Add docstrings (#7)

src/Omelette.jl

@@ -64,7 +64,7 @@ function add_predictor(
     predictor::AbstractPredictor,
     x::Vector{JuMP.VariableRef},
 )
-    y = JuMP.@variable(model, [1:size(predictor, 1)])
+    y = JuMP.@variable(model, [1:size(predictor, 1)], base_name = "omelette_y")
     add_predictor!(model, predictor, x, y)
     return y
 end

src/models/LinearRegression.jl

@@ -3,6 +3,37 @@
 # Use of this source code is governed by an MIT-style license that can be found
 # in the LICENSE.md file or at https://opensource.org/licenses/MIT.
 
+"""
+    LinearRegression(parameters::Matrix)
+
+Represents the linear relationship:
+```math
+f(x) = A x
+```
+where \$A\$ is the \$m \\times n\$ matrix `parameters`.
+
+## Example
+
+```jldoctest
+julia> using JuMP, Omelette
+
+julia> model = Model();
+
+julia> @variable(model, x[1:2]);
+
+julia> f = Omelette.LinearRegression([2.0, 3.0])
+Omelette.LinearRegression([2.0 3.0])
+
+julia> y = Omelette.add_predictor(model, f, x)
+1-element Vector{VariableRef}:
+ omelette_y[1]
+
+julia> print(model)
+ Feasibility
+ Subject to
+  2 x[1] + 3 x[2] - omelette_y[1] = 0
+```
+"""
 struct LinearRegression <: AbstractPredictor
     parameters::Matrix{Float64}
 end

src/models/LogisticRegression.jl

@@ -3,6 +3,37 @@
 # Use of this source code is governed by an MIT-style license that can be found
 # in the LICENSE.md file or at https://opensource.org/licenses/MIT.
 
+"""
+    LogisticRegression(parameters::Matrix)
+
+Represents the linear relationship:
+```math
+f(x) = \\frac{1}{1 + e^{-A x}}
+```
+where \$A\$ is the \$m \\times n\$ matrix `parameters`.
+
+## Example
+
+```jldoctest
+julia> using JuMP, Omelette
+
+julia> model = Model();
+
+julia> @variable(model, x[1:2]);
+
+julia> f = Omelette.LogisticRegression([2.0, 3.0])
+Omelette.LogisticRegression([2.0 3.0])
+
+julia> y = Omelette.add_predictor(model, f, x)
+1-element Vector{VariableRef}:
+ omelette_y[1]
+
+julia> print(model)
+Feasibility
+Subject to
+ (1.0 / (1.0 + exp(-2 x[1] - 3 x[2]))) - omelette_y[1] = 0
+```
+"""
 struct LogisticRegression <: AbstractPredictor
     parameters::Matrix{Float64}
 end