Merge pull request #167 from mrandri19/master

Use points range rather than function for plotting

docs/src/kriging.md

@@ -22,11 +22,13 @@ n_samples = 4
 lower_bound = 0.0
 upper_bound = 1.0
 
+xs = lower_bound:0.001:upper_bound
+
 x = sample(n_samples, lower_bound, upper_bound, SobolSample())
 y = f.(x)
 
 scatter(x, y, label="Sampled points", xlims=(lower_bound, upper_bound), ylims=(-7, 17))
-plot!(f, label="True function")
+plot!(xs, f.(xs), label="True function")
 ```
 ### Building a surrogate
 
@@ -38,8 +40,8 @@ With our sampled points we can build the Kriging surrogate using the `Kriging` f
 kriging_surrogate = Kriging(x, y, lower_bound, upper_bound, p=1.9);
 
 plot(x, y, seriestype=:scatter, label="Sampled points", xlims=(lower_bound, upper_bound), ylims=(-7, 17))
-plot!(f, label="True function")
-plot!(kriging_surrogate, label="Surrogate function", ribbon=p->std_error_at_point(kriging_surrogate, p))
+plot!(xs, f.(xs), label="True function")
+plot!(xs, kriging_surrogate.(xs), label="Surrogate function", ribbon=p->std_error_at_point(kriging_surrogate, p))
 ```
 ### Optimizing
 Having built a surrogate, we can now use it to search for minimas in our original function `f`.
@@ -50,6 +52,6 @@ To optimize using our surrogate we call `surrogate_optimize` method. We choose t
 @show surrogate_optimize(f, SRBF(), lower_bound, upper_bound, kriging_surrogate, SobolSample())
 
 scatter(x, y, label="Sampled points", ylims=(-7, 7))
-plot!(f, label="True function")
-plot!(kriging_surrogate, label="Surrogate function", ribbon=p->std_error_at_point(kriging_surrogate, p))
+plot!(xs, f.(xs), label="True function")
+plot!(xs, kriging_surrogate.(xs), label="Surrogate function", ribbon=p->std_error_at_point(kriging_surrogate, p))
 ```