def train(self,A_pos_set, A_neg_set, online=False):
assert(online==False)
filename="%s-training"%(self.filename,)
labelfilename="%s-traininglabels"%(self.filename,)
svmformat.output_matrix_svmformat(numpy.vstack([A_pos_set,A_neg_set]) ,filename)
f=open(labelfilename,"w")
f.write("\n".join(["1",] * len(A_pos_set) + ["-1",] * len(A_neg_set)))
f.close()
os.system("svmlin %s %s "%(filename,labelfilename))
def test(self,data,log=True): ## return likelihood of samples
filename="%s-training.weights"%(self.filename,)
filename2="%s-testing"%(self.filename,)
svmformat.output_matrix_svmformat(data,filename2)
print "running test"
os.system("svmlin -f %s %s"%(filename, filename2) )
A=numpy.array(map(lambda x:float(x),open("%s.testing.outputs"%(self.filename,)).readlines()))
if log:
return numpy.log(A)
else:
return A