Trying to equal my fork to the upstream

Author: davibarreira

LICENSE

@@ -1,16 +1,6 @@
 name = "OptimalTransport"
 uuid = "7e02d93a-ae51-4f58-b602-d97af76e3b33"
 authors = ["zsteve <[email protected]>"]
-<<<<<<< HEAD
-version = "0.1.9"
-
-[deps]
-Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
-PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
-=======
 version = "0.2.0"
 
 [deps]
@@ -21,25 +11,10 @@ LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
->>>>>>> upstream/master
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
 Distances = "0.9.0, 0.10"
-<<<<<<< HEAD
-IterativeSolvers = "0.8.4"
-PyCall = "1.91"
-julia = "1"
-
-[extras]
-Tulip = "6dd1b50a-3aae-11e9-10b5-ef983d2400fa"
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[targets]
-test = ["Tulip","CUDA", "Random", "Test"]
-=======
 FillArrays = "0.9, 0.10"
 IterativeSolvers = "0.8.4, 0.9"
 LazyArrays = "0.18, 0.19"
@@ -56,4 +31,3 @@ Tulip = "6dd1b50a-3aae-11e9-10b5-ef983d2400fa"
 
 [targets]
 test = ["Tulip", "CUDA", "PyCall", "Random", "Test"]
->>>>>>> upstream/master

Manifest.toml

@@ -1,13 +1,7 @@
 <a href="http://zsteve.phatcode.net/OptimalTransportDocs/">
-<<<<<<< HEAD
-<img src="images/optimaltransport_logo2.png" height="125"><br></a>
-
-## Optimal Transport algorithms for Julia
-=======
 <img src="images/optimaltransport_logo.png" height="125"><br></a>
 
 ## Optimal transport algorithms for Julia
->>>>>>> upstream/master
 
 [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://zsteve.github.io/OptimalTransport.jl/stable)
 [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://zsteve.github.io/OptimalTransport.jl/dev)
@@ -21,14 +15,7 @@ This package provides some implementations of optimal transport algorithms, incl
 
 ## Supported algorithms
 
-<<<<<<< HEAD
-Currently, Earth Mover's Distance (solution to exact Monge-Kantorovich problem) is wrapped via PyCall using the excellent [POT (Python Optimal Transport)](https://pythonot.github.io/) library. The hope is that _gradually_, more and more functionality will be implemented in native Julia.
-
-However, the following algorithms are offered in pure Julia:
-
-=======
 * Earth-Mover's distance (`emd`, `emd2`)
->>>>>>> upstream/master
 * Sinkhorn algorithm (`sinkhorn`, `sinkhorn2`)
 * Log-stabilized Sinkhorn algorithm (`sinkhorn_stabilized`)
 * Epsilon-scaling stabilized Sinkhorn algorithm (`sinkhorn_stabilized_epsscaling`) 
@@ -40,12 +27,8 @@ See the documentation pages for further information. Most calling conventions ar
 
 ## Documentation
 
-<<<<<<< HEAD
-Read the [documentation](http://zsteve.phatcode.net/OptimalTransportDocs/)
-=======
  - [Stable](https://zsteve.github.io/OptimalTransport.jl/stable)
  - [Dev](https://zsteve.github.io/OptimalTransport.jl/dev)
->>>>>>> upstream/master
 
 ## Basic usage and examples
 

Project.toml

@@ -1,13 +1,7 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-<<<<<<< HEAD
-
-[compat]
-Documenter = "0.25"
-=======
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
 
 [compat]
 Documenter = "0.25, 0.26"
 PyCall = "1"
->>>>>>> upstream/master