if (BatchSize == 1):
BP_F = 1
else:
BMBP_F = 1
ELM_F = 0
ELMT_F = 0
LDA_F = 0
LDAT_F = 0
if (BP_F == 1):
# Number of epochs, step and mommentum
momentum = 0
trainingAlg = paC.Training_Alg("BP", [N_epochs, Learning_Rate, momentum])
if (BMBP_F == 1):
# Number of epochs, step and number of partitions
trainingAlg = paC.Training_Alg("BMBP",
[N_epochs, Learning_Rate, BatchSize, 1])
if (ELM_F == 1):
trainingAlg = paC.Training_Alg("ELM", ["bias"])
if (ELMT_F == 1):
trainingAlg = paC.Training_Alg("ELMT", [500, 0.0005, 20, "bias"])
if (LDA_F == 1):
trainingAlg = paC.Training_Alg("LDA", [])
initDistrib = paC.Init_Distrib("default",
["uniform", -1, 1]) # Define initialization
#initDistrib = paC.Init_Distrib("default", ["normal",0,1])# Define initialization
mySLFN.set_initDistrib(initDistrib) # Set the initialization
mySLFN.init_Weights() # Initialize
BP_F = 0
ELM_F = 1
BMBP_F = 0
FT_F = 0
# DEFINE TRAINING ALGORITHM
if (BP_F == 1):
# Step and number of epochs
trainingAlg = paC.Training_Alg("BP", [1000, 0.0003])
if (BMBP_F == 1):
# Step and number of epochs
trainingAlg = paC.Training_Alg("BMBP", [100, 0.0005])
if (ELM_F == 1):
trainingAlg = paC.Training_Alg("ELM", ["bias"])
if (FT_F == 1):
trainingAlg = paC.Training_Alg("FT", [100, 0.0005, "bias", "normal"])
mySLFN.set_trainigAlg(trainingAlg) # Set the trainig algorithm
#################################################################
########################## Boosting #########################
regularization = paC.Regularization("NoL2", [0.000005])
######## TRAINING ALGORITHM ########
BP_F = 1
BMBP_F = 0
ELM_F = 0
ELMT_F = 0
LDA_F = 0
LDAT_F = 0
if (BP_F == 1):
# Number of epochs, step and mommentum
trainingAlg = paC.Training_Alg("BP", [50, 0.01, 0.0])
if (BMBP_F == 1):
# Number of epochs, step and number of partitions
trainingAlg = paC.Training_Alg("BMBP", [200, 0.01, 1])
if (ELM_F == 1):
trainingAlg = paC.Training_Alg("ELM", ["no_bias"])
if (ELMT_F == 1):
trainingAlg = paC.Training_Alg("ELMT", [100, 0.01, 20, "bias"])
if (LDA_F == 1):
trainingAlg = paC.Training_Alg("LDA", [])
if (LDAT_F == 1):
regularization = paC.Regularization("L2", [0.000005])
######## TRAINING ALGORITHM ########
BP_F = 0
BMBP_F = 1
ELM_F = 0
ELMT_F = 0
LDA_F = 0
LDAT_F = 0
if (BP_F == 1):
# Number of epochs, step and mommentum
trainingAlg = paC.Training_Alg("BP", [500, 0.0003, 0.2])
if (BMBP_F == 1):
# Number of epochs, step and number of partitions
trainingAlg = paC.Training_Alg("BMBP", [200, 0.001, 10, 3])
if (ELM_F == 1):
trainingAlg = paC.Training_Alg("ELM", ["bias"])
if (ELMT_F == 1):
trainingAlg = paC.Training_Alg("ELMT", [10, 0.0005, 10, "bias"])
if (LDA_F == 1):
trainingAlg = paC.Training_Alg("LDA", [])
if (LDAT_F == 1):
# CENTRE INITIALIZATION
initCenters = paC.Init_Centers("randomSamples") # Define initialization of centres
#initCenters = paC.Init_Centers("Kmeans", [300,10,"nosplit"]) # Define initialization of centres
myRBF.set_initCenters (initCenters) # Set the initialization
# Set the initialization
BP_F = 0
ELM_F = 1
BMBP_F = 0
FT_F = 0
# DEFINE TRAINING ALGORITHM
if (BP_F == 1):
# Step and number of epochs
trainingAlg = paC.Training_Alg("BP",[300, 0.0003 ])
if (BMBP_F == 1):
# Step and number of epochs
trainingAlg = paC.Training_Alg("BMBP",[1500, 0.00008])
if (ELM_F == 1):
trainingAlg = paC.Training_Alg("ELM",["bias"])
if (FT_F == 1):
trainingAlg = paC.Training_Alg("FT",[7000, 0.00015, "bias","normal"])
# nHs = range (10, 200,5)
# mySLFN.ELM_validate(nHs, n_iter = 10)
myRBF.set_trainigAlg(trainingAlg) # Set the trainig algorithm
