Python LibLinear.set_liblinear_solver_typeの例

コード例 #1

ファイル: classifier_liblinear_modular.py プロジェクト: manantomar/test

def classifier_liblinear_modular (train_fname, test_fname,
		label_fname, C, epsilon):

	from modshogun import RealFeatures, SparseRealFeatures, BinaryLabels
	from modshogun import LibLinear, L2R_L2LOSS_SVC_DUAL
	from modshogun import Math_init_random, CSVFile
	Math_init_random(17)

	feats_train=RealFeatures(CSVFile(train_fname))
	feats_test=RealFeatures(CSVFile(test_fname))
	labels=BinaryLabels(CSVFile(label_fname))

	svm=LibLinear(C, feats_train, labels)
	svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL)
	svm.set_epsilon(epsilon)
	svm.set_bias_enabled(True)
	svm.train()

	predictions = svm.apply(feats_test)
	return predictions, svm, predictions.get_labels()

コード例 #2

ファイル: example-binary-svm-find-best-c.py プロジェクト: haeusler/shogun-quickstart

from modshogun import RealFeatures, BinaryLabels
from modshogun import LibLinear, L2R_L2LOSS_SVC_DUAL

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"]