type handling

Author: baggepinnen

Project.toml

@@ -1,7 +1,7 @@
 name = "SingularSpectrumAnalysis"
 uuid = "77f5812f-bd1f-5f6e-9b72-07b28d8bbbc3"
 authors = ["baggepinnen <[email protected]>"]
-version = "0.4.0"
+version = "0.4.1"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

src/SingularSpectrumAnalysis.jl

@@ -94,11 +94,11 @@ function reconstruct(USV, trends, seasonal::AbstractArray)
     yrt, yrs
 end
 
-function reconstruct(USV, groupings::AbstractArray)
+function reconstruct(USV::SVD{T}, groupings::AbstractArray) where T
     M = length(groupings)
     K,L = size(USV.U)
     N = K+L-1
-    yr = zeros(N,M)
+    yr = zeros(T,N,M)
     for m = 1:M
         X = elementary(USV,groupings[m])
         yr[:,m] = unhankel(X)
@@ -110,7 +110,7 @@ end
 trend, seasonal_groupings = autogroup(USV, th = 0.95)
 Try to automatically group singular values. `th ∈ (0,1)` determins the percentage of variance to explain.
 """
-function autogroup(USV, th = 0.95)
+function autogroup(USV::SVD, th = 0.95)
     nS = USV.S .- minimum(USV.S)
     nS ./= sum(nS)
     cs = cumsum(nS)

test/runtests.jl

@@ -67,7 +67,7 @@ const SSA = SingularSpectrumAnalysis
 
         yt, ys = analyze(yn, L, robust=false)
         A,x = fit_trend(yt, 1)
-        @test x[2] ≈ 0.001 rtol = 1e-3
+        @test x[2] ≈ 0.001 rtol = 1e-2
 
         USV = hsvd(yn,L)