def leave_one_out(y, x, param, n='DUMMY'):
results = []
for i, test in enumerate(zip(y, x)):
training_y = y[:i] + y[i+1:]
training_x = x[:i] + x[i+1:]
problem = svm.svm_problem(training_y, training_x)
model = svmutil.svm_train(problem, param, '-q')
result = svmutil.svm_predict(y[i:i+1], x[i:i+1], model, '-b 1')
results.append(result + (test[0], make_d.decode(x[i], make_d.decode_dic)))
return results
def leave_one_out(y, x, param, n="DUMMY"):
results = []
for i, test in enumerate(zip(y, x)):
training_y = y[:i] + y[i + 1 :]
training_x = x[:i] + x[i + 1 :]
problem = svm.svm_problem(training_y, training_x)
# t0 = time.clock()
model = svmutil.svm_train(problem, param, "-q")
# t1 = time.clock()
# print 'Training took', t1 - t0, 'seconds.'
result = svmutil.svm_predict(y[i : i + 1], x[i : i + 1], model, "-b 1")
results.append(result + (test[0], make_d.decode(x[i], make_d.decode_dic)))
return results
def leave_one_out(y, x, param=None, n=None):
results = []
for i, test in enumerate(zip(y, x)):
training_y = y[:i] + y[i+1:]
training_x = x[:i] + x[i+1:]
training_y = np.array(training_y)
training_x = np.array([np.array(tx) for tx in training_x])
learner = mil_rf.rf_learner()
learner = mil_multi.one_against_one(learner)
model = learner.train(training_x, training_y)
result = model.apply(np.array(x[i:i+1][0]))
results.append((result,) + (test[0],
make_d.decode(x[i], DECODE_DIC)))
return results
