# Cluster configuration file
# This file specifies the clusters used to make classifciation.
# each cluster is a separate entity that can be checked using its "name"

# nepheloid layer cluster
# This cluster keeps track of depth and hydroscat to classify data
# cluster2 and cluster3 are the ones that identifies the feature of interrest

# mfox {
#      name = "nepheloid";
#      descr = Hydroscat bb470 Hydroscat bb676 Hydroscat fl676_uncorr depthb470 product depthb676 product depthfl product bbfl product;

#      Hydroscat = "Hydroscat";
#      DepthSensorIFServer = "Depth";
#      depthb470 = "mult" DepthSensorIFServer depth Hydroscat bb470;
#      depthb676 = "mult" DepthSensorIFServer depth Hydroscat bb676;
#      depthfl = "mult" DepthSensorIFServer depth Hydroscat fl676_uncorr;
#      bbfl = "mult" Hydroscat bb470 Hydroscat fl676_uncorr;

#      avg = 0.00477979 0.00255224 0.000214371 0.197782 0.0945301 0.00173292 2.3384e-06;
#      sd = 0.00417154 0.00324263 0.00042562 0.23081 0.160994 0.00389195 9.19552e-06;

#      # This is the list of clusters :
#      #   numbering is starting from 0 (Note : bare in mind that in matlab it starting to 1 ... I will probably correct it later)
#      cluster0 = 0.441923      0.71101      -0.118371      0.220116      0.482289      0.00231484      -0.0640213;
#      cluster1 = 0.539069      0.520826      0.114313      0.229645      -0.0426228      -0.608551      0.0137975;      
#      # This the cluster I am looking for (?) : 
#      cluster2 = 0.00668774      0.300576      -0.23573      0.384407      0.803967      -0.212959      -0.120655;      
#      cluster3 = 0.0711429      0.0942018      -0.181911      0.762345      0.602217      -0.00787658      -0.0952715;      
#      cluster4 = 0.101707      -0.0977764      -0.218273      0.904086      0.311411      -0.0725903      -0.113292;      
#      cluster5 = -0.564604      -0.121079      -0.0643606      -0.667228      -0.394719      -0.160406      -0.188926;      
#      cluster6 = -0.201459      -0.3388      -0.274072      0.865111      0.0127309      0.0151081      -0.143895;      
#      cluster7 = 0.414562      -0.0298142      0.489343      -0.621031      -0.439456      -0.0501564      0.0804716;      
#      cluster8 = -0.108096      -0.330846      -0.317172      0.556749      -0.103832      -0.659767      -0.149046;      
#      cluster9 = -0.295736      -0.462931      -0.330731      0.693486      -0.220081      -0.175598      -0.169293;      
#      cluster10 = -0.368096      -0.655608      -0.325845      -0.147159      -0.469823      0.206574      -0.208436;      
#      cluster11 = 0.716768      0.335095      0.394431      -0.337725      -0.234708      0.0142539      0.221426;      
#      cluster12 = 0.509309      0.0483954      0.422705      -0.405322      -0.320968      0.534397      0.0818326;      
#      cluster13 = -0.288669      -0.559886      -0.356995      -0.240063      -0.417485      -0.466631      -0.161506;      
#      cluster14 = -0.290621      -0.49855      -0.15176      -0.601033      -0.42007      -0.287938      -0.152804;      
#      cluster15 = 0.485756      0.372875      0.595517      -0.141715      -0.0887816      0.250041      0.424122;      
#      cluster16 = -0.664265      -0.338693      -0.31406      -0.501722      -0.23419      -0.0228037      -0.195745;      
#      cluster17 = 0.312157      0.318872      0.493827      -0.161942      -0.0766118      0.702742      0.176273;      
#      cluster18 = -0.421152      0.129543      0.244637      -0.630469      -0.314264      0.4823      -0.130908;      
#      cluster19 = -0.610703      -0.342754      -0.351196      -0.448023      -0.227168      -0.320381      -0.176773;      
#      cluster20 = -0.388965      0.0453838      -0.290459      -0.616913      -0.353943      -0.468729      -0.191612;      
#      cluster21 = -0.33451      -0.33284      -0.159078      0.050372      -0.124134      0.844857      -0.142422;      
#      cluster22 = -0.334706      0.0166383      -0.295565      0.266338      0.829233      0.124234      -0.162293;      
#      cluster23 = -0.466679      -0.203898      -0.325435      -0.198515      0.129301      -0.746453      -0.146323;      
# }

# fake classifier
# This pseudo classifier just behave such as peope can do test connecting to cluster without dealing with external devices.

fake {
    name = "sim";
    freq = 10;
}