#!/usr/bin/env python2.7 # # This software is distributed under BSD 3-clause license (see LICENSE file). # # Copyright (C) 2014 Thoralf Klein # from modshogun import RealFeatures, BinaryLabels, LibLinear from numpy import random, mean X_train = RealFeatures(random.randn(30, 100)) Y_train = BinaryLabels(random.randn(X_train.get_num_vectors())) svm = LibLinear(1.0, X_train, Y_train) svm.train() Y_pred = svm.apply_binary(X_train) Y_train.get_labels() == Y_pred.get_labels() print "accuracy:", mean(Y_train.get_labels() == Y_pred.get_labels())
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()