# -*- coding: utf-8 -*-
"""
Created on Tue Aug 28 15:50:42 2012

@author: eric
"""

from tools.load import LoadMatrix
lm = LoadMatrix()
fm_train_real = lm.load_numbers('../data/fm_train_real.dat')
fm_test_real = lm.load_numbers('/data/fm_test_real.dat')
label_train_multiclass = lm.load_labels('../data/label_train_multiclass.dat')

parameter_list=[
        [ fm_train_real, fm_test_real, label_train_multiclass, 1.2, 1.2, 1e-5, 1, 0.001, 1.5],
        [ fm_train_real, fm_test_real, label_train_multiclass, 5, 1.2, 1e-2, 1, 0.001, 2]]

def mkl_multiclass_modular(fm_train_real, fm_test_real, label_train_multiclass,
    width, C, epsilon, num_threads, mkl_epsilon, mkl_norm):

    from shogun.Features import CombinedFeatures, RealFeatures, MulticlassLabels
    from shogun.Kernel import CombinedKernel, GaussianKernel, LinearKernel,PolyKernel
    from shogun.Classifier import MKLMulticlass

    kernel = CombinedKernel()
    feats_train = CombinedFeatures()
    feats_test = CombinedFeatures()

    # 1
    subkfeats_train = RealFeatures(fm_train_real)
    subkfeats_test = RealFeatures(fm_test_real)
    subkernel = LinearKernel()
    feats_train.append_feature_obj(subkfeats_train)
    feats_test.append_feature_obj(subkfeats_test)
    kernel.append_kernel(subkernel)

    # 2
    subkfeats_train = RealFeatures(fm_train_real)
    subkfeats_test = RealFeatures(fm_test_real)
    subkernel = GaussianKernel(10, width)
    feats_train.append_feature_obj(subkfeats_train)
    feats_test.append_feature_obj(subkfeats_test)
    kernel.append_kernel(subkernel)

    # 3
    subkfeats_train = RealFeatures(fm_train_real)
    subkfeats_test = RealFeatures(fm_test_real)
    subkernel = PolyKernel(10,2)
    feats_train.append_feature_obj(subkfeats_train)
    feats_test.append_feature_obj(subkfeats_test)
    kernel.append_kernel(subkernel)


    kernel.init(feats_train, feats_train)
    labels = MulticlassLabels(label_train_multiclass)
    mkl = MKLMulticlass(C, kernel, labels)
    mkl.set_epsilon(epsilon);
    mkl.parallel.set_num_threads(num_threads)
    mkl.set_mkl_epsilon(mkl_epsilon)
    mkl.set_mkl_norm(mkl_norm)

    mkl.train()
    print kernel.get_subkernel_weights()

    kernel.init(feats_train, feats_test)
    out =  mkl.apply().get_labels()

    return out

if __name__ == '__main__':
    print('mkl_multiclass')
    mkl_multiclass_modular(*parameter_list[0])