diff --git a/test/bivariate.jl b/test/bivariate.jl
index 216c3880..1006ceef 100644
--- a/test/bivariate.jl
+++ b/test/bivariate.jl
@@ -56,5 +56,11 @@ for X in ([0.0], [0.0,0.0], [0.0,0.5], [-0.5:0.1:0.5;])
         @test all(k5.density .>= 0.0)
         @test sum(k5.density)*step(k5.x)*step(k5.y) ≈ 1.0
 
+        k6 = kde([X X],(r,r);kernel=D, weights=ones(X)/length(X))
+        @test k4.density ≈ k6.density
     end
 end
+
+k1 = kde([0.0 0.0; 1.0 1.0], (r,r), bandwidth=(1,1), weights=[0,1])
+k2 = kde([1.0 1.0], (r,r), bandwidth=(1,1))
+@test k1.density ≈ k2.density
diff --git a/test/univariate.jl b/test/univariate.jl
index 9a07a595..dcefd08d 100644
--- a/test/univariate.jl
+++ b/test/univariate.jl
@@ -2,7 +2,7 @@ using Base.Test
 using Distributions
 using KernelDensity
 
-import KernelDensity: kernel_dist, default_bandwidth, kde_boundary, kde_range, tabulate, UniformWeights
+import KernelDensity: kernel_dist, default_bandwidth, kde_boundary, kde_range, tabulate
 
 for D in [Normal,Uniform,Logistic]
     d = kernel_dist(D,0.5)
@@ -54,10 +54,10 @@ for X in ([0.0], [0.0,0.0], [0.0,0.5], [-0.5:0.1:0.5;])
         @test sum(k5.density)*step(k5.x) ≈ 1.0
 
         k6 = kde(X,r;kernel=D, weights=ones(X)/length(X))
-        @test_approx_eq k4.density k6.density
+        @test k4.density ≈ k6.density
     end
 end
 
 k1 = kde([0.0, 1.], r, bandwidth=1, weights=[0,1])
 k2 = kde([1.], r, bandwidth=1)
-@test k1.density == k2.density
+@test k1.density ≈ k2.density