Range -> AbstractRange

Author: awllee

README.md

@@ -41,7 +41,7 @@ least-squares cross validation. It accepts the above keyword arguments, except
 
 There are also some slightly more advanced interfaces:
 ```
-kde(data, midpoints::Range)
+kde(data, midpoints::R) where R<:AbstractRange
 ```
 allows specifying the internal grid to use. Optional keyword arguments are
 `kernel` and `bandwidth`.
@@ -53,7 +53,7 @@ allows specifying the exact distribution to use as the kernel. Optional
 keyword arguments are `boundary` and `npoints`.
 
 ```
-kde(data, midpoints::Range, dist::Distribution)
+kde(data, midpoints::R, dist::Distribution) where R<:AbstractRange
 ```
 allows specifying both the distribution and grid.
 
@@ -95,4 +95,3 @@ pdf(ik, x)
 ```
 
 `InterpKDE` will pass any extra arguments to `interpolate`.
-

src/bivariate.jl

@@ -1,5 +1,5 @@
 # Store both grid and density for KDE over R2
-mutable struct BivariateKDE{Rx<:Range,Ry<:Range} <: AbstractKDE
+mutable struct BivariateKDE{Rx<:AbstractRange,Ry<:AbstractRange} <: AbstractKDE
     x::Rx
     y::Ry
     density::Matrix{Float64}
@@ -26,7 +26,8 @@ function default_bandwidth(data::Tuple{RealVector,RealVector})
 end
 
 # tabulate data for kde
-function tabulate(data::Tuple{RealVector, RealVector}, midpoints::Tuple{Range, Range}, weights::Weights = default_weights(data))
+function tabulate(data::Tuple{RealVector, RealVector}, midpoints::Tuple{Rx, Ry},
+        weights::Weights = default_weights(data)) where {Rx<:AbstractRange,Ry<:AbstractRange}
     xdata, ydata = data
     ndata = length(xdata)
     length(ydata) == ndata || error("data vectors must be of same length")
@@ -87,7 +88,8 @@ const BivariateDistribution = Union{MultivariateDistribution,Tuple{UnivariateDis
 
 default_weights(data::Tuple{RealVector, RealVector}) = UniformWeights(length(data[1]))
 
-function kde(data::Tuple{RealVector, RealVector}, weights::Weights, midpoints::Tuple{Range, Range}, dist::BivariateDistribution)
+function kde(data::Tuple{RealVector, RealVector}, weights::Weights, midpoints::Tuple{Rx, Ry},
+        dist::BivariateDistribution) where {Rx<:AbstractRange,Ry<:AbstractRange}
     k = tabulate(data, midpoints, weights)
     conv(k,dist)
 end
@@ -104,8 +106,9 @@ function kde(data::Tuple{RealVector, RealVector}, dist::BivariateDistribution;
     kde(data,weights,(xmid,ymid),dist)
 end
 
-function kde(data::Tuple{RealVector, RealVector}, midpoints::Tuple{Range, Range};
-             bandwidth=default_bandwidth(data), kernel=Normal, weights::Weights = default_weights(data))
+function kde(data::Tuple{RealVector, RealVector}, midpoints::Tuple{Rx, Ry};
+             bandwidth=default_bandwidth(data), kernel=Normal,
+             weights::Weights = default_weights(data)) where {Rx<:AbstractRange,Ry<:AbstractRange}
 
     dist = kernel_dist(kernel,bandwidth)
     kde(data,weights,midpoints,dist)

src/univariate.jl

@@ -1,5 +1,5 @@
 # Store both grid and density for KDE over the real line
-mutable struct UnivariateKDE{R<:Range} <: AbstractKDE
+mutable struct UnivariateKDE{R<:AbstractRange} <: AbstractKDE
     x::R
     density::Vector{Float64}
 end
@@ -80,7 +80,7 @@ const Weights = Union{UniformWeights, RealVector, StatsBase.Weights}
 
 
 # tabulate data for kde
-function tabulate(data::RealVector, midpoints::Range, weights::Weights=default_weights(data))
+function tabulate(data::RealVector, midpoints::R, weights::Weights=default_weights(data)) where R<:AbstractRange
     npoints = length(midpoints)
     s = step(midpoints)
 
@@ -131,7 +131,7 @@ function conv(k::UnivariateKDE, dist::UnivariateDistribution)
 end
 
 # main kde interface methods
-function kde(data::RealVector, weights::Weights, midpoints::Range, dist::UnivariateDistribution)
+function kde(data::RealVector, weights::Weights, midpoints::R, dist::UnivariateDistribution) where R<:AbstractRange
     k = tabulate(data, midpoints, weights)
     conv(k,dist)
 end
@@ -143,8 +143,8 @@ function kde(data::RealVector, dist::UnivariateDistribution;
     kde(data,weights,midpoints,dist)
 end
 
-function kde(data::RealVector, midpoints::Range;
-             bandwidth=default_bandwidth(data), kernel=Normal, weights=default_weights(data))
+function kde(data::RealVector, midpoints::R;
+             bandwidth=default_bandwidth(data), kernel=Normal, weights=default_weights(data)) where R<:AbstractRange
     bandwidth > 0.0 || error("Bandwidth must be positive")
     dist = kernel_dist(kernel,bandwidth)
     kde(data,weights,midpoints,dist)
@@ -162,10 +162,10 @@ end
 #   B. W. Silverman (1986)
 #   sections 3.4.3 (pp. 48-52) and 3.5 (pp. 61-66)
 
-function kde_lscv(data::RealVector, midpoints::Range;
+function kde_lscv(data::RealVector, midpoints::R;
                   kernel=Normal,
                   bandwidth_range::Tuple{Real,Real}=(h=default_bandwidth(data); (0.25*h,1.5*h)),
-                  weights=default_weights(data))
+                  weights=default_weights(data)) where R<:AbstractRange
 
     ndata = length(data)
     k = tabulate(data, midpoints, weights)