from pybrain.datasets import ClassificationDataSet

print "Reading data set..."

DS = ClassificationDataSet.loadFromFile('dataset.csv')

#Split validation set

TestDS, TrainDS = DS.splitWithProportion( 0.25 )

#train svm

from svm import svm_problem, svm_parameter, libsvm, gen_svm_nodearray

#define problem with data from the pybrain dataset.

# best python explanation for libsvm is here: https://github.com/arnaudsj/libsvm/tree/master/python

#we have to convert the data to ints and lists because of the low-level c interface

prob = svm_problem([int(t) for t in TrainDS['target']],[list(i) for i in TrainDS['input']])

param = svm_parameter()

# option: -t 0: linear kernel. Best for classification.

# option: -c 0.01: regularization parameter. smaller is more regularization

# see below for all options

param.parse_options('-t 0 -c 0.01')

print "Training svm..."

model = libsvm.svm_train(prob,param)

print "Testing svm with three random inputs"

from random import randrange

for j in range(3):

i = randrange(0,len(TestDS))

#again some conversion needed because of low level interface

x0,m_idx = gen_svm_nodearray(list(TestDS['input'][i]))

prediction = libsvm.svm_predict(model, x0)

print("Target:{0}, prediction:{1}".format(TestDS['target'][i],prediction))

#test svm over test dataset

correct = 0

for j in range(len(TestDS)):

#again some conversion needed because of low level interface

x0,m_idx = gen_svm_nodearray(list(TestDS['input'][j]))

prediction = libsvm.svm_predict(model, x0)

if int(prediction) == int(TestDS['target'][j]):

correct +=1

print "Accuracy on test set is {0}%".format(correct*100.0/len(TestDS))

###possible parameters###

# options:

# -s svm_type : set type of SVM (default 0)

# 0 -- C-SVC

# 1 -- nu-SVC

# 2 -- one-class SVM

# 3 -- epsilon-SVR

# 4 -- nu-SVR

# -t kernel_type : set type of kernel function (default 2)

# 0 -- linear: u'*v

# 1 -- polynomial: (gamma*u'*v + coef0)^degree

# 2 -- radial basis function: exp(-gamma*|u-v|^2)

# 3 -- sigmoid: tanh(gamma*u'*v + coef0)

# -d degree : set degree in kernel function (default 3)

# -g gamma : set gamma in kernel function (default 1/num_features)

# -r coef0 : set coef0 in kernel function (default 0)

# -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)

# -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)

# -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)

# -m cachesize : set cache memory size in MB (default 100)

# -e epsilon : set tolerance of termination criterion (default 0.001)

# -h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)

# -b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)

# -wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1)