from numpy import random, mean
X_train = RealFeatures(random.randn(30, 100))
Y_train = BinaryLabels(random.randn(X_train.get_num_vectors()))
results = []
for C1_pow in range(-3, 1):
for C2_pow in range(-3, 1):
svm = LibLinear()
svm.set_bias_enabled(False)
svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL)
svm.set_C(10**C1_pow, 10**C2_pow)
svm.set_features(X_train)
svm.set_labels(Y_train)
svm.train()
Y_pred = svm.apply_binary(X_train)
accuracy = mean(Y_train.get_labels() == Y_pred.get_labels())
print 10**C1_pow, 10**C2_pow, accuracy
results.append({"accuracy":accuracy, "svm":svm})
results.sort(key=lambda x:x["accuracy"], reverse=True)
best_svm = results[0]["svm"]
print "best machine weights:", best_svm.get_w()
print " argmin: ", best_svm.get_w().argmin()
print " argmax: ", best_svm.get_w().argmax()
