Update of readme and tests.

Author: emmanuellujan

README.md

@@ -1,14 +1,14 @@
 # StreamingSampling.jl
 
-StreamingSampling is a Julia-based proof-of-concept implementation of a streamed variant of maximum-entropy sampling ([UPmaxentropy](https://www.rdocumentation.org/packages/sampling/versions/2.11/topics/UPmaxentropy)). It is designed to process large datasets stored on disk with minimal impact on RAM. The method begins by computing first-order inclusion probabilities using a [DPP](https://dahtah.github.io/Determinantal.jl/dev/)-based heuristic, and then feeds these probabilities into the classical UPmaxentropy algorithm to produce diverse samples.
+StreamingSampling is a Julia-based proof-of-concept implementation of a streamed variants of maximum-entropy sampling ([UPmaxentropy](https://www.rdocumentation.org/packages/sampling/versions/2.11/topics/UPmaxentropy)) and weighted sampling. It is designed to process large datasets stored on disk with minimal impact on RAM. The method begins by computing first-order inclusion probabilities using a [DPP](https://dahtah.github.io/Determinantal.jl/dev/)-based heuristic, and then feeds these probabilities into classical sampling algorithms to produce diverse samples.
 
 <a href="https://julia.mit.edu/StreamingSampling.jl/dev/">
 <img alt="Development documentation" src="https://img.shields.io/badge/documentation-in%20development-orange?style=flat-square">
 </a>
 <a href="https://mit-license.org">
 <img alt="MIT license" src="https://img.shields.io/badge/License-MIT-blue.svg?style=flat-square">
 </a>
-<a href="https://github.com/cesmix-mit/PotentialLearning.jl/issues/new">
+<a href="https://github.com/JuliaLabs/StreamingSampling.jl/issues/new">
 <img alt="Ask us anything" src="https://img.shields.io/badge/Ask%20us-anything-1abc9c.svg?style=flat-square">
 </a>
 </a> 

docs/src/index.md

@@ -4,7 +4,18 @@ CurrentModule = StreamingSampling
 
 # StreamingSampling.jl
 
-Documentation for [StreamingSampling](https://github.com/emmanuellujan/StreamingSampling.jl).
+StreamingSampling is a Julia-based proof-of-concept implementation of a streamed variants of maximum-entropy sampling ([UPmaxentropy](https://www.rdocumentation.org/packages/sampling/versions/2.11/topics/UPmaxentropy)) and weighted sampling. It is designed to process large datasets stored on disk with minimal impact on RAM. The method begins by computing first-order inclusion probabilities using a [DPP](https://dahtah.github.io/Determinantal.jl/dev/)-based heuristic, and then feeds these probabilities into classical sampling algorithms to produce diverse samples.
 
-StreamingSampling is a Julia-based proof-of-concept implementation of a streamed variant of maximum-entropy sampling ([UPmaxentropy](https://www.rdocumentation.org/packages/sampling/versions/2.11/topics/UPmaxentropy)). It is designed to process large datasets stored on disk with minimal impact on RAM. The method begins by computing first-order inclusion probabilities using a [DPP](https://dahtah.github.io/Determinantal.jl/dev/)-based heuristic, and then feeds these probabilities into the classical UPmaxentropy algorithm to produce diverse samples.
+<a href="https://julia.mit.edu/StreamingSampling.jl/dev/">
+<img alt="Development documentation" src="https://img.shields.io/badge/documentation-in%20development-orange?style=flat-square">
+</a>
+<a href="https://mit-license.org">
+<img alt="MIT license" src="https://img.shields.io/badge/License-MIT-blue.svg?style=flat-square">
+</a>
+<a href="https://github.com/JuliaLabs/StreamingSampling.jl/issues/new">
+<img alt="Ask us anything" src="https://img.shields.io/badge/Ask%20us-anything-1abc9c.svg?style=flat-square">
+</a>
+</a> 
+<br />
+<br />
 

test/runtests.jl

@@ -12,13 +12,18 @@ using Statistics
                   "data/data3.txt",
                   "data/data4.txt"]
     # Compute streaming weights
-    ws = compute_weights(file_paths; chunksize=200, subchunksize=50)
+    ws = compute_weights(file_paths;
+                         chunksize=200,
+                         subchunksize=50)
 
     # Define sample size
     n = 1781
 
     # Sample by weighted sampling
-    inds = StatsBase.sample(1:length(ws), Weights(ws), n; replace=false)
+    inds = StatsBase.sample(1:length(ws),
+                            Weights(ws),
+                            n;
+                            replace=false)
 
     # Checks
     println("Checking sample size.")
@@ -32,7 +37,10 @@ end
                   "data/data3.txt",
                   "data/data4.txt"]
     # Compute streaming weights
-    ws = compute_weights(file_paths; chunksize=500, subchunksize=100)
+    ws = compute_weights(file_paths;
+                         chunksize=500,
+                         subchunksize=100,
+                         normalize=false)
 
     # Define sample size
     n = 2351