def etrims_tree(n_hidden = [1000], coef = [1000.], size=6): print_time('tree2etrims test size is %d' % size) print_time('load_etrims') train_data, train_signal, test_data, test_signal = load_etrims(size=size) num_function = 100 print_time('train_DecisionTree num function is %d' % num_function) dt = DecisionTree(num_function=num_function) dt.fit(train_data, train_signal) print_time('test_DecisionTree') score = dt.score(test_data, test_signal) print_time('score is %f' % score) print_time('DecisionTree info') dt.info() elm_hidden = [(2*size+1)*(2*size+1)*2] print_time('train_ExtremeDecisionTree elm_hidden is %d, num function is %d' % (elm_hidden[0], num_function)) edt = ExtremeDecisionTree(elm_hidden=elm_hidden, elm_coef=None, num_function=num_function) edt.fit(train_data, train_signal) print_time('test_ExtremeDecisionTree') score = edt.score(test_data, test_signal) print_time('score is %f' % score) print_time('test_ExtremeDecisionTree') score = edt.score(test_data, test_signal) print_time('score is %f' % score) print_time('ExtremeDecisionTree info') edt.info() print_time('tree2etrims test is finished !')
def mnist_mlelm(n_hidden=[1000]): print "hidden:", n_hidden # initialize train_set, valid_set, test_set = load_mnist() train_data, train_target = train_set valid_data, valid_target = valid_set test_data, test_target = test_set # size train_size = 500 # max 50000 valid_size = 10 # max 10000 test_size = 10 # max 10000 train_data, train_target = train_data[:train_size], train_target[:train_size] valid_data, valid_target = valid_data[:valid_size], valid_target[:valid_size] test_data, test_target = test_data[:test_size], test_target[:test_size] # add valid_data/target to train_data/target """ train_data = train_data + valid_data train_target = train_target + valid_target """ # model dt = DecisionTree() #""" edt1 = ExtremeDecisionTree(elm_hidden=n_hidden) edt2 = ExtremeDecisionTree(elm_hidden=n_hidden, elm_coef=[1000., 100., 1000.]) #""" # fit #print "fitting ..." dt.fit(train_data, train_target) #""" edt1.fit(train_data, train_target) edt2.fit(train_data, train_target) #""" # test print "test score is ", score_dt = dt.score(test_data, test_target) #""" score_edt1 = edt1.score(test_data, test_target) score_edt2 = edt2.score(test_data, test_target) print score_dt, score_edt1, score_edt2 #""" #print score_dt print "dt" dt.info() #""" print "edt1" edt1.info() print "edt2" edt2.info()