Remove unneeded stuff from tests

Author: kagalenko-m-b

src/signalcorr.jl

@@ -302,7 +302,7 @@ function crosscov!(r::AbstractMatrix, x::AbstractVector, y::AbstractMatrix, lags
     return r
 end
 
-function crosscov!(r::AbstractArray{<:Any}, x::AbstractMatrix, y::AbstractMatrix, lags::AbstractVector{<:Integer}; demean::Bool=true)
+function crosscov!(r::AbstractArray{<:Any,3}, x::AbstractMatrix, y::AbstractMatrix, lags::AbstractVector{<:Integer}; demean::Bool=true)
     lx = size(x, 1)
     nx = size(x, 2)
     ny = size(y, 2)
@@ -458,7 +458,7 @@ function crosscor!(r::AbstractMatrix, x::AbstractVector, y::AbstractMatrix, lags
     return r
 end
 
-function crosscor!(r::AbstractArray{<:Any}, x::AbstractMatrix, y::AbstractMatrix, lags::AbstractVector{<:Integer}; demean::Bool=true)
+function crosscor!(r::AbstractArray{<:Any,3}, x::AbstractMatrix, y::AbstractMatrix, lags::AbstractVector{<:Integer}; demean::Bool=true)
     lx = size(x, 1)
     nx = size(x, 2)
     ny = size(y, 2)

test/rcall_test.jl

@@ -129,25 +129,17 @@ pacf_ls = [-1.598495044296996e-03 - 2.915104118351207e-01im
 
 @test pacf(x1, 1:4) ≈ pacf_ls[1:4]
 
-## pacf Yule-Walker
-
-function yulewalker_qr(v::AbstractVector)
-    A = toeplitz(v)
-    b = v[2:end]
-    x = -A\b
-end
-function toeplitz(v::AbstractVector{T}) where T
-    N = length(v)
-    A = zeros(T, N - 1, N - 1)
-    for n in 1:N-1
-        A[n, n+1:end] = conj(v[2:N-n])
-        A[n, 1:n] = reverse(v[1:n])
-    end
-    return A
-end
-# durbin solver 
+## PACF Durbin solver
+p_qr = [ 0.007061066965510003 + 0.2467057077053922im
+         0.3390065585042673 - 0.19328284216710082im
+         0.17336154780689775 - 0.026816622064955047im
+         0.17103798323175215 - 0.16022674917138569im
+         -0.10466472359738815 - 0.1320218198465664im
+         0.0486999464280363 - 0.10069213696609468im
+         0.056254003938430636 - 0.09745957137815701im
+         -0.07247287124498031 + 0.05734525689737796im
+         0.10967412213230482 - 0.05581001671362978im]
 acf = autocor(x1)
-p_qr = [yulewalker_qr(acf[1:n])[n-1] for n in 2:length(acf)]
 y = similar(acf[2:end])
 pd = similar(acf[2:end])
 StatsBase.durbin!(acf[2:end], y, pd)