Be slightly more strict on data type, see #41

src/train.jl

@@ -28,7 +28,7 @@ GMM(x::Vector{T}) where T <: AbstractFloat = GMM(reshape(x, length(x), 1))  # st
 
 ## constructors based on data or matrix
 function GMM(n::Int, x::DataOrMatrix{T}; method::Symbol=:kmeans, kind=:diag,
-             nInit::Int=50, nIter::Int=10, nFinal::Int=nIter, sparse=0) where T
+             nInit::Int=50, nIter::Int=10, nFinal::Int=nIter, sparse=0) where T <: AbstractFloat
     if n < 2
         GMM(x, kind=kind)
     elseif method==:split
@@ -40,7 +40,7 @@ function GMM(n::Int, x::DataOrMatrix{T}; method::Symbol=:kmeans, kind=:diag,
     end
 end
 ## a 1-dimensional Gaussian can be initialized with a vector, skip kind=
-GMM(n::Int, x::Vector{T}; method::Symbol=:kmeans, nInit::Int=50, nIter::Int=10, nFinal::Int=nIter, sparse=0) where T = GMM(n, reshape(x, length(x), 1); method=method, kind=:diag, nInit=nInit, nIter=nIter, nFinal=nFinal, sparse=sparse)
+GMM(n::Int, x::Vector{T}; method::Symbol=:kmeans, nInit::Int=50, nIter::Int=10, nFinal::Int=nIter, sparse=0) where T <: AbstractFloat = GMM(n, reshape(x, length(x), 1); method=method, kind=:diag, nInit=nInit, nIter=nIter, nFinal=nFinal, sparse=sparse)
 
 ## we sometimes end up with pathological gmms
 function sanitycheck!(gmm::GMM)
@@ -72,7 +72,7 @@ end
 
 
 ## initialize GMM using Clustering.kmeans (which uses a method similar to kmeans++)
-function GMMk(n::Int, x::DataOrMatrix{T}; kind=:diag, nInit::Int=50, nIter::Int=10, sparse=0) where T
+function GMMk(n::Int, x::DataOrMatrix{T}; kind=:diag, nInit::Int=50, nIter::Int=10, sparse=0) where T <: AbstractFloat
     nₓ, d = size(x)
     hist = [History(@sprintf("Initializing GMM, %d Gaussians %s covariance %d dimensions using %d data points", n, diag, d, nₓ))]
     @info(last(hist).s)