#Input parameters for MLZ (Don't delete them)
#Space between ":" and "value" is important
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# BLANK SPACE
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# FILENAMES V
TrainFile : SDSS_MGS.train # Name of training file
TestFile : SDSS_MGS.test # Name of test file
FinalFileName : SDSS_MGS # Root of Final name
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# PATHS
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Path_Train : test/ # Path to train file
Path_Test : test/ # Path to test file
Path_Output : test/ # Path to results
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# COLUMNS AND ATTRIBUTES
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#in columns names the column for error must be named same a the column with an e in front
Columns : zs,u,g,r,i,z,u-g,g-r,r-i,i-z,eu,eg,er,ei,ez,eu-g,eg-r,er-i,ei-z # Columns of training file
Att : u,g,r,i,z,u-g,g-r,r-i,i-z # Attributes to use, try a subset!
Columns_Test : zs,u,g,r,i,z,u-g,g-r,r-i,i-z,eu,eg,er,ei,ez,eu-g,eg-r,er-i,ei-z # Columns for test, no need have a KetAtt column
KeyAtt : zs #name of the column to be predicted
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# CODE STEPS
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CheckOnly : no # Check everything with small samples to test the codes
PredictionMode : TPZ # TPZ/SOM/TPZ_C
PredictionClass : Reg # Reg/Class
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# REDSHIFT RELATED (OR FOR CLASSIFICATION)
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MinZ : 0.001 # Min redshift (class label)
MaxZ : 0.3 # Max redshift (class label)
NzBins : 80 # Number of bins in z range (2 for Classification), Final output is double this value
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# COMMON FOR TPZ/SOM
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NRandom : 2 # Number of random realizations (1 original data)
NTrees : 4 # Number of trees or maps! (total number is NRandom x NTrees)
Natt : 3 # Number of attributes m* for TPZ or subsample for SOM
OobError : no # Out-of-bag error?
VarImportance : no # Perform variable importance ranking?
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# TPZ
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MinLeaf : 5 # Minimum number for terminal leaf
ImpurityIndex : entropy # options (entropy/gini/classE) for Classification
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# SOM
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Topology : hex # grid,hex,sphere
Periodic : yes # periodic? yes/no
Ntop : 15 # map size (in the case fo sphere, npix=12*ntop*ntop , ntop: must be power of 2)
Iterations : 200 # number of iterations
SomType : online # online/batch
AlphaStart : 0.9 # Alpha start
AlphaEnd : 0.5 # Alpha end
ImportanceFile : none # path to importance file, write 'none' is not used
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# Get PDFs
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SigmaFactor : 3. # Gaussian smoothing with kernel Sigma1*Resolution
RmsFactor : 0.02 # initial rms, can be modified using oob data
WriteFits : no # yes, PDF file in fits format, no: numpy format
MultipleFiles : no # Write a PDFs file per core instead of merging them (faster)
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# SPARSE REPRESENTATION
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SparseRep : no # Sparse representation, it creates a fits file
SparseDims : 200,50,2 # Nmu, Nsigma, Nv (To create dictionary)
NumberCoef : 32001 # Integers to represent the coefficients
NumberBases : 20 # Number of fixed bases to use
OriginalPdfFile : yes # Write the original PDFs also?
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