fix kind of proxy for density estimation examples

Author: ablaom

docs/src/fit_update.md

@@ -85,7 +85,7 @@ which consumes no data, returns the learned density:
 
 ```julia
 model = fit(algorithm, y) # no features
-predict(model)  # shortcut for  `predict(model, Distribution())`, or similar
+predict(model)  # shortcut for  `predict(model, SingleDistribution())`, or similar
 ```
 
 A one-liner will typically be implemented as well:

test/patterns/incremental_algorithms.jl

@@ -70,25 +70,25 @@ end
 LearnAPI.features(::NormalEstimator, y) = nothing
 LearnAPI.target(::NormalEstimator, y) = y
 
-LearnAPI.predict(model::NormalEstimatorFitted, ::Distribution) =
+LearnAPI.predict(model::NormalEstimatorFitted, ::SingleDistribution) =
     Distributions.Normal(model.ȳ, sqrt(model.ss/model.n))
 LearnAPI.predict(model::NormalEstimatorFitted, ::Point) = model.ȳ
 function LearnAPI.predict(model::NormalEstimatorFitted, ::ConfidenceInterval)
-    d = predict(model, Distribution())
+    d = predict(model, SingleDistribution())
     return (quantile(d, 0.025), quantile(d, 0.975))
 end
 
 # for fit and predict in one line:
 LearnAPI.predict(::NormalEstimator, k::LearnAPI.KindOfProxy, y)  =
     predict(fit(NormalEstimator(), y), k)
-LearnAPI.predict(::NormalEstimator, y) = predict(NormalEstimator(), Distribution(), y)
+LearnAPI.predict(::NormalEstimator, y) = predict(NormalEstimator(), SingleDistribution(), y)
 
 LearnAPI.extras(model::NormalEstimatorFitted) = (μ=model.ȳ, σ=sqrt(model.ss/model.n))
 
 @trait(
     NormalEstimator,
     constructor = NormalEstimator,
-    kinds_of_proxy = (Distribution(), Point(), ConfidenceInterval()),
+    kinds_of_proxy = (SingleDistribution(), Point(), ConfidenceInterval()),
     tags = ("density estimation", "incremental algorithms"),
     is_pure_julia = true,
     human_name = "normal distribution estimator",