docs: fix some typos (#331)

MilesCranmer · Jul 4, 2024 · cd23a6e · cd23a6e
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Showing 3 changed files with 4 additions and 4 deletions.
diff --git a/src/MLJInterface.jl b/src/MLJInterface.jl
@@ -576,7 +576,7 @@ eval(
         for units) will trigger dimensional analysis to be used.
 
     - `w` is the observation weights which can either be `nothing` (default) or an
-      `AbstractVector` whoose element scitype is `Count` or `Continuous`.
+      `AbstractVector` whose element scitype is `Count` or `Continuous`.
 
     Train the machine using `fit!(mach)`, inspect the discovered expressions with
     `report(mach)`, and predict on new data with `predict(mach, Xnew)`.
@@ -700,7 +700,7 @@ eval(
       `y` (use `DynamicQuantities` for units) will trigger dimensional analysis to be used.
 
     - `w` is the observation weights which can either be `nothing` (default) or an
-      `AbstractVector` whoose element scitype is `Count` or `Continuous`. The same
+      `AbstractVector` whose element scitype is `Count` or `Continuous`. The same
       weights are used for all targets.
 
     Train the machine using `fit!(mach)`, inspect the discovered expressions with

diff --git a/src/Population.jl b/src/Population.jl
@@ -124,7 +124,7 @@ function _best_of_sample(
     n = length(members)  # == tournament_selection_n
     scores = Vector{L}(undef, n)
     if options.use_frequency_in_tournament
-        # Score based on frequency of that size occuring.
+        # Score based on frequency of that size occurring.
         # In the end, all sizes should be just as common in the population.
         adaptive_parsimony_scaling = L(options.adaptive_parsimony_scaling)
         # e.g., for 100% occupied at one size, exp(-20*1) = 2.061153622438558e-9; which seems like a good punishment for dominating the population.

diff --git a/src/SingleIteration.jl b/src/SingleIteration.jl
@@ -84,7 +84,7 @@ function s_r_cycle(
                 member.score
             end
             # TODO: Note that this per-population hall of fame only uses the batched
-            #       loss, and is therefore innaccurate. Therefore, some expressions
+            #       loss, and is therefore inaccurate. Therefore, some expressions
             #       may be loss if a very small batch size is used.
             # - Could have different batch size for different things (smaller for constant opt)
             # - Could just recompute losses here (expensive)