Repository: Angel-ML/angel (language: java)

Issue:
Typo in warpLDA
In the documentation (wiki and Readme) `warpLDA` is sometimes called `wrapLDA`


Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 14cc191bc..4be0cd8fd 100644
--- a/README.md
+++ b/README.md
@@ -63,7 +63,7 @@
 		* [Softmax Regression](./docs/algo/softmax_on_angel.md)
 		* [KMeans](./docs/algo/kmeans_on_angel.md)
 		* [GBDT](./docs/algo/gbdt_on_angel.md)
-		* [LDA\*](./docs/algo/lda_on_angel.md) ([WrapLDA](./docs/algo/wrap_lda_on_angel.md))
+		* [LDA\*](./docs/algo/lda_on_angel.md) ([WarpLDA](./docs/algo/warp_lda_on_angel.md))
 * **Spark on Angel**
 	* **Angel-Mllib**
 		* [FM](https://github.com/Angel-ML/PyTorch-On-Angel/blob/branch-0.2.0/docs/recommendation.md)
diff --git a/README_en.md b/README_en.md
index 2edae06b5..f19dbb310 100644
--- a/README_en.md
+++ b/README_en.md
@@ -45,15 +45,15 @@ We welcome everyone interested in machine learning or graph computing to contrib
 - [**Algorithm Parameter Description**](./docs/algo/model_config_details.md)
 - **Angel**
   - **Traditional Machine Learning Methods**
-    - [Logistic Regression(LR)](./docs/algo/lr_on_angel.md)
-    - [Support Vector Machine(SVM)](./docs/algo/svm_on_angel.md)
+    - [Logistic Regression(LR)](./docs/algo/lr_on_angel_en.md)
+    - [Support Vector Machine(SVM)](./docs/algo/svm_on_angel_en.md)
     - [Factorization Machine(FM)](./docs/algo/fm_on_angel.md)
-    - [Linear Regression](./docs/algo/linear_on_angel.md)
-    - [Robust Regression](./docs/algo/robust_on_angel.md)
-    - [Softmax Regression](./docs/algo/softmax_on_angel.md)
-    - [KMeans](./docs/algo/kmeans_on_angel.md)
-    - [GBDT](./docs/algo/gbdt_on_angel.md)
-    - [LDA\*](./docs/algo/lda_on_angel.md) ([WrapLDA](./docs/algo/wrap_lda_on_angel.md))
+    - [Linear Regression](./docs/algo/linear_on_angel_en.md)
+    - [Robust Regression](./docs/algo/robust_on_angel_en.md)
+    - [Softmax Regression](./docs/algo/softmax_on_angel_en.md)
+    - [KMeans](./docs/algo/kmeans_on_angel_en.md)
+    - [GBDT](./docs/algo/gbdt_on_angel_en.md)
+    - [LDA\*](./docs/algo/lda_on_angel_en.md) ([WarpLDA](./docs/algo/warp_lda_on_angel.md))
 - **Spark on Angel**
   - **Angel-Mllib**
     - [FM](https://github.com/Angel-ML/PyTorch-On-Angel/blob/branch-0.2.0/docs/recommendation.md)
diff --git a/docs/algo/lda_on_angel_en.md b/docs/algo/lda_on_angel_en.md
index a04288567..44251a92a 100644
--- a/docs/algo/lda_on_angel_en.md
+++ b/docs/algo/lda_on_angel_en.md
@@ -13,8 +13,7 @@ A text corpus ``$ C $`` contains a set of documents `` $ \{D_1, \cdots, D_{M}\}
 LDA models the generative process for each document in the corpus. It draws a ``$ K $`` dimensional topic distribution, ``$ \theta_d $``, from a Dirichlet distribution, ``$ Dir(\alpha) $``, where ``$ \alpha $`` is the parameter vector of the Dirichlet (hyperparameter of the LDA). To generate each word ``$ t_{dn} $`` in document ``$ d $``, LDA first draws the topic of the word, ``$ z_{dn} $``, from a multinomial distribution ``$ Mult(\theta_d) $``, and then draws the word ``$ w_{dn} \in V $`` from a multinomial distribution ``$ Mult(\phi_{z_{dn}}) $``.
 
 ### Gibbs Sampling
-A common inference technique for LDA is Gibbs Sampling, which is a MCMC method for sampling from the posterior distribution of ``$ z_{dn} $`` and infer the distribution over topics and the distribution over words for each document. Some commonly used Gibbs Sampling variants include the Collapsed Gibbs Sampling(CGS), SparseLDA, 
-AliasLDA, F+LDA, LightLDA and WarpLDA, to name a few, and our experiment results suggest F+LDA as most suitable for training LDA on Angel. 
+A common inference technique for LDA is Gibbs Sampling, which is a MCMC method for sampling from the posterior distribution of ``$ z_{dn} $`` and infer the distribution over topics and the distribution over words for each document. Some commonly used Gibbs Sampling variants include the Collapsed Gibbs Sampling(CGS), SparseLDA, AliasLDA, F+LDA, LightLDA and WarpLDA, to name a few, and our experiment results suggest F+LDA as most suitable for training LDA on Angel. 
 
 ### Collapsed Gibbs Sampling (CGS)
 We use ``$ Z=\{z_d\}_{d=1}^D $`` to represent the set of topics for all words, ``$ \Phi = [\phi_1 \cdots \phi_{V}] $`` to represent the ``$ V \times K $`` topic-word matrix, and ``$ \Theta = [\theta_1 \cdots \theta_D] $`` to represent the matrix whose columns are the topic distributions for all documents, then, training LDA requires inferring the posterior of the latent variable ``$ (\Theta, \Phi, Z) $``, given the observed variable ``$ Z $`` and the hyperparameters. Using conjugate prior, CGS gives a closed-form expression for the posterior of ``$ Z $``, resulting in simple iterations for sampling ``$ z_{dn} $`` following the conditional probability below:
diff --git a/docs/algo/wrap_lda_on_angel.md b/docs/algo/warp_lda_on_angel.md
similarity index 100%
rename from docs/algo/wrap_lda_on_angel.md
rename to docs/algo/warp_lda_on_angel.md


Test patch: