From 204476bfedfb5e2ce00651050c92b0b3c124427f Mon Sep 17 00:00:00 2001
From: Angelos Chatzimparmpas <angelos.chatzimparmpas@gmail.com>
Date: Fri, 3 Apr 2020 17:19:52 +0200
Subject: [PATCH] new readme

Former-commit-id: b085ca19ee4c79bde00bafb9ca31036473e852eb
---
 README.md | 51 +++++++--------------------------------------------
 1 file changed, 7 insertions(+), 44 deletions(-)

diff --git a/README.md b/README.md
index e4cd571..babc139 100755
--- a/README.md
+++ b/README.md
@@ -1,7 +1,4 @@
-
-[![Build Status](https://travis-ci.org/lvdmaaten/bhtsne.svg)](https://travis-ci.org/lvdmaaten/bhtsne)
-
-This software package contains a Barnes-Hut implementation of the t-SNE algorithm. The implementation is described in [this paper](http://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf).
+This Git repository contains the code that accompanies a research publication so-called "t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections". The details are described in [this paper](https://arxiv.org/abs/2002.06910).
 
 **Note:** This repository is a version of t-SNE modified to support ongoing research. It may be slightly slower than the original. If you're just trying to run t-SNE, check the original repository that we forked from.
 
@@ -41,50 +38,16 @@ The executable will be called `windows\bh_tsne.exe`.
 
 The code comes with wrappers for Matlab and Python. These wrappers write your data to a file called `data.dat`, run the `bh_tsne` binary, and read the result file `result.dat` that the binary produces. There are also external wrappers available for [Torch](https://github.com/clementfarabet/manifold), [R](https://github.com/jkrijthe/Rtsne), and [Julia](https://github.com/zhmz90/BHTsne.jl). Writing your own wrapper should be straightforward; please refer to one of the existing wrappers for the format of the data and result files.
 
-Demonstration of usage in Matlab:
-
-```matlab
-filename = websave('mnist_train.mat', 'https://github.com/awni/cs224n-pa4/blob/master/Simple_tSNE/mnist_train.mat?raw=true');
-load(filename);
-numDims = 2; pcaDims = 50; perplexity = 50; theta = .5; alg = 'svd';
-map = fast_tsne(digits', numDims, pcaDims, perplexity, theta, alg);
-gscatter(map(:,1), map(:,2), labels');
-```
-
 Demonstration of usage in Python:
 
-```python
-import numpy as np
-import bhtsne
-
-data = np.loadtxt("mnist2500_X.txt", skiprows=1)
-
-embedding_array = bhtsne.run_bh_tsne(data, initial_dims=data.shape[1])
 ```
+# first terminal: hosting the visualization side (client)
+python3 -m http.server # for Python3
+#or 
+python -m SimpleHTTPServer 8000 # for Python2
 
-### Python Wrapper
-
-Usage:
-
-```bash
-python bhtsne.py [-h] [-d NO_DIMS] [-p PERPLEXITY] [-t THETA]
-                  [-r RANDSEED] [-n INITIAL_DIMS] [-v] [-i INPUT]
-                  [-o OUTPUT] [--use_pca] [--no_pca] [-m MAX_ITER]
+# second terminal: hosting the computational side (server)
+FLASK_APP=tsneGrid.py flask run
 ```
 
-Below are the various options the wrapper program `bhtsne.py` expects:
-
-- `-h, --help`                      show this help message and exit
-- `-d NO_DIMS, --no_dims`           NO_DIMS
-- `-p PERPLEXITY, --perplexity`     PERPLEXITY
-- `-t THETA, --theta`               THETA
-- `-r RANDSEED, --randseed`         RANDSEED
-- `-n INITIAL_DIMS, --initial_dims` INITIAL_DIMS
-- `-v, --verbose`
-- `-i INPUT, --input`               INPUT: the input file, expects a TSV with the first row as the header.
-- `-o OUTPUT, --output`             OUTPUT: A TSV file having each row as the `d` dimensional embedding.
-- `--use_pca`
-- `--no_pca`
-- `-m MAX_ITER, --max_iter`         MAX_ITER
-
 # t-viSNE