fix the bwlcv methods for multivariate kde

src/bandwidth.jl

@@ -144,7 +144,7 @@ function bwlcv(xdata::RealVector, kernel::Function)
     return Optim.minimizer(Optim.optimize(h->lcv(xdata,kernel,h,w,n), hlb, hub, iterations=200,abs_tol=h0/n^2))
 end
 
-function lcv(xdata::RealMatrix, kernel::Array{Function, 1}, h::RealVector, w::Vector, n::Int)
+function lcv(xdata::RealMatrix, kernel::Vector, h::RealVector, w::Vector, n::Int)
 #     -mean(kerneldensity(xdata,xdata,kernel,h)) + mean(map(kernel, xdata, xdata, h))
     if any(h .<= 0.0)
         return Inf
@@ -170,7 +170,7 @@ function lcv(xdata::RealMatrix, kernel::Array{Function, 1}, h::RealVector, w::Ve
     end
     -ll
 end
-function bwlcv(xdata::RealMatrix, kernel::Array{Function, 1})
+function bwlcv(xdata::RealMatrix, kernel::Vector)
     n, p = size(xdata)
     w = ones(n)
     h0 = zeros(p)
@@ -191,7 +191,12 @@ function bwlcv(xdata::RealMatrix, kernel::Array{Function, 1})
             hub[j] = h0[j]
         end
     end
-    Optim.minimizer(Optim.optimize(h->lcv(xdata, kernel, h, w, n), h0))
+    h = Optim.minimizer(Optim.optimize(h->lcv(xdata, kernel, h, w, n), h0))
+    if all(hlb .<= h .<= hub)
+        return h
+    else
+        return h0
+    end
 end
 
 

src/density.jl

@@ -15,11 +15,12 @@ function kerneldensity(xdata::RealVector; xeval::RealVector=xdata, lb::Real=-Inf
     return den
 end
 
-function kerneldensity(xdata::RealMatrix; xeval::RealMatrix=xdata, 
-    kernel::Array{Function, 1}=[gaussiankernel for i in 1:size(xdata)[2]], h::RealVector=bwlcv(xdata, kernel))
-    
+function kerneldensity(xdata::RealMatrix; xeval::RealMatrix=xdata,
+    kernel::Vector=[gaussiankernel for i in 1:size(xdata)[2]], h::RealVector=-Inf .* ones(size(xdata, 2)))
+
     if any(h .<= 0)
-        error("h < 0!")
+        h = bwlcv(xdata, kernel)
+        warn("The user are responsible for giving the bandwidth! The defaults may not work well.")
     end
     m, p=size(xeval)
     n, p1 = size(xdata)

test/testreg.jl

@@ -1,6 +1,7 @@
 
 ##Univariate kerneldensity and regression
 using Distributions
+srand(2017);
 x=rand(Normal(10), 500)
 xeval=linspace(minimum(x), maximum(x), 100)
 h = bwlscv(x, gaussiankernel)
@@ -28,6 +29,11 @@ yfit1=npr(x, y, xeval=xeval, reg=locallinear)
 cb=bootstrapCB(x, y, xeval=xeval)
 @test mean(vec(cb[1,:]) .<= yfit1 .<= vec(cb[2,:])) > .8
 
+#multivariate density estimation
+x = rand(Normal(10), 500, 3)
+denvalues = kerneldensity(x, h = [1.0, 1.0, 1.0])
+@test all(denvalues .>= 0)
+
 #multivariate regression
 x = rand(Normal(10), 500, 3)
 y = x * ones(3) .+ x.^2 *ones(3)