Optimize sample!

[Tortar]

This is possible, and even much faster than when using a Dict (wrong)

[Tortar]

Actually I don't really like that the same id will be given to different agents when using sample! with a vector

[Tortar]

As you can see, the speed-up is very good:

# this pr:

@btime sample!(model, 10^6) setup=(model = fake_model(Xoshiro(42), 10^7)) evals=1 samples=10 seconds=10
 276.573 ms (1000010 allocations: 114.44 MiB)

# before:

@btime sample!(model, 10^6) setup=(model = fake_model(Xoshiro(42), 10^7)) evals=1 samples=10 seconds=10
  1.947 s (48424 allocations: 168.59 MiB)

(I changed the scope of this pr to be just an optimization of the current function)

[codecov-commenter]

Codecov Report

Attention: 4 lines in your changes are missing coverage. Please review.

Comparison is base (3526b5c) 92.27% compared to head (30895f9) 92.18%. Report is 1 commits behind head on main.

Files	Patch %	Lines
src/core/model_free_extensions.jl	0.00%	4 Missing :warning:

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #943 +/- ## ========================================== - Coverage 92.27% 92.18% -0.09% ========================================== Files 33 33 Lines 2277 2330 +53 ========================================== + Hits 2101 2148 +47 - Misses 176 182 +6 ```

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[Tortar]

Actually I used a model with no space to benchmark, the new method falls behind when there is a space instead. Need to change the code to acknowledge this

[Tortar]

should be okay now, this is an example benchmark:

using Agents, BenchmarkTools, Random

# no space model

model_step!(model) = nothing

function fake_model(rng, nagents)
    model = StandardABM(NoSpaceAgent; model_step!, rng)
    for i in 1:nagents
        add_agent!(model)
    end
    model
end

@btime sample!(model, 10^5) setup=(model = fake_model(Xoshiro(42), 10^6)) evals=1

# space model

function fake_model(rng, nagents)
    model = StandardABM(GridAgent{2}, GridSpace((100, 100)); model_step!, rng)
    for i in 1:nagents
        add_agent!(model)
    end
    model
end

@btime sample!(model, 10^5) setup=(model = fake_model(Xoshiro(42), 10^6)) evals=1

where we have that

before pr, no space: 87.246 ms (4769 allocations: 16.87 MiB)
after pr, no space: 17.157 ms (100010 allocations: 11.45 MiB)

before pr, space: 162.883 ms (4789 allocations: 16.95 MiB) 
after pr, space: 22.714 ms (100010 allocations: 12.98 MiB)

In general, the perf improvement can vary, but it should be always an improvement except for edge cases like 10 agent in a 1 million positions space.