Merge pull request #311 from abhiwand/docfixes-release

Updated spark version 1.3 to 1.5 for Model docs

engine-plugins/model-plugins/src/main/scala/org/trustedanalytics/atk/engine/model/plugins/classification/NaiveBayesNewPlugin.scala

@@ -42,7 +42,7 @@ This model runs the MLLib implementation of Naive Bayes [2]_.
 .. rubric:: footnotes
 
 .. [1] https://en.wikipedia.org/wiki/Naive_Bayes_classifier
-.. [2] https://spark.apache.org/docs/1.3.0/mllib-naive-bayes.html
+.. [2] https://spark.apache.org/docs/1.5.0/mllib-naive-bayes.html
              """,
   returns = """A new instance of NaiveBayesModel""")
 class NaiveBayesNewPlugin extends CommandPlugin[GenericNewModelArgs, ModelReference] {

engine-plugins/model-plugins/src/main/scala/org/trustedanalytics/atk/engine/model/plugins/classification/RandomForestClassifierNewPlugin.scala

@@ -44,7 +44,7 @@ using built-in binary and multi-class Classification Metrics.
 .. rubric:: footnotes
 
 .. [1] https://en.wikipedia.org/wiki/Random_forest
-.. [2] https://spark.apache.org/docs/1.3.0/mllib-ensembles.html
+.. [2] https://spark.apache.org/docs/1.5.0/mllib-ensembles.html#random-forests
  """,
   returns = """A new instance of RandomForestClassifierModel""")
 class RandomForestClassifierNewPlugin extends CommandPlugin[GenericNewModelArgs, ModelReference] {

engine-plugins/model-plugins/src/main/scala/org/trustedanalytics/atk/engine/model/plugins/classification/SVMWithSGDNewPlugin.scala

@@ -45,7 +45,7 @@ the true labels using built-in binary Classification Metrics.
 .. rubric:: footnotes
 
 .. [1] https://en.wikipedia.org/wiki/Support_vector_machine
-.. [2] https://spark.apache.org/docs/1.3.0/mllib-linear-methods.html
+.. [2] https://spark.apache.org/docs/1.5.0/mllib-linear-methods.html#linear-support-vector-machines-svms
 .. [3] https://en.wikipedia.org/wiki/Stochastic_gradient_descent""",
   returns = """A new instance of SvmModel""")
 class SVMWithSGDNewPlugin extends CommandPlugin[GenericNewModelArgs, ModelReference] {

engine-plugins/model-plugins/src/main/scala/org/trustedanalytics/atk/engine/model/plugins/classification/glm/LogisticRegressionNewPlugin.scala

@@ -39,7 +39,7 @@ It also allows the user to select the optimizer to be used - L-BFGS [3]_ or SGD
 .. rubric:: footnotes
 
 .. [1] https://en.wikipedia.org/wiki/Logistic_regression
-.. [2] https://spark.apache.org/docs/1.3.0/mllib-linear-methods.html#logistic-regression
+.. [2] https://spark.apache.org/docs/1.5.0/mllib-linear-methods.html#logistic-regression
 .. [3] https://en.wikipedia.org/wiki/Limited-memory_BFGS
 .. [4] https://en.wikipedia.org/wiki/Stochastic_gradient_descent
     """,

engine-plugins/model-plugins/src/main/scala/org/trustedanalytics/atk/engine/model/plugins/clustering/KMeansNewPlugin.scala

@@ -42,7 +42,7 @@ center and also from every other cluster center.
 .. rubric:: footnotes
 
 .. [1] https://en.wikipedia.org/wiki/K-means_clustering
-.. [2] https://spark.apache.org/docs/1.3.0/mllib-clustering.html#k-means""",
+.. [2] https://spark.apache.org/docs/1.5.0/mllib-clustering.html#k-means""",
   returns = """A new instance of KMeansModel""")
 class KMeansNewPlugin extends CommandPlugin[KMeansNewArgs, ModelReference] {
 

engine-plugins/model-plugins/src/main/scala/org/trustedanalytics/atk/engine/model/plugins/clustering/PowerIterationClusteringNewPlugin.scala

@@ -34,7 +34,7 @@ A Power Iteration Clustering model is initialized and the cluster assignments of
 .. rubric:: footnotes
 
 .. [1] http://www.cs.cmu.edu/~wcohen/postscript/icm12010-pic-final.pdf
-.. [2] https://spark.apache.org/docs/1.3.0/mllib-clustering.html#power-iteration-clustering-pic""",
+.. [2] https://spark.apache.org/docs/1.5.0/mllib-clustering.html#power-iteration-clustering-pic""",
   returns = """A new instance of PowerIterationClustering Model""")
 class PowerIterationClusteringNewPlugin extends CommandPlugin[GenericNewModelArgs, ModelReference] {
 

engine-plugins/model-plugins/src/main/scala/org/trustedanalytics/atk/engine/model/plugins/dimensionalityreduction/PrincipalComponentsNewPlugin.scala

@@ -47,7 +47,7 @@ predict and 2) compute the t-squared index during prediction.
 
 .. [1] https://en.wikipedia.org/wiki/Principal_component_analysis
 .. [2] https://en.wikipedia.org/wiki/Singular_value_decomposition
-.. [3] https://spark.apache.org/docs/1.3.0/mllib-dimensionality-reduction.html""",
+.. [3] https://spark.apache.org/docs/1.5.0/mllib-dimensionality-reduction.html""",
   returns = """A new instance of PrincipalComponentsModel""")
 class PrincipalComponentsNewPlugin extends CommandPlugin[GenericNewModelArgs, ModelReference] {
   /**

engine-plugins/model-plugins/src/main/scala/org/trustedanalytics/atk/engine/model/plugins/regression/LinearRegressionWithSGDNewPlugin.scala

@@ -37,7 +37,7 @@ SGD [3]_ optimizer.
 .. rubric:: footnotes
 
 .. [1] https://en.wikipedia.org/wiki/Linear_regression
-.. [2] https://spark.apache.org/docs/1.3.0/mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression
+.. [2] https://spark.apache.org/docs/1.5.0/mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression
 .. [3] https://en.wikipedia.org/wiki/Stochastic_gradient_descent""",
   returns = """A new instance of LinearRegressionModel""")
 class LinearRegressionWithSGDNewPlugin extends CommandPlugin[GenericNewModelArgs, ModelReference] {

engine-plugins/model-plugins/src/main/scala/org/trustedanalytics/atk/engine/model/plugins/regression/RandomForestRegressorNewPlugin.scala

@@ -41,7 +41,7 @@ value of the random forest.
 .. rubric:: footnotes
 
 .. [1] https://en.wikipedia.org/wiki/Random_forest
-.. [2] https://spark.apache.org/docs/1.3.0/mllib-ensembles.html""",
+.. [2] https://spark.apache.org/docs/1.5.0/mllib-ensembles.html#random-forests""",
   returns = """A new instance of RandomForestRegressor Model""")
 class RandomForestRegressorNewPlugin extends CommandPlugin[GenericNewModelArgs, ModelReference] {
   /**