def __init__(self,Gs,ls):
self._K = ls.shape[1]
self._Gs = Gs
self._N = Gs.shape[1]
self._ls = ls
self._svm = LibSvm('c_svc','rbf',\
gamma=1.0/self._N,C=100,probability=True)
def BuildModel(self, data, labels):
# Create and train the classifier.
svm = LibSvm(kernel_type=self.kernel,
C=self.C,
gamma=self.gamma)
svm.learn(data, labels)
return svm
def bench_mlpy(X, y, T, valid):
#
# .. MLPy ..
#
from mlpy import LibSvm
start = datetime.now()
clf = LibSvm(kernel_type='rbf', C=1., gamma=1. / sigma)
clf.learn(X, y.astype(np.float64))
score = np.mean(clf.pred(T) == valid)
return score, datetime.now() - start
def __init__(self, config):
self.config = config
self.commands = {}
self.command_names = {}
commands_folder = self.config['commands_folder']
for index, command_name in enumerate(os.listdir(commands_folder)):
cmd = Command(command_name, index,
os.path.join(commands_folder, command_name))
self.commands[command_name] = cmd
self.command_names[index] = command_name
self.svm = LibSvm(svm_type='c_svc', kernel_type='linear')
x, y = [], []
for command in self.commands.itervalues():
for feature in command.objects:
x.append(feature)
y.append(command.index)
self.svm.learn(x, y)
def metric(self):
totalTimer = Timer()
with totalTimer:
model = LibSvm(**self.build_opts)
model.learn(self.data_split[0], self.data_split[1])
if len(self.data) >= 2:
predictions = model.pred(self.data[1])
metric = {}
metric["runtime"] = totalTimer.ElapsedTime()
if len(self.data) == 3:
confusionMatrix = Metrics.ConfusionMatrix(self.data[2],
predictions)
metric['ACC'] = Metrics.AverageAccuracy(confusionMatrix)
metric['MCC'] = Metrics.MCCMultiClass(confusionMatrix)
metric['Precision'] = Metrics.AvgPrecision(confusionMatrix)
metric['Recall'] = Metrics.AvgRecall(confusionMatrix)
metric['MSE'] = Metrics.SimpleMeanSquaredError(
self.data[2], predictions)
return metric
