From 3ee6d43ebe58522f04c14e2ac5bfb6789cb39461 Mon Sep 17 00:00:00 2001
From: nd <nd.startup@gmail.com>
Date: Wed, 11 Nov 2015 17:55:18 +0300
Subject: [PATCH] adds readme and cleanup

---
 README.md | 27 +++++++++++++++++++++++++++
 build.sbt |  2 --
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 69291fa..977f7bb 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,33 @@ At the moment the simplest direct scheme is implemented. It allows to ensure a m
 - Singular value decomposition
 - Eigen value decomposition
 
+## Getting ranola
+
+If you're using SBT, add the following line to your build file:
+
+```scala
+resolvers += "jitpack" at "https://jitpack.io"
+
+libraryDependencies += "com.github.nikdon" % "ranola" % "v0.1.0"
+
+```
+    
+For Maven:
+
+```maven
+<repository>
+    <id>jitpack.io</id>
+    <url>https://jitpack.io</url>
+</repository>
+
+<dependency>
+    <groupId>com.github.nikdon</groupId>
+    <artifactId>ranola</artifactId>
+    <version>v0.1.0</version>
+</dependency>
+
+```
+
 ##  Randomized schemes
 
 1. Generic scheme ([§4.1][1]) is designed for solving the fixed-rank problem, where the target rank of the input matrix is specified in advance. This algorithm works well for matrices whose singular values exhibit some decay, but they may produce a poor basis when the input matrix has a flat singular spectrum or when the input matrix is very large.
diff --git a/build.sbt b/build.sbt
index 2802de3..853219b 100644
--- a/build.sbt
+++ b/build.sbt
@@ -6,8 +6,6 @@ scalaVersion := "2.11.7"
 
 licenses += ("Apache-2.0", url("http://opensource.org/licenses/Apache-2.0"))
 
-libraryDependencies += "org.spire-math" % "spire_2.11" % "0.10.1"
-
 libraryDependencies ++= Seq(
   "org.scalanlp" %% "breeze" % "0.11.2",
   "org.scalanlp" %% "breeze-natives" % "0.11.2",