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Vector -> AbstractVector #112

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Merged
merged 2 commits into from
Jul 5, 2017
Merged

Vector -> AbstractVector #112

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ba5daf4
abstract vectors
ChrisRackauckas Jul 1, 2017
e8dd087
abstraction merge with function changes
ChrisRackauckas Jul 1, 2017
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33 changes: 16 additions & 17 deletions src/finite_difference.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -98,15 +98,15 @@ end
##############################################################################

function finite_difference!{S <: Number, T <: Number}(f,
x::Vector{S},
g::Vector{T},
x::AbstractVector{S},
g::AbstractVector{T},
dtype::Symbol)
# What is the dimension of x?
n = length(x)

# Iterate over each dimension of the gradient separately.
# Use xplusdx to store x + dx instead of creating a new vector on each pass.
# Use xminusdx to store x - dx instead of creating a new vector on each pass.
# Use xplusdx to store x + dx instead of creating a new AbstractVector on each pass.
# Use xminusdx to store x - dx instead of creating a new AbstractVector on each pass.
if dtype == :forward
# Establish a baseline value of f(x).
f_x = f(x)
Expand Down Expand Up @@ -136,10 +136,10 @@ function finite_difference!{S <: Number, T <: Number}(f,
return
end
function finite_difference{T <: Number}(f,
x::Vector{T},
x::AbstractVector{T},
dtype::Symbol = :central)
# Allocate memory for gradient
g = Vector{Float64}(length(x))
g = AbstractVector{Float64}(length(x))

# Mutate allocated gradient
finite_difference!(f, float(x), g, dtype)
Expand All @@ -157,8 +157,8 @@ end
function finite_difference_jacobian!{R <: Number,
S <: Number,
T <: Number}(f,
x::Vector{R},
f_x::Vector{S},
x::AbstractVector{R},
f_x::AbstractVector{S},
J::Array{T},
dtype::Symbol = :central)
# What is the dimension of x?
Expand Down Expand Up @@ -191,7 +191,7 @@ function finite_difference_jacobian!{R <: Number,
return
end
function finite_difference_jacobian{T <: Number}(f,
x::Vector{T},
x::AbstractVector{T},
dtype::Symbol = :central)
# Establish a baseline for f_x
f_x = f(x)
Expand Down Expand Up @@ -232,7 +232,7 @@ end

function finite_difference_hessian!{S <: Number,
T <: Number}(f,
x::Vector{S},
x::AbstractVector{S},
H::Array{T})
# What is the dimension of x?
n = length(x)
Expand Down Expand Up @@ -264,7 +264,7 @@ function finite_difference_hessian!{S <: Number,
Base.LinAlg.copytri!(H,'U')
end
function finite_difference_hessian{T <: Number}(f,
x::Vector{T})
x::AbstractVector{T})
# What is the dimension of x?
n = length(x)

Expand All @@ -279,7 +279,7 @@ function finite_difference_hessian{T <: Number}(f,
end
function finite_difference_hessian{T <: Number}(f,
g,
x::Vector{T},
x::AbstractVector{T},
dtype::Symbol = :central)
finite_difference_jacobian(g, x, dtype)
end
Expand Down Expand Up @@ -329,16 +329,15 @@ end
##############################################################################

# The function "dirderivative" calculates directional derivatives in the direction v.
# The function supplied must have the form Vector{Float64} -> Float64
# function dirderivative(f, v::Vector{Float64}, x0::Vector{Float64}, h::Float64, twoside::Bool)
# function dirderivative(f, v::AbstractVector{Float64}, x0::AbstractVector{Float64}, h::Float64, twoside::Bool)
# derivative(t::Float64 -> f(x0 + v*t) / norm(v), 0.0, h, twoside)
# end
# function dirderivative(f, v::Vector{Float64}, x0::Vector{Float64}, h::Float64)
# function dirderivative(f, v::AbstractVector{Float64}, x0::AbstractVector{Float64}, h::Float64)
# dirderivative(f, v, x0, h, true)
# end
# function dirderivative(f, v::Vector{Float64}, x0::Vector{Float64}, )
# function dirderivative(f, v::AbstractVector{Float64}, x0::AbstractVector{Float64}, )
# derivative(f, v, x0, 0.0001)
# end
# function dirderivative(f, v::Vector{Float64})
# function dirderivative(f, v::AbstractVector{Float64})
# x -> dirderivative(f, v, x)
# end