Minor fixed before release (#224)

* Quickfix dispatch

* Adapt cartpole tests like suggested

* Adapt Readme

* Update julia version

* Add the print

Author: AlCap23

Project.toml

@@ -29,7 +29,7 @@ QuadGK = "2.4"
 Reexport = "1.0"
 StatsBase = "0.32.0, 0.33"
 Symbolics = "0.1"
-julia = "^1.5.0"
+julia = "^1.6.0"
 
 [extras]
 DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"

README.md

@@ -38,35 +38,45 @@ end
 
 u0 = [1.0;0.0;0.0]
 tspan = (0.0,100.0)
-dt = 0.005
+dt = 0.1
 prob = ODEProblem(lorenz,u0,tspan)
 sol = solve(prob, Tsit5(), saveat = dt, progress = true)
 
-# Differential data from equations
-X = Array(sol)
-DX = similar(X)
-for (i, xi) in enumerate(eachcol(X))
-    DX[:,i] = lorenz(xi, [], 0.0)
-end
 
 ## Start the automatic discovery
-ddprob = ContinuousDataDrivenProblem(X, sol.t, DX = DX)
+ddprob = ContinuousDataDrivenProblem(sol)
 
 @variables t x(t) y(t) z(t)
 u = [x;y;z]
 basis = Basis(polynomial_basis(u, 5), u, iv = t)
 opt = STLSQ(exp10.(-5:0.1:-1))
 ddsol = solve(ddprob, basis, opt, normalize = true)
-system = result(ddsol)
+print(ddsol, Val{true})
 ```
 
 ```
-Model ##Basis#350 with 3 equations
-x(t) y(t) z(t)
+Explicit Result
+Solution with 3 equations and 7 parameters.
+Returncode: sucess
+Sparsity: 7.0
+L2 Norm Error: 26.7343984476783
+AICC: 1.0013570199499398
+
+Model ##Basis#366 with 3 equations
+States : x(t) y(t) z(t)
 Parameters : 7
 Independent variable: t
 Equations
 Differential(t)(x(t)) = p₁*x(t) + p₂*y(t)
 Differential(t)(y(t)) = p₃*x(t) + p₄*y(t) + p₅*x(t)*z(t)
 Differential(t)(z(t)) = p₇*z(t) + p₆*x(t)*y(t)
+
+Parameters:
+   p₁ : -10.0
+   p₂ : 10.0
+   p₃ : 28.0
+   p₄ : -1.0
+   p₅ : -1.0
+   p₆ : 1.0
+   p₇ : -2.7
 ```

src/solution.jl

@@ -95,9 +95,9 @@ function Base.summary(io::IO, r::DataDrivenSolution)
 end
 
 
-function Base.print(io::IO, r::DataDrivenSolution, fullview::DataType = Val{false})
+function Base.print(io::IO, r::DataDrivenSolution, fullview::DataType)
 
-    fullview == Val{false} && return summary(io, r)
+    fullview != Val{true} && return summary(io, r)
 
     is_implicit(r) ? println(io,"Implicit Result") : println(io,"Explicit Result")
     println(io, "Solution with $(length(r.res.eqs)) equations and $(length(r.ps)) parameters.")

test/sindy/cartpole.jl

@@ -1,4 +1,3 @@
-
 function cart_pole(u, p, t)
     du = similar(u)
     F = -0.2 + 0.5*sin(6*t) # the input
@@ -40,7 +39,7 @@ push!(polys, sin.(u[1]).*u[3:4].^2...)
 push!(polys, sin.(u[1]).*cos.(u[1])...)
 push!(polys, sin.(u[1]).*cos.(u[1]).*u[3:4]...)
 push!(polys, sin.(u[1]).*cos.(u[1]).*u[3:4].^2...)
-implicits = [du;  du .* cos(u[1]);   du .* cos(u[1])^2; polys]
+implicits = [du;  du[1] .* u; du[2] .* u; du .* cos(u[1]);   du .* cos(u[1])^2; polys]
 push!(implicits, x...)
 push!(implicits, x[1]*cos(u[1]))
 push!(implicits, x[1]*sin(u[1]))
@@ -60,4 +59,4 @@ m = metrics(res)
 
 @test m.Sparsity == 10
 @test m.AICC < 180.0
-@test m.Error < 100.0
+@test m.Error < 1.0