def getOptCG(labels, values):
# Format data for subsequent methods
# Set up cross-validation settings
param = Param()
param.cset = range(-5, 15, 2)
param.gset = range(3, -15, -2)
param.nfold = 10
prob = svm_problem(labels, values)
rVal = [[0 for col in range(len(param.cset))] for row in range(len(param.gset))];
# Cross-validation to get optimal parameters
for i in range(len(param.gset)):
param.g = 2 ** param.gset[i]
for j in range(len(param.cset)):
param.c = 2 ** param.cset[j]
testParam = svm_parameter(param.libsvm)
# Train on learning data with x-validation and store result
rVal[i][j] = svm_train(prob, param.libsvm + " -v " + str(param.nfold))
# Select the parameters with highest accuracy
min_val, loc = getMax(rVal)
g = 2 ** param.gset[loc[0]]
c = 2 ** param.cset[loc[1]]
return c, g
def svmtrain(labels, values=None, c=None, g=None):
# If Dictionary
if isinstance(labels, dict):
values = [j for i in labels.itervalues() for j in i.itervalues()]
labels = [i + 1 for i in range(len(labels.values())) for j in range(len(labels[labels.keys()[i]]))]
if values != None:
optParam = Param()
optParam.c = c
optParam.g = g
if c == None or g == None:
# Retrieve optimal c and g
optParam.c, optParam.g = getOptCG(labels, values)
# Train model with optimal c and g
prob = svm_problem(labels, values)
m = svm_train(prob, optParam.libsvm)
# Return model
return m
else:
raise TypeError("Values not provided for the arguments")