 SparkR supports the following machine learning models and algorithms.
 
-* Generalized Linear Model (GLM)
+* Accelerated Failure Time (AFT) Survival Model
 
-* Random Forest
+* Collaborative Filtering with Alternating Least Squares (ALS)
+
+* Gaussian Mixture Model (GMM)
+
+* Generalized Linear Model (GLM)
 
 * Gradient-Boosted Trees (GBT)
 
-* Naive Bayes Model
+* Isotonic Regression Model
 
 * $k$-means Clustering
 
-* Accelerated Failure Time (AFT) Survival Model
-
-* Gaussian Mixture Model (GMM)
+* Kolmogorov-Smirnov Test
 
 * Latent Dirichlet Allocation (LDA)
 
-* Multilayer Perceptron Model
-
-* Collaborative Filtering with Alternating Least Squares (ALS)
-
-* Isotonic Regression Model
-
 * Logistic Regression Model
 
-* Kolmogorov-Smirnov Test
+* Multilayer Perceptron Model
 
-More will be added in the future.
+* Naive Bayes Model
+
+* Random Forest
 
 ### R Formula
 
@@ -601,8 +599,6 @@ head(aftPredictions)
 
 #### Gaussian Mixture Model
 
-(Added in 2.1.0)
-
 `spark.gaussianMixture` fits multivariate [Gaussian Mixture Model](https://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model)
(GMM) against a `SparkDataFrame`. [Expectation-Maximization](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
(EM) is used to approximate the maximum likelihood estimator (MLE) of the model.
 
 We use a simulated example to demostrate the usage.
@@ -620,8 +616,6 @@ head(select(gmmFitted, "V1", "V2", "prediction"))
 
 #### Latent Dirichlet Allocation
 
-(Added in 2.1.0)
-
 `spark.lda` fits a [Latent Dirichlet Allocation](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)
model on a `SparkDataFrame`. It is often used in topic modeling in which topics are inferred
from a collection of text documents. LDA can be thought of as a clustering algorithm as follows:
 
 * Topics correspond to cluster centers, and documents correspond to examples (rows) in a
dataset.
@@ -676,8 +670,6 @@ perplexity
 
 #### Multilayer Perceptron
 
-(Added in 2.1.0)
-
 Multilayer perceptron classifier (MLPC) is a classifier based on the [feedforward artificial
neural network](https://en.wikipedia.org/wiki/Feedforward_neural_network). MLPC consists of
multiple layers of nodes. Each layer is fully connected to the next layer in the network.
Nodes in the input layer represent the input data. All other nodes map inputs to outputs by
a linear combination of the inputs with the node’s weights $w$ and bias $b$ and applying
an activation function. This can be written in matrix form for MLPC with $K+1$ layers as follows:
 $$
 y(x)=f_K(\ldots f_2(w_2^T f_1(w_1^T x + b_1) + b_2) \ldots + b_K).
@@ -726,8 +718,6 @@ head(select(predictions, predictions$prediction))
 
 #### Collaborative Filtering
 
-(Added in 2.1.0)
-
 `spark.als` learns latent factors in [collaborative filtering](https://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering)
via [alternating least squares](http://dl.acm.org/citation.cfm?id=1608614).
 
 There are multiple options that can be configured in `spark.als`, including `rank`, `reg`,
`nonnegative`. For a complete list, refer to the help file.
@@ -757,8 +747,6 @@ head(predicted)
 
 #### Isotonic Regression Model
 
-(Added in 2.1.0)
-
 `spark.isoreg` fits an [Isotonic Regression](https://en.wikipedia.org/wiki/Isotonic_regression)
model against a `SparkDataFrame`. It solves a weighted univariate a regression problem under
a complete order constraint. Specifically, given a set of real observed responses $y_1, \ldots,
y_n$, corresponding real features $x_1, \ldots, x_n$, and optionally positive weights $w_1,
\ldots, w_n$, we want to find a monotone (piecewise linear) function $f$ to  minimize
 $$
 \ell(f) = \sum_{i=1}^n w_i (y_i - f(x_i))^2.
@@ -802,8 +790,6 @@ head(predict(isoregModel, newDF))
 
 #### Logistic Regression Model
 
-(Added in 2.1.0)
-
 [Logistic regression](https://en.wikipedia.org/wiki/Logistic_regression) is a widely-used
model when the response is categorical. It can be seen as a special case of the [Generalized
Linear Predictive Model](https://en.wikipedia.org/wiki/Generalized_linear_model).
 We provide `spark.logit` on top of `spark.glm` to support logistic regression with advanced
hyper-parameters.
 It supports both binary and multiclass classification with elastic-net regularization and
feature standardization, similar to `glmnet`.


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