def testSVM():
svm = SVM()
for i in iris_training:
svm.addTraining(labels[i], iris_data[i, :])
svm.train(verbose=1)
success = 0.0
total = 0.0
for i in iris_testing:
c = svm.predict(iris_data[i, :])
#print c, labels[i]
if c == labels[i]:
success += 1
total += 1
print "SVM Rate:", success / total
def train(self, C=None, Gamma=None, ilog=None, callback=None):
# Create the SVM
self.svm = SVM(svm_type=TYPE_C_SVC)
# Add training data
for sub_id, tiles in self.training_data.iteritems():
for tile in tiles:
self.svm.addTraining(sub_id, tile)
# Train the SVM
if C != None and Gamma != None:
self.svm.train(C_range=C,
G_range=Gamma,
verbose=True,
callback=callback)
else:
#Automatic
self.svm.train(verbose=True, callback=callback)
table = pv.Table()
values = {0: [], 1: []}
correct = 0
total = 0
for each in testing:
label = clsfy.predict(each[1], ilog=ilog)
total += 1
if label == each[0]:
correct += 1
rate = float(correct) / total
if ilog: ilog.table(table)
return rate
if __name__ == "__main__":
from pyvision.vector.SVM import SVM
svm = SVM(kernel='LINEAR', random_seed=30)
ilog = pv.ImageLog()
print "SVM rate:", genderClassifier(svm, ilog=None)
svm = SVM(kernel='RBF', random_seed=30)
ilog = pv.ImageLog()
print "SVM rate:", genderClassifier(svm, ilog=None)
ilog.show()
def runSVM(self):
svm = SVM()
print "I am in the SVM module now"
