<NME> cbm.properties
<BEF> ADDFILE
<MSG> add cbm properties

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+# path to input train data
+input.trainData=/scratch/wang.bin/trec.data/scene/data_sets/train
+
+# path to input test data
+input.testData=/scratch/wang.bin/trec.data/scene/data_sets/test
+
+# matrix format; can be dense, sparse_random or sparse_sequential
+# use sparse format for text data with large number of sparse features to save memory
+# sparse_sequential should be avoided when boosting is used; use sparse_random for boosting
+input.matrixType=dense
+
+# folder for the program output
+output=/scratch/li.che/projects/pyramid/archives/exp211/scene/101
+
+# number of clusters in the CBM
+mixture.numClusters=20
+
+# type of the multi-class classifier in CBM; supported type: lr (for Logistic Regression) or boost (for Gradient Boosting)
+mixture.multiClassClassifierType=lr
+
+# type of the binary classifier in CBM; supported type: lr or boost
+mixture.binaryClassifierType=lr
+
+# whether to start training CBM from an existing model, if such model exists in the output folder
+# true, or false, or auto
+# false = alway start from scratch
+# true = first load back an existing model, and then continue training
+# auto = detect whether there is an existing model in the output folder, if yes, load it back and keep training; if not, start from scratch
+train.warmStart=false
+
+# whether to initialize CBM with BM; if true, 10 BM models with be trained and the best one will be kept as the initialization of CBM
+train.initialize=true
+
+# whether to allow empty subset to be predicted; 
+# true = allow empty prediction
+# false = do not allow empty prediction
+# auto = allow empty prediction only if the training set contains empty label sets
+predict.allowEmpty=auto
+
+########## EM ###########
+em.numIterations=10
+
+####### logistic regression #####
+# Gaussian prior variance for LR weights. Small values indicate strong regularizations
+lr.multiClassVariance=1.0
+lr.binaryVariance=1.0
+
+############# boosting #########
+# how many boosting iterations to run (i.e., number of trees to add) in each M step of the EM loop
+boost.numIterationsBinary=20
+boost.numIterationsMultiClass=20
+# number of leaves in the regression tree
+boost.numLeavesBinary=5
+boost.numLeavesMultiClass=5
+# shrinkage of boosting
+boost.shrinkageBinary=0.1
+boost.shrinkageMultiClass=0.1
+
+

add cbm properties