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Change all Array and Vector to AbstractArray and AbstractVector #87

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Change all Array and Vector to AbstractArray and AbstractVector #87

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4 changes: 2 additions & 2 deletions src/check_derivative.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ function check_derivative(f::Function, g::Function, x::Number)
return maximum(abs(g(x) - auto_g(x)))
end

function check_gradient{T <: Number}(f::Function, g::Function, x::Vector{T})
function check_gradient{T <: Number}(f::Function, g::Function, x::AbstractVector{T})
auto_g = gradient(f)
return maximum(abs(g(x) - auto_g(x)))
end
Expand All @@ -13,7 +13,7 @@ function check_second_derivative(f::Function, h::Function, x::Number)
return maximum(abs(h(x) - auto_h(x)))
end

function check_hessian{T <: Number}(f::Function, h::Function, x::Vector{T})
function check_hessian{T <: Number}(f::Function, h::Function, x::AbstractVector{T})
auto_h = hessian(f)
return maximum(abs(h(x) - auto_h(x)))
end
30 changes: 15 additions & 15 deletions src/derivative.jl
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Expand Up @@ -2,19 +2,19 @@ function derivative(f::Function, ftype::Symbol, dtype::Symbol)
if ftype == :scalar
g(x::Number) = finite_difference(f, float(x), dtype)
elseif ftype == :vector
g(x::Vector) = finite_difference(f, float(x), dtype)
g(x::AbstractVector) = finite_difference(f, float(x), dtype)
else
error("ftype must :scalar or :vector")
end
return g
end
Compat.@compat derivative{T <: Number}(f::Function, x::Union{T, Vector{T}}, dtype::Symbol = :central) = finite_difference(f, float(x), dtype)
Compat.@compat derivative{T <: Number}(f::Function, x::Union{T, AbstractVector{T}}, dtype::Symbol = :central) = finite_difference(f, float(x), dtype)
derivative(f::Function, dtype::Symbol = :central) = derivative(f, :scalar, dtype)

Compat.@compat gradient{T <: Number}(f::Function, x::Union{T, Vector{T}}, dtype::Symbol = :central) = finite_difference(f, float(x), dtype)
Compat.@compat gradient{T <: Number}(f::Function, x::Union{T, AbstractVector{T}}, dtype::Symbol = :central) = finite_difference(f, float(x), dtype)
gradient(f::Function, dtype::Symbol = :central) = derivative(f, :vector, dtype)

Compat.@compat function Base.gradient{T <: Number}(f::Function, x::Union{T, Vector{T}}, dtype::Symbol = :central)
Compat.@compat function Base.gradient{T <: Number}(f::Function, x::Union{T, AbstractVector{T}}, dtype::Symbol = :central)
Base.warn_once("The finite difference methods from Calculus.jl no longer extend Base.gradient and should be called as Calculus.gradient instead. This usage is deprecated.")
Calculus.gradient(f,x,dtype)
end
Expand All @@ -26,11 +26,11 @@ end

ctranspose(f::Function) = derivative(f)

function jacobian{T <: Number}(f::Function, x::Vector{T}, dtype::Symbol)
function jacobian{T <: Number}(f::Function, x::AbstractVector{T}, dtype::Symbol)
finite_difference_jacobian(f, x, dtype)
end
function jacobian(f::Function, dtype::Symbol)
g(x::Vector) = finite_difference_jacobian(f, x, dtype)
g(x::AbstractVector) = finite_difference_jacobian(f, x, dtype)
return g
end
jacobian(f::Function) = jacobian(f, :central)
Expand All @@ -39,22 +39,22 @@ function second_derivative(f::Function, g::Function, ftype::Symbol, dtype::Symbo
if ftype == :scalar
h(x::Number) = finite_difference_hessian(f, g, x, dtype)
elseif ftype == :vector
h(x::Vector) = finite_difference_hessian(f, g, x, dtype)
h(x::AbstractVector) = finite_difference_hessian(f, g, x, dtype)
else
error("ftype must :scalar or :vector")
end
return h
end
Compat.@compat function second_derivative{T <: Number}(f::Function, g::Function, x::Union{T, Vector{T}}, dtype::Symbol)
Compat.@compat function second_derivative{T <: Number}(f::Function, g::Function, x::Union{T, AbstractVector{T}}, dtype::Symbol)
finite_difference_hessian(f, g, x, dtype)
end
Compat.@compat function hessian{T <: Number}(f::Function, g::Function, x::Union{T, Vector{T}}, dtype::Symbol)
Compat.@compat function hessian{T <: Number}(f::Function, g::Function, x::Union{T, AbstractVector{T}}, dtype::Symbol)
finite_difference_hessian(f, g, x, dtype)
end
Compat.@compat function second_derivative{T <: Number}(f::Function, g::Function, x::Union{T, Vector{T}})
Compat.@compat function second_derivative{T <: Number}(f::Function, g::Function, x::Union{T, AbstractVector{T}})
finite_difference_hessian(f, g, x, :central)
end
Compat.@compat function hessian{T <: Number}(f::Function, g::Function, x::Union{T, Vector{T}})
Compat.@compat function hessian{T <: Number}(f::Function, g::Function, x::Union{T, AbstractVector{T}})
finite_difference_hessian(f, g, x, :central)
end
function second_derivative(f::Function, x::Number, dtype::Symbol)
Expand All @@ -63,10 +63,10 @@ end
function hessian(f::Function, x::Number, dtype::Symbol)
finite_difference_hessian(f, derivative(f), x, dtype)
end
function second_derivative{T <: Number}(f::Function, x::Vector{T}, dtype::Symbol)
function second_derivative{T <: Number}(f::Function, x::AbstractVector{T}, dtype::Symbol)
finite_difference_hessian(f, gradient(f), x, dtype)
end
function hessian{T <: Number}(f::Function, x::Vector{T}, dtype::Symbol)
function hessian{T <: Number}(f::Function, x::AbstractVector{T}, dtype::Symbol)
finite_difference_hessian(f, gradient(f), x, dtype)
end
function second_derivative(f::Function, x::Number)
Expand All @@ -75,10 +75,10 @@ end
function hessian(f::Function, x::Number)
finite_difference_hessian(f, derivative(f), x, :central)
end
function second_derivative{T <: Number}(f::Function, x::Vector{T})
function second_derivative{T <: Number}(f::Function, x::AbstractVector{T})
finite_difference_hessian(f, gradient(f), x, :central)
end
function hessian{T <: Number}(f::Function, x::Vector{T})
function hessian{T <: Number}(f::Function, x::AbstractVector{T})
finite_difference_hessian(f, gradient(f), x, :central)
end
second_derivative(f::Function, g::Function, dtype::Symbol) = second_derivative(f, g, :scalar, dtype)
Expand Down
22 changes: 11 additions & 11 deletions src/finite_difference.jl
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Expand Up @@ -98,8 +98,8 @@ end
##############################################################################

function finite_difference!{S <: Number, T <: Number}(f::Function,
x::Vector{S},
g::Vector{T},
x::AbstractVector{S},
g::AbstractVector{T},
dtype::Symbol)
# What is the dimension of x?
n = length(x)
Expand Down Expand Up @@ -136,7 +136,7 @@ function finite_difference!{S <: Number, T <: Number}(f::Function,
return
end
function finite_difference{T <: Number}(f::Function,
x::Vector{T},
x::AbstractVector{T},
dtype::Symbol = :central)
# Allocate memory for gradient
g = Array(Float64, length(x))
Expand All @@ -157,9 +157,9 @@ end
function finite_difference_jacobian!{R <: Number,
S <: Number,
T <: Number}(f::Function,
x::Vector{R},
f_x::Vector{S},
J::Array{T},
x::AbstractVector{R},
f_x::AbstractVector{S},
J::AbstractArray{T},
dtype::Symbol = :central)
# What is the dimension of x?
m, n = size(J)
Expand Down Expand Up @@ -192,7 +192,7 @@ function finite_difference_jacobian!{R <: Number,
return
end
function finite_difference_jacobian{T <: Number}(f::Function,
x::Vector{T},
x::AbstractVector{T},
dtype::Symbol = :central)
# Establish a baseline for f_x
f_x = f(x)
Expand Down Expand Up @@ -233,8 +233,8 @@ end

function finite_difference_hessian!{S <: Number,
T <: Number}(f::Function,
x::Vector{S},
H::Array{T})
x::AbstractVector{S},
H::AbstractArray{T})
# What is the dimension of x?
n = length(x)

Expand Down Expand Up @@ -265,7 +265,7 @@ function finite_difference_hessian!{S <: Number,
Base.LinAlg.copytri!(H,'U')
end
function finite_difference_hessian{T <: Number}(f::Function,
x::Vector{T})
x::AbstractVector{T})
# What is the dimension of x?
n = length(x)

Expand All @@ -280,7 +280,7 @@ function finite_difference_hessian{T <: Number}(f::Function,
end
function finite_difference_hessian{T <: Number}(f::Function,
g::Function,
x::Vector{T},
x::AbstractVector{T},
dtype::Symbol = :central)
finite_difference_jacobian(g, x, dtype)
end
Expand Down
6 changes: 6 additions & 0 deletions test/check_derivative.jl
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Expand Up @@ -21,3 +21,9 @@
@test check_hessian(x -> sin(x[1]) + cos(x[2]), x -> [-sin(x[1]) 0.0; 0.0 -cos(x[2])], [10.0, 10.0]) < 10e-4
@test check_hessian(x -> sin(x[1]) + cos(x[2]), x -> [-sin(x[1]) 0.0; 0.0 -cos(x[2])], [100.0, 100.0]) < 10e-4
@test check_hessian(x -> sin(x[1]) + cos(x[2]), x -> [-sin(x[1]) 0.0; 0.0 -cos(x[2])], [1000.0, 1000.0]) < 10e-4

# Test functionality for other AbstractArray types
@test check_gradient(x -> sin(x[1]) + cos(x[2]), x -> [cos(x[1]), -sin(x[2])],
sub([0.0, 0.0],:)) < 10e-4
@test check_hessian(x -> sin(x[1]) + cos(x[2]), x -> [-sin(x[1]) 0.0; 0.0 -cos(x[2])],
sub([0.0, 0.0],:)) < 10e-4
17 changes: 15 additions & 2 deletions test/derivative.jl
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Expand Up @@ -9,10 +9,14 @@ f1(x::Real) = sin(x)
@test norm(derivative(f1, :central)(0.0) - cos(0.0)) < 10e-4
@test norm(derivative(f1)(0.0) - cos(0.0)) < 10e-4

f2(x::Vector) = sin(x[1])
f2(x::AbstractVector) = sin(x[1])
@test norm(derivative(f2, :vector, :forward)([0.0]) .- cos(0.0)) < 10e-4
@test norm(derivative(f2, :vector, :central)([0.0]) .- cos(0.0)) < 10e-4

# Test functionality for SubArrays
@test norm(derivative(f2, :vector, :forward)(sub([0.0],:)) .- cos(0.0)) < 10e-4
@test norm(derivative(f2, :vector, :central)(sub([0.0],:)) .- cos(0.0)) < 10e-4

#
# ctranspose overloading
#
Expand All @@ -26,11 +30,16 @@ end
# gradient()
#

f4(x::Vector) = (100.0 - x[1])^2 + (50.0 - x[2])^2
f4(x::AbstractVector) = (100.0 - x[1])^2 + (50.0 - x[2])^2
@test norm(Calculus.gradient(f4, :forward)([100.0, 50.0]) - [0.0, 0.0]) < 10e-4
@test norm(Calculus.gradient(f4, :central)([100.0, 50.0]) - [0.0, 0.0]) < 10e-4
@test norm(Calculus.gradient(f4)([100.0, 50.0]) - [0.0, 0.0]) < 10e-4

# Test for SubArrays
@test norm(Calculus.gradient(f4, :forward)(sub([100.0, 50.0],:)) - [0.0, 0.0]) < 10e-4
@test norm(Calculus.gradient(f4, :central)(sub([100.0, 50.0],:)) - [0.0, 0.0]) < 10e-4
@test norm(Calculus.gradient(f4)(sub([100.0, 50.0],:)) - [0.0, 0.0]) < 10e-4

#
# second_derivative()
#
Expand All @@ -47,3 +56,7 @@ f4(x::Vector) = (100.0 - x[1])^2 + (50.0 - x[2])^2
f5(x) = sin(x[1]) + cos(x[2])
@test norm(Calculus.gradient(f5)([0.0, 0.0]) - [cos(0.0), -sin(0.0)]) < 10e-4
@test norm(hessian(f5)([0.0, 0.0]) - [-sin(0.0) 0.0; 0.0 -cos(0.0)]) < 10e-4

# And for SubArrays again
@test norm(Calculus.gradient(f5)(sub([0.0, 0.0],:)) - [cos(0.0), -sin(0.0)]) < 10e-4
@test norm(hessian(f5)(sub([0.0, 0.0],:)) - [-sin(0.0) 0.0; 0.0 -cos(0.0)]) < 10e-4
9 changes: 9 additions & 0 deletions test/finite_difference.jl
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Expand Up @@ -30,6 +30,11 @@
@test norm(Calculus.finite_difference(x -> exp(-x[1]), [1.0], :central) - [-exp(-1.0)]) < 10e-4
@test norm(Calculus.finite_difference(x -> exp(-x[1]), [1.0]) - [-exp(-1.0)]) < 10e-4

# Gradients of f when x is a SubArrays
@test norm(Calculus.finite_difference(x -> exp(-x[1]), sub([1.0],:), :forward) - [-exp(-1.0)]) < 10e-4
@test norm(Calculus.finite_difference(x -> exp(-x[1]), sub([1.0],:), :central) - [-exp(-1.0)]) < 10e-4
@test norm(Calculus.finite_difference(x -> exp(-x[1]), sub([1.0],:)) - [-exp(-1.0)]) < 10e-4

#
# Second derivatives of f: R -> R
#
Expand All @@ -52,6 +57,10 @@ gx = Calculus.gradient(fx)
@test norm(Calculus.finite_difference_hessian(fx, gx, [0.0, 0.0], :central) - [-sin(0.0) 0.0; 0.0 -cos(0.0)]) < 10e-4
@test norm(Calculus.finite_difference_hessian(fx, [0.0, 0.0]) - [-sin(0.0) 0.0; 0.0 -cos(0.0)]) < 10e-4

@test norm(gx(sub([0.0, 0.0],:)) - [cos(0.0), -sin(0.0)]) < 10e-4
@test norm(Calculus.finite_difference_hessian(fx, gx, sub([0.0, 0.0],:), :central) - [-sin(0.0) 0.0; 0.0 -cos(0.0)]) < 10e-4
@test norm(Calculus.finite_difference_hessian(fx, sub([0.0, 0.0],:)) - [-sin(0.0) 0.0; 0.0 -cos(0.0)]) < 10e-4

#
# Taylor Series first derivatives
#
Expand Down