typos

Author: bgctw

dev/doubleMM.jl

@@ -42,7 +42,7 @@ train_dataloader = MLUtils.DataLoader(
     (xM, xP, y_o, y_unc, 1:n_site);
     batchsize = n_batch, partial = false)
 σ_o = exp.(y_unc[:, 1] / 2)
-# assign the train_loader, otherwise it eatch time creates another version of synthetic data
+# assign the train_loader, otherwise it each time creates another version of synthetic data
 prob0 = HybridProblem(prob0_; train_dataloader)
 #tmp = HVI.get_hybridproblem_ϕunc(prob0; scenario)
 #prob0.covar
@@ -248,7 +248,7 @@ end
     (y2_K1global, θsP2_K1global, θsMs2_K1global) = (y, θsP, θsMs);
 end
 
-() -> begin # otpimize using LUX
+() -> begin # optimize using LUX
     #using Lux
     g_lux = Lux.Chain(
         # dense layer with bias that maps to 8 outputs and applies `tanh` activation

docs/src/explanation/theory_hvi.md

@@ -28,7 +28,7 @@ In order to learn $\phi_g$, the user needs to provide a batch of $i \in \{1 \ldo
 ## Estimation using the ELBO
 
 In order to find the parameters of the approximation of the posterior, HVI
-minizes the KL divergence between the approximation and the true posterior.
+minimizes the KL divergence between the approximation and the true posterior.
 This is achieve by maximizing the evidence lower bound (ELBO).
 
 $$\mathcal{L}(\phi) = \mathbb{E}_{q(\theta)} \left[\log p(y,\theta) \right] - \mathbb{E}_{q(\theta)} \left[\log q(\theta) \right]$$
@@ -128,7 +128,7 @@ $\phi = (\phi_P, \phi_g, \phi_u)$, comprises
 -  $\phi_P = \mu_{\zeta_P}$: the  means of the distributions of the transformed global   
   parameters, 
 -  $\phi_g$: the parameters of the machine learning model, and 
--  $\phi_u$: paramerization of $\Sigma_\zeta$ that is additional to the means. 
+-  $\phi_u$: parameterization of $\Sigma_\zeta$ that is additional to the means. 
 
 ### Details
 Specifically, $\phi_u= (log\sigma^2_P, log\sigma^2_{M0}, log\sigma^2_{M\eta}, a_P, a_M)$, 

docs/src/tutorials/basic_cpu.md

@@ -50,7 +50,7 @@ access the components by its symbolic names in the provided `ComponentArray`.
 HVI requires the evaluation of the likelihood of the predictions.
 It corresponds to the cost of predictions given some observations.
 
-The user specifies a function of the negative log-Likehood
+The user specifies a function of the negative log-Likelihood
 `neg_logden(obs, pred, uncertainty_parameters)`,
 where all of the parameters are arrays with columns for sites.
 

docs/src/tutorials/basic_cpu.qmd

@@ -59,7 +59,7 @@ access the components by its symbolic names in the provided `ComponentArray`.
 HVI requires the evaluation of the likelihood of the predictions.
 It corresponds to the cost of predictions given some observations.
 
-The user specifies a function of the negative log-Likehood 
+The user specifies a function of the negative log-Likelihood 
 `neg_logden(obs, pred, uncertainty_parameters)`,
 where all of the parameters are arrays with columns for sites.
 

docs/src/tutorials/blocks_corr.md

@@ -1,4 +1,4 @@
-# How to model indenpendent parameter-blocks in the posterior
+# How to model independent parameter-blocks in the posterior
 
 
 ``` @meta

docs/src/tutorials/blocks_corr.qmd

@@ -1,5 +1,5 @@
 ---
-title: "How to model indenpendent parameter-blocks in the posterior"
+title: "How to model independent parameter-blocks in the posterior"
 engine: julia
 execute:
   echo: true

src/AbstractHybridProblem.jl

@@ -91,7 +91,7 @@ end
 """
     get_hybridproblem_transforms(::AbstractHybridProblem; scenario)
 
-Return a NamedTupe of
+Return a NamedTuple of
 - `transP`: Bijectors.Transform for the global PBM parameters, θP
 - `transM`: Bijectors.Transform for the single-site PBM parameters, θM
 """

src/RRuleMonitor.jl

@@ -6,7 +6,7 @@ and raises an error if the supplied cotangent or the jacobian
 contains non-finitie entries.
 
 Arguments
-- label: id (String, or symbole) used in the error message.
+- label: id (String, or symbol) used in the error message.
 - `ad_backend`: the AD backend used in `DifferentiationInterface.jacobian`.
   Defaults to `AutoZygote().`
 """

src/util_ca.jl

@@ -31,7 +31,7 @@ component information that might be present in the dimensions.
 function compose_axes(axtuples::NamedTuple)
     ls = map(axtuple -> Val(prod(axis_length.(axtuple))), axtuples)
     # to work on types, need to construct value types of intervals
-    intervals = _construct_invervals(;lengths=ls)
+    intervals = _construct_intervals(;lengths=ls)
     named_intervals = (;zip(keys(axtuples),intervals)...)
     axc = map(named_intervals, axtuples) do interval, axtuple
         ax = length(axtuple) == 1 ? axtuple[1] : CA.ShapedAxis(axis_length.(axtuple))
@@ -40,7 +40,7 @@ function compose_axes(axtuples::NamedTuple)
     CA.Axis(; axc...)
 end
 
-function _construct_invervals(;lengths) 
+function _construct_intervals(;lengths) 
     reduce((ranges,length) -> _add_interval(;ranges, length), 
         Iterators.tail(lengths), init=(Val(1:_val_value(first(lengths))),))    
 end

test/test_ComponentArrayInterpreter.jl

@@ -12,7 +12,7 @@ using Suppressor
 gdev = Suppressor.@suppress gpu_device() # not loaded CUDA
 cdev = cpu_device()
 
-@testset "construct StaticComponentArrayInterepreter" begin
+@testset "construct StaticComponentArrayInterpreter" begin
     intv = @inferred CP.StaticComponentArrayInterpreter(CA.ComponentVector(a=1:3, b=reshape(4:9,3,2)))
     ints = @inferred CP.StaticComponentArrayInterpreter((;a=Val(3), b = Val((3,2))))
     # @descend_code_warntype CP.StaticComponentArrayInterpreter((;a=Val(3), b = Val((3,2))))