Merge pull request #139 from JuliaOpt/jmw/show

Print something readable for problems with more than two parameters
JuliaNLSolvers · Oct 13, 2015 · b62747e · b62747e
2 parents 8ad3d9b + ac72bb1
commit b62747e
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Showing 3 changed files with 60 additions and 22 deletions.
diff --git a/src/types.jl b/src/types.jl
@@ -102,8 +102,16 @@ end
 function Base.show(io::IO, r::MultivariateOptimizationResults)
     @printf io "Results of Optimization Algorithm\n"
     @printf io " * Algorithm: %s\n" r.method
-    @printf io " * Starting Point: [%s]\n" join(r.initial_x, ",")
-    @printf io " * Minimum: [%s]\n" join(r.minimum, ",")
+    if length(join(r.initial_x, ",")) < 40
+        @printf io " * Starting Point: [%s]\n" join(r.initial_x, ",")
+    else
+        @printf io " * Starting Point: [%s, ...]\n" join(r.initial_x[1:2], ",")
+    end
+    if length(join(r.minimum, ",")) < 40
+        @printf io " * Minimum: [%s]\n" join(r.minimum, ",")
+    else
+        @printf io " * Minimum: [%s, ...]\n" join(r.minimum[1:2], ",")
+    end
     @printf io " * Value of Function at Minimum: %f\n" r.f_minimum
     @printf io " * Iterations: %d\n" r.iterations
     @printf io " * Convergence: %s\n" converged(r)

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -4,26 +4,28 @@
 
 using Optim
 
-my_tests = ["bfgs.jl",
-            "gradient_descent.jl",
-            "momentum_gradient_descent.jl",
-            "grid_search.jl",
-            "l_bfgs.jl",
-            "levenberg_marquardt.jl",
-            "newton.jl",
-            "cg.jl",
-            "nelder_mead.jl",
-            "optimize.jl",
-            "simulated_annealing.jl",
-            "interpolating_line_search.jl",
-            "api.jl",
-            "golden_section.jl",
-            "brent.jl",
-            "type_stability.jl",
-            "array.jl",
-            "constrained.jl",
-            "callbacks.jl"
-           ]
+my_tests = [
+    "types.jl",
+    "bfgs.jl",
+    "gradient_descent.jl",
+    "momentum_gradient_descent.jl",
+    "grid_search.jl",
+    "l_bfgs.jl",
+    "levenberg_marquardt.jl",
+    "newton.jl",
+    "cg.jl",
+    "nelder_mead.jl",
+    "optimize.jl",
+    "simulated_annealing.jl",
+    "interpolating_line_search.jl",
+    "api.jl",
+    "golden_section.jl",
+    "brent.jl",
+    "type_stability.jl",
+    "array.jl",
+    "constrained.jl",
+    "callbacks.jl",
+]
 
 println("Running tests:")
 

diff --git a/test/types.jl b/test/types.jl
@@ -0,0 +1,28 @@
+module TestTypes
+    using Base.Test
+    using Compat
+    using Optim
+
+    prob = Optim.UnconstrainedProblems.examples["Rosenbrock"]
+    f_prob = prob.f
+    res = Optim.nelder_mead(f_prob, prob.initial_x)
+
+    io = IOBuffer()
+    show(io, res)
+    s = takebuf_string(io)
+
+    lines = split(s, '\n')
+    @test lines[1] == "Results of Optimization Algorithm"
+    @test startswith(lines[2], " * Algorithm: ")
+    @test startswith(lines[3], " * Starting Point: ")
+    @test startswith(lines[4], " * Minimum: [")
+    @test startswith(lines[5], " * Value of Function at Minimum: ")
+    @test startswith(lines[6], " * Iterations: ")
+    @test startswith(lines[7], " * Convergence: ")
+    @test startswith(lines[8], "   * |x - x'| < ")
+    @test startswith(lines[9], "   * |f(x) - f(x')| / |f(x)| < ")
+    @test startswith(lines[10], "   * |g(x)| < ")
+    @test startswith(lines[11], "   * Exceeded Maximum Number of Iterations: ")
+    @test startswith(lines[12], " * Objective Function Calls: ")
+    @test startswith(lines[13], " * Gradient Call: ")
+end