# WARNING: isnan(x::AbstractArray{T}) where T \<: Number is deprecated, use isnan.(x) instead
**URL:** https://discourse.julialang.org/t/warning-isnan-x-abstractarray-t-where-t-number-is-deprecated-use-isnan-x-instead/13537
**Category:** New to Julia
**Created:** [August 15, 2018, 11:52pm UTC](https://discourse.julialang.org/t/warning-isnan-x-abstractarray-t-where-t-number-is-deprecated-use-isnan-x-instead/13537 "2018-08-15T23:52:49Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@marcelord12](https://discourse.julialang.org/u/marcelord12)
#### Post date: [August 15, 2018, 11:52pm UTC](https://discourse.julialang.org/t/warning-isnan-x-abstractarray-t-where-t-number-is-deprecated-use-isnan-x-instead/13537/1 "2018-08-15T23:52:49Z")
Hi guys
I’m trying again to run a script file when suddenly I got this warning message. I couldn’t debug these warning and I don’t know what to do. Can somebody help to fix? I’m working on Julia 0.6.4 in Atom 1.29. My code is depicted bellow:
script file: sample.jl
```julia
# MDP problem description
# Get transition matrices
Tensor = Dict()
f = open(joinpath(@ __DIR__ , "Tensor1.txt"), "r+")
Tensor["1"] = readdlm(f)
g=f = open(joinpath(@ __DIR__ , "Tensor2.txt"), "r+")
Tensor["2"] = readdlm(g)
# Get reward function
Rewards = Dict()
h = open(joinpath(@ __DIR__ , "Rewards1.txt"), "r+")
Rewards["1"] = readdlm(h)
i = open(joinpath(@ __DIR__ , "Rewards1.txt"), "r+")
Rewards["2"] = readdlm(i)
# State space description
States = Dict()
States["#states"] = size(Tensor["1"],1)
States["FeatSize"] = [2,162]
Actions = Dict()
Actions["#actions"] = 2
Constraints = Dict()
Constraints["#constraints"] = 1
Constraints["States"] = [52]
# Initial condition
init = 1
λ = 0.7
VXpolicy = CMDP(States,Actions,
Tensor,Rewards,
λ,init,zeros(3),Constraints)
(Values,Policy) = SolveCMDP(VXpolicy);
TotalStates = States["#states"]
MC = zeros(TotalStates,TotalStates)
for i = 1:TotalStates
ind = Policy[i]
MC[i,:] = Tensor["$(ind)"][i,:]
end
```
script file: MDP\_LP.jl
```nohighlight
using SCS
type CMDP
States::Dict{Any,Any} # Dictionary describing states
Actions::Dict{Any,Any} # Dictionary describing actions
Tensor::Dict{Any,Any} # Dictionary for Tensor
Rewards::Dict{Any,Any} # Dictionary for Rewards
λ::Float64 # Discount factor
InitState::Int # Initial state
InitStateArray::Array{Int,1} # Initial features
Constraints::Dict{Any,Any} # Array of state constraints (index of state)
end
function SolveCMDP(problem::CMDP)
Ns = problem.States["#states"]
Na = problem.Actions["#actions"]
StateFeat = problem.States["FeatSize"]
Tensor = problem.Tensor
Rewards = problem.Rewards
init = problem.InitState
Findex = problem.InitStateArray
NCon = problem.Constraints["#constraints"]
StateCon = problem.Constraints["States"]
λ = problem.λ
if(init == 0)
init = sub2ind(StateFeat[end:-1:1],Findex[end:-1:1])
end
# Note that the variables are the occupational measures
X = Variable(Ns*Na)
# Objective
C = zeros(Ns*Na,1)
for i=1:Na
C[(i-1)*Ns+1:i*Ns,:] = Rewards["$(i)"]
end
# Equality constraint
I = zeros(Ns,Ns*Na)
T = zeros(Ns,Ns*Na)
for i=1:Na
I[:,(i-1)*Ns+1:i*Ns] = eye(Ns)
T[:,(i-1)*Ns+1:i*Ns] = transpose(Tensor["$(i)"])
end
Aeq = I - T
Beq = zeros(Ns,1)
Beq[init] = 1
if NCon > 0
for i=1:NCon
AeqC = zeros(Na,Ns*Na)
BeqC = zeros(Na,1)
for j=1:Na
# State constraint index
state_con_id = (j-1)*Ns + StateCon[i]
AeqC[j,state_con_id] = 1;
Aeq = vcat(Aeq,AeqC)
Beq = vcat(Beq,BeqC)
end
end
end
Aineq = eye(2*Ns)
Bineq = zeros(Ns*Na,1)
Constraint = [Aineq*X >= Bineq;Aeq*X == Beq]
p = maximize(C'*X,Constraint)
solve!(p, SCSSolver(verbose=0,max_iters=50000))
Values = -Constraint[2].dual
OccMeas = zeros(Ns,Na)
ActDist = zeros(Ns,Na)
OptAct = zeros(Int,Ns)
sol = X.value
for i=1:Na
OccMeas[:,i] = sol[(i-1)*Ns+1:i*Ns]
end
for i=1:Ns
normalizer = sum(OccMeas[i,:])
for j=1:Na
ActDist[i,j] = OccMeas[i,j]/normalizer
end
(maxval,index) = findmax(ActDist[i,:])
OptAct[i] = index
end
return Values,OptAct end
```
WARNING: isnan(x::AbstractArray{T}) where T \<: Number is deprecated, use isnan.(x) instead.
Stacktrace:
[1] depwarn(::String, ::Symbol) at ./deprecated.jl:70
[2] isnan(::Array{Float64,1}) at ./deprecated.jl:57
[3] populate\_solution!(::SCS.SCSMathProgModel, ::Convex.Problem, ::Dict{UInt64,Tuple{Int64,Int64}}, ::Array{Convex.ConicConstr,1}) at /Users/marcelord12/.julia/v0.6/Convex/src/solution.jl:118
[4] #solve!#25(::Bool, ::Bool, ::Bool, ::Function, ::Convex.Problem) at /Users/marcelord12/.julia/v0.6/Convex/src/solution.jl:38
[5] #solve!#24(::Array{Any,1}, ::Function, ::Convex.Problem, ::SCS.SCSSolver) at /Users/marcelord12/.julia/v0.6/Convex/src/solution.jl:13
[6] SolveCMDP(::CMDP) at /Users/marcelord12/Documents/phd2018/mppo/thesis/MDP/LP/MDP\_LP.jl:87
[7] include\_string(::String, ::String) at ./loading.jl:522
[8] include\_string(::String, ::String, ::Int64) at /Users/marcelord12/.julia/v0.6/CodeTools/src/eval.jl:30
[9] include\_string(::Module, ::String, ::String, ::Int64, ::Vararg{Int64,N} where N) at /Users/marcelord12/.julia/v0.6/CodeTools/src/eval.jl:34
[10] (::Atom.##102#107{String,Int64,String})() at /Users/marcelord12/.julia/v0.6/Atom/src/eval.jl:82
[11] withpath(::Atom.##102#107{String,Int64,String}, ::String) at /Users/marcelord12/.julia/v0.6/CodeTools/src/utils.jl:30
[12] withpath(::Function, ::String) at /Users/marcelord12/.julia/v0.6/Atom/src/eval.jl:38
[13] hideprompt(::Atom.##101#106{String,Int64,String}) at /Users/marcelord12/.julia/v0.6/Atom/src/repl.jl:67
[14] macro expansion at /Users/marcelord12/.julia/v0.6/Atom/src/eval.jl:80 [inlined]
[15] (::Atom.##100#105{Dict{String,Any}})() at ./task.jl:80
while loading /Users/marcelord12/Documents/phd2018/mppo/thesis/MDP/LP/Sample.jl, in expression starting on line 39
WARNING: Problem status UnknownError; solution may be inaccurate.
---
### Author:  [@kristoffer.carlsson](https://discourse.julialang.org/u/kristoffer.carlsson)
#### Post date: [August 15, 2018, 11:54pm UTC](https://discourse.julialang.org/t/warning-isnan-x-abstractarray-t-where-t-number-is-deprecated-use-isnan-x-instead/13537/2 "2018-08-15T23:54:35Z")
The deprecation warnings comes from inside `Convex.jl` so it seems that package needs to get updated.