Loading index.jl +2 −2 Original line number Diff line number Diff line Loading @@ -84,8 +84,8 @@ dparse_utc(str::AbstractString) = ZonedDateTime(DateTime(str[1:end-1], dformat_r # load the K-medians we use to approximate the data realtimeKmedians = sparse(convert(Array{Float32, 2}, readdlm(args["realtime-model"][1:end-5]*"20medians.txt"))) realtimeKmediansWeights = convert(Array{Float32, 1}, vec(readdlm(args["realtime-model"][1:end-5]*"20mediansWeights.txt"))) preopKmedians = sparse(convert(Array{Float32, 2}, readdlm(args["preop-model"][1:end-5]*"19medians.txt"))) preopKmediansWeights = convert(Array{Float32, 1}, vec(readdlm(args["preop-model"][1:end-5]*"19mediansWeights.txt"))) preopKmedians = sparse(convert(Array{Float32, 2}, readdlm(args["preop-model"][1:end-5]*"20medians.txt"))) preopKmediansWeights = convert(Array{Float32, 1}, vec(readdlm(args["preop-model"][1:end-5]*"20mediansWeights.txt"))) # pre-load the training data we will sample from #realtimeX = open(deserialize, args["realtime-train-data"])' Loading Loading
index.jl +2 −2 Original line number Diff line number Diff line Loading @@ -84,8 +84,8 @@ dparse_utc(str::AbstractString) = ZonedDateTime(DateTime(str[1:end-1], dformat_r # load the K-medians we use to approximate the data realtimeKmedians = sparse(convert(Array{Float32, 2}, readdlm(args["realtime-model"][1:end-5]*"20medians.txt"))) realtimeKmediansWeights = convert(Array{Float32, 1}, vec(readdlm(args["realtime-model"][1:end-5]*"20mediansWeights.txt"))) preopKmedians = sparse(convert(Array{Float32, 2}, readdlm(args["preop-model"][1:end-5]*"19medians.txt"))) preopKmediansWeights = convert(Array{Float32, 1}, vec(readdlm(args["preop-model"][1:end-5]*"19mediansWeights.txt"))) preopKmedians = sparse(convert(Array{Float32, 2}, readdlm(args["preop-model"][1:end-5]*"20medians.txt"))) preopKmediansWeights = convert(Array{Float32, 1}, vec(readdlm(args["preop-model"][1:end-5]*"20mediansWeights.txt"))) # pre-load the training data we will sample from #realtimeX = open(deserialize, args["realtime-train-data"])' Loading