Added test for qda discriminants

src/qda.jl

@@ -75,11 +75,12 @@ function discriminants_qda{T<:BlasReal}(
     δ = Array(T, n, k)  # discriminant function values
     H = Array(T, n, p)  # temporary array to prevent re-allocation k times
     Q = Array(T, n, p)  # Q := H*W_k
+    hᵀh = Array(T, n)
     for j = 1:k
         translate!(copy!(H, Z), -vec(M[j,:]))
-        s = dot_rows(BLAS.gemm!('N', 'N', one(T), H, W_k[j], zero(T), Q))
+        dotrows!(BLAS.gemm!('N', 'N', one(T), H, W_k[j], zero(T), Q), hᵀh)
         for i = 1:n
-            δ[i, j] = -s[i]/2 + log(priors[j])
+            δ[i, j] = -hᵀh[i]/2 + log(priors[j])
         end
     end
     δ

test/test_qda.jl

@@ -118,3 +118,21 @@ for T in FloatingPointTypes
         @test_approx_eq model.W_k[i] W_k_tmp[i]
     end
 end
+
+info("Testing ", MOD.discriminants_qda)
+for T in FloatingPointTypes
+    X_tmp = copy(convert(Matrix{T}, X))
+    priors_tmp = convert(Vector{T}, priors)
+
+    model = qda(X_tmp, y)
+    Z2 = hcat([MOD.dotrows((X_tmp .- M[i,:])*model.W_k[i]) for i in eachindex(priors_tmp)]...)
+    δ = -Z2/2 .+ log(priors_tmp)'
+    @test_approx_eq δ MOD.discriminants(model, X_tmp)
+
+    for γ in (zero(T), convert(T, 0.25), convert(T, 0.75), one(T))
+        model = qda(X_tmp, y)
+        Z2 = hcat([MOD.dotrows((X_tmp .- M[i,:])*model.W_k[i]) for i in eachindex(priors_tmp)]...)
+        δ = -Z2/2 .+ log(priors_tmp)'
+        @test_approx_eq δ MOD.discriminants(model, X_tmp)
+    end
+end