Update README.md

README.md

@@ -5,52 +5,14 @@
 [![codecov](https://codecov.io/gh/JuliaDynamics/StreamSampling.jl/graph/badge.svg?token=F8W0MC53Z0)](https://codecov.io/gh/JuliaDynamics/StreamSampling.jl)
 [![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)
 
-
-The scope of this package is providing general methods to sample from any stream in a single pass through the data, even when the number of items contained in the stream is unknown.
+The scope of this package is to provide general methods to sample from any stream in a single pass through the data, even when 
+the number of items contained in the stream is unknown.
 
 This has some advantages over other sampling procedures:
 
 - If the iterable is lazy, the memory required grows in relation to the size of the sample, instead of the all population.
 - The sample collected is a random sample of the portion of the stream seen thus far at any point of the sampling process.
-
-Moreover, it turns out that sampling with the techniques implemented in this library
-is also much faster in some common cases, as highlighted below:
-
-
-```julia
-julia> using StreamSampling
-
-julia> using BenchmarkTools, Random, StatsBase
-
-julia> rng = Xoshiro(42);
-
-julia> iter = Iterators.filter(x -> x != 10, 1:10^7);
-
-julia> wv(el) = 1.0
-
-julia> @btime itsample($rng, $iter, 10^4, algRSWRSKIP);
-  11.744 ms (5 allocations: 156.39 KiB)
-
-julia> @btime sample($rng, collect($iter), 10^4; replace=true);
-  131.933 ms (20 allocations: 146.91 MiB)
-
-julia> @btime itsample($rng, $iter, 10^4, algL);
-  10.260 ms (3 allocations: 78.22 KiB)
-
-julia> @btime sample($rng, collect($iter), 10^4; replace=false);
-  132.069 ms (27 allocations: 147.05 MiB)
-
-julia> @btime itsample($rng, $iter, $wv, 10^4, algWRSWRSKIP);
-  32.278 ms (18 allocations: 547.34 KiB)
-
-julia> @btime sample($rng, collect($iter), Weights($wv.($iter)), 10^4; replace=true);
-  348.220 ms (49 allocations: 675.21 MiB)
-
-julia> @btime itsample($rng, $iter, $wv, 10^4, algAExpJ);
-  39.965 ms (11 allocations: 234.78 KiB)
-
-julia> @btime sample($rng, collect($iter), Weights($wv.($iter)), 10^4; replace=false);
-  306.039 ms (43 allocations: 370.19 MiB)
-```
-
+- In some cases, sampling with the techniques implemented in this library can bring considerable performance gains, since
+  the population of items doesn't need to be previously stored in memory.
+
 More information can be found in the [documentation](https://juliadynamics.github.io/StreamSampling.jl/dev/).