def get(self): data = super(System, self).get() data['cpuInfoDto'] = random_data( data['cpuInfoDto'], 'capacity', 'usedCapacity', 'freeCapacity', 600, 1600) data['memInfoDto'] = random_data( data['memInfoDto'], 'totalMemory', 'usedMemory', 'freeMemory', 3500, 4700) return data
def test_est_propensity(): D = np.array([0, 0, 0, 1, 1, 1]) X = np.array([[7, 8], [3, 10], [7, 10], [4, 7], [5, 10], [9, 8]]) Y = random_data(D_cur=D, X_cur=X) causal = c.CausalModel(Y, D, X) causal.est_propensity() lin = [0, 1] qua = [] coef = np.array([6.8066090, -0.0244874, -0.7524939]) loglike = -3.626517 fitted = np.array([0.6491366, 0.3117840, 0.2911631, 0.8086407, 0.3013733, 0.6379023]) se = np.array([8.5373779, 0.4595191, 0.8106499]) keys = {'lin', 'qua', 'coef', 'loglike', 'fitted', 'se'} assert_equal(causal.propensity['lin'], lin) assert_equal(causal.propensity['qua'], qua) assert np.allclose(causal.propensity['coef'], coef) assert np.allclose(causal.propensity['loglike'], loglike) assert np.allclose(causal.propensity['fitted'], fitted) assert np.allclose(causal.propensity['se'], se) assert_equal(set(causal.propensity.keys()), keys) assert np.allclose(causal.raw_data['pscore'], fitted)
def test_select_qua_terms(): Y, D, X = random_data() X_c_random, X_t_random = X[D == 0], X[D == 1] lin1 = [0, 1] C1 = np.inf ans1 = [] assert_equal(p.select_qua_terms(X_c_random, X_t_random, lin1, C1), ans1) lin2 = [1, 0] C2 = 0 ans2 = [(1, 1), (1, 0), (0, 0)] assert_equal(p.select_qua_terms(X_c_random, X_t_random, lin2, C2), ans2) lin3 = [0] C3 = -983.340 ans3 = [(0, 0)] assert_equal(p.select_qua_terms(X_c_random, X_t_random, lin3, C3), ans3) lin4 = [] C4 = 34.234 ans4 = [] assert_equal(p.select_qua_terms(X_c_random, X_t_random, lin4, C4), ans4) X_c = np.array([[7, 8], [3, 10], [7, 10]]) X_t = np.array([[4, 7], [5, 10], [9, 8]]) lin5 = [0, 1] C5 = 1.1 ans5 = [(1, 1), (0, 1), (0, 0)] assert_equal(p.select_qua_terms(X_c, X_t, lin5, C5), ans5)
def test_select_lin(): Y, D, X = random_data(K=4) X_c_random, X_t_random = X[D == 0], X[D == 1] lin1 = [0, 1, 2, 3] C1 = np.random.rand(1) ans1 = [0, 1, 2, 3] assert_equal(p.select_lin(X_c_random, X_t_random, lin1, C1), ans1) X_c = np.array([[1, 2], [9, 7]]) X_t = np.array([[1, 4], [9, 6]]) lin2 = [] C2 = 0.07 ans2 = [] assert_equal(p.select_lin(X_c, X_t, lin2, C2), ans2) lin3 = [] C3 = 0.06 ans3 = [1, 0] assert_equal(p.select_lin(X_c, X_t, lin3, C3), ans3) lin4 = [1] C4 = 0.35 ans4 = [1] assert_equal(p.select_lin(X_c, X_t, lin4, C4), ans4) lin5 = [1] C5 = 0.34 ans5 = [1, 0] assert_equal(p.select_lin(X_c, X_t, lin5, C5), ans5)
def test_select_lin_terms(): Y, D, X = random_data(K=4) X_c_random, X_t_random = X[D == 0], X[D == 1] lin1 = [3, 0, 1] C1 = np.inf ans1 = [3, 0, 1] assert_equal(p.select_lin_terms(X_c_random, X_t_random, lin1, C1), ans1) lin2 = [2] C2 = 0 ans2 = [2, 0, 1, 3] assert_equal(p.select_lin_terms(X_c_random, X_t_random, lin2, C2), ans2) lin3 = [] C3 = 0 ans3 = [0, 1, 2, 3] assert_equal(p.select_lin_terms(X_c_random, X_t_random, lin3, C3), ans3) lin4 = [3, 1] C4 = -34.234 ans4 = [3, 1, 0, 2] assert_equal(p.select_lin_terms(X_c_random, X_t_random, lin4, C4), ans4) X_c = np.array([[1, 2], [9, 7]]) X_t = np.array([[1, 4], [9, 7]]) lin5 = [] C5 = 0.06 ans5 = [1, 0] assert_equal(p.select_lin_terms(X_c, X_t, lin5, C5), ans5)
def test_est_propensity(): D = np.array([0, 0, 0, 1, 1, 1]) X = np.array([[7, 8], [3, 10], [7, 10], [4, 7], [5, 10], [9, 8]]) Y = random_data(D_cur=D, X_cur=X) causal = c.CausalModel(Y, D, X) causal.est_propensity(feature_names=[], exclude=[]) lin = [0, 1] qua = [] coef = np.array([6.8066090, -0.0244874, -0.7524939]) loglike = -3.626517 fitted = np.array( [0.6491366, 0.3117840, 0.2911631, 0.8086407, 0.3013733, 0.6379023]) se = np.array([8.5373779, 0.4595191, 0.8106499]) keys = {'lin', 'qua', 'coef', 'loglike', 'fitted', 'se'} assert_equal(causal.propensity['lin'], lin) assert_equal(causal.propensity['qua'], qua) assert np.allclose(causal.propensity['coef'], coef) assert np.allclose(causal.propensity['loglike'], loglike) assert np.allclose(causal.propensity['fitted'], fitted) assert np.allclose(causal.propensity['se'], se) assert_equal(set(causal.propensity.keys()), keys) assert np.allclose(causal.raw_data['pscore'], fitted)
def test_select_lin_terms(): Y, D, X = random_data(K=4) X_c_random, X_t_random = X[D==0], X[D==1] lin1 = [3, 0, 1] C1 = np.inf ans1 = [3, 0, 1] assert_equal(p.select_lin_terms(X_c_random, X_t_random, lin1, C1), ans1) lin2 = [2] C2 = 0 ans2 = [2, 0, 1, 3] assert_equal(p.select_lin_terms(X_c_random, X_t_random, lin2, C2), ans2) lin3 = [] C3 = 0 ans3 = [0, 1, 2, 3] assert_equal(p.select_lin_terms(X_c_random, X_t_random, lin3, C3), ans3) lin4 = [3, 1] C4 = -34.234 ans4 = [3, 1, 0, 2] assert_equal(p.select_lin_terms(X_c_random, X_t_random, lin4, C4), ans4) X_c = np.array([[1, 2], [9, 7]]) X_t = np.array([[1, 4], [9, 7]]) lin5 = [] C5 = 0.06 ans5 = [1, 0] assert_equal(p.select_lin_terms(X_c, X_t, lin5, C5), ans5)
def test_select_lin(): Y, D, X = random_data(K=4) X_c_random, X_t_random = X[D==0], X[D==1] lin1 = [0, 1, 2, 3] C1 = np.random.rand(1) ans1 = [0, 1, 2, 3] assert_equal(p.select_lin(X_c_random, X_t_random, lin1, C1), ans1) X_c = np.array([[1, 2], [9, 7]]) X_t = np.array([[1, 4], [9, 6]]) lin2 = [] C2 = 0.07 ans2 = [] assert_equal(p.select_lin(X_c, X_t, lin2, C2), ans2) lin3 = [] C3 = 0.06 ans3 = [1, 0] assert_equal(p.select_lin(X_c, X_t, lin3, C3), ans3) lin4 = [1] C4 = 0.35 ans4 = [1] assert_equal(p.select_lin(X_c, X_t, lin4, C4), ans4) lin5 = [1] C5 = 0.34 ans5 = [1, 0] assert_equal(p.select_lin(X_c, X_t, lin5, C5), ans5)
def test_select_qua_terms(): Y, D, X = random_data() X_c_random, X_t_random = X[D==0], X[D==1] lin1 = [0, 1] C1 = np.inf ans1 = [] assert_equal(p.select_qua_terms(X_c_random, X_t_random, lin1, C1), ans1) lin2 = [1, 0] C2 = 0 ans2 = [(1, 1), (1, 0), (0, 0)] assert_equal(p.select_qua_terms(X_c_random, X_t_random, lin2, C2), ans2) lin3 = [0] C3 = -983.340 ans3 = [(0, 0)] assert_equal(p.select_qua_terms(X_c_random, X_t_random, lin3, C3), ans3) lin4 = [] C4 = 34.234 ans4 = [] assert_equal(p.select_qua_terms(X_c_random, X_t_random, lin4, C4), ans4) X_c = np.array([[7, 8], [3, 10], [7, 10]]) X_t = np.array([[4, 7], [5, 10], [9, 8]]) lin5 = [0, 1] C5 = 1.1 ans5 = [(1, 1), (0, 1), (0, 0)] assert_equal(p.select_qua_terms(X_c, X_t, lin5, C5), ans5)
def __init__(self, timesheet=None, date=date.today(), data=None, div_size=DIV_SIZE, total_time=TOTAL_TIME, start_time=0): """ Inputs ------ div_size: The size of the time divisions of a particular day in minutes. A div_size that doesnt break down the day into whole number minute divisions is rounded to the nearest div_size that accomplishes this data: The on/off light data that will be given by the microcontroller, 'random' for randome data, 'ones' for ones, deafult is zeros date: the date of the data """ self.date = date self.div_size = u.get_whole_div(div_size, FACTORS) self.start_time = start_time self.total_time = total_time self.end_time = self.start_time + self.total_time - 5 if isinstance(timesheet, pd.DataFrame): self.timesheet = timesheet else: if data is 'random': data = u.random_data(self.div_size) if data is 'ones': data = np.full(total_time // self.div_size, 1) self.timesheet = u.construct_dataframe( data, self.div_size, self.date.strftime('%Y-%m-%d'), self.total_time, self.start_time)
def main(args): dNet = net.DigitRecNet() optimizer = optim.SGD(dNet.parameters(), lr=args.lr, momentum=0.5) criterion = torch.nn.NLLLoss() if not args.train: logging.info('-' * 50) logging.info('Start testing ... ') load_model(dNet, args.model_file, 'BestModel') logging.info('finish load model: %s' % args.model_file) test_x = utils.load_test_data(args.test_file, args.N, args.M) logging.info('Load test : %d' % len(test_x)) test_input_x = Variable(torch.FloatTensor(test_x)) test_input_x = test_input_x.resize(test_input_x.size()[0], 1, args.N, args.M) only_test(dNet, test_input_x, args.result_file) return train_x, train_y = utils.load_data(args.train_file, args.N, args.M) dev_x, dev_y = utils.load_data(args.dev_file, args.N, args.M) logging.info('-' * 50) logging.info('Load train : %d, Load dev : %d' % (len(train_x), len(dev_x))) #train logging.info('-' * 50) logging.info('Start training ... ') dev_input_x = Variable(torch.FloatTensor(dev_x)) dev_input_x = dev_input_x.resize(dev_input_x.size()[0], 1, args.N, args.M) dev_pred_y = Variable(torch.LongTensor(dev_y)) best_accuracy = 0 for epoch_id in range(args.epoch): logging.info('Epoch : %d' % epoch_id) data = utils.random_data((train_x, train_y), args.batch_size) for it, (input_x, pred_y) in enumerate(data): input_x = Variable(torch.FloatTensor(input_x)) input_x = input_x.resize(input_x.size()[0], 1, args.N, args.M) pred_y = Variable(torch.LongTensor(pred_y)) assert input_x.size()[0] == pred_y.size()[0] optimizer.zero_grad() output_x = dNet(input_x) loss = criterion(output_x, pred_y) loss.backward() optimizer.step() logging.info('Iteration (%d) loss : %.6f' % (it, loss)) if (it % args.iter_cnt == 0): tmp_accuracy = test(dNet, dev_input_x, dev_pred_y) if tmp_accuracy > best_accuracy: best_accuracy = tmp_accuracy save_model(dNet, epoch_id, args.model_file, 'Best') logging.info( "Epoch : %d, Accuarcy : %.2f%%, Best Accuatcy : %.2f%%" % (epoch_id, tmp_accuracy, best_accuracy))
def test_select_qua(): Y, D, X = random_data() X_c_random, X_t_random = X[D==0], X[D==1] lin1 = [1, 0] qua1 = [(1, 0), (0, 0), (1, 1)] C1 = np.random.rand(1) ans1 = [(1, 0), (0, 0), (1, 1)] assert_equal(p.select_qua(X_c_random, X_t_random, lin1, qua1, C1), ans1) lin2 = [1] qua2 = [(1, 1)] C2 = np.random.rand(1) ans2 = [(1, 1)] assert_equal(p.select_qua(X_c_random, X_t_random, lin2, qua2, C2), ans2) X_c = np.array([[7, 8], [3, 10], [7, 10]]) X_t = np.array([[4, 7], [5, 10], [9, 8]]) lin3 = [0, 1] qua3 = [] C3 = 1.2 ans3 = [] assert_equal(p.select_qua(X_c, X_t, lin3, qua3, C3), ans3) lin4 = [0, 1] qua4 = [] C4 = 1.1 ans4 = [(1, 1), (0, 1), (0, 0)] assert_equal(p.select_qua(X_c, X_t, lin4, qua4, C4), ans4) lin5 = [0, 1] qua5 = [(1, 1)] C5 = 2.4 ans5 = [(1, 1)] assert_equal(p.select_qua(X_c, X_t, lin5, qua5, C5), ans5) lin6 = [0, 1] qua6 = [(1, 1)] C6 = 2.3 ans6 = [(1, 1), (0, 1), (0, 0)] assert_equal(p.select_qua(X_c, X_t, lin6, qua6, C6), ans6) lin7 = [0, 1] qua7 = [(1, 1), (0, 1)] C7 = 3.9 ans7 = [(1, 1), (0, 1)] assert_equal(p.select_qua(X_c, X_t, lin7, qua7, C7), ans7) lin8 = [0, 1] qua8 = [(1, 1), (0, 1)] C8 = 3.8 ans8 = [(1, 1), (0, 1), (0, 0)] assert_equal(p.select_qua(X_c, X_t, lin8, qua8, C8), ans8)
def test_select_qua(): Y, D, X = random_data() X_c_random, X_t_random = X[D == 0], X[D == 1] lin1 = [1, 0] qua1 = [(1, 0), (0, 0), (1, 1)] C1 = np.random.rand(1) ans1 = [(1, 0), (0, 0), (1, 1)] assert_equal(p.select_qua(X_c_random, X_t_random, lin1, qua1, C1), ans1) lin2 = [1] qua2 = [(1, 1)] C2 = np.random.rand(1) ans2 = [(1, 1)] assert_equal(p.select_qua(X_c_random, X_t_random, lin2, qua2, C2), ans2) X_c = np.array([[7, 8], [3, 10], [7, 10]]) X_t = np.array([[4, 7], [5, 10], [9, 8]]) lin3 = [0, 1] qua3 = [] C3 = 1.2 ans3 = [] assert_equal(p.select_qua(X_c, X_t, lin3, qua3, C3), ans3) lin4 = [0, 1] qua4 = [] C4 = 1.1 ans4 = [(1, 1), (0, 1), (0, 0)] assert_equal(p.select_qua(X_c, X_t, lin4, qua4, C4), ans4) lin5 = [0, 1] qua5 = [(1, 1)] C5 = 2.4 ans5 = [(1, 1)] assert_equal(p.select_qua(X_c, X_t, lin5, qua5, C5), ans5) lin6 = [0, 1] qua6 = [(1, 1)] C6 = 2.3 ans6 = [(1, 1), (0, 1), (0, 0)] assert_equal(p.select_qua(X_c, X_t, lin6, qua6, C6), ans6) lin7 = [0, 1] qua7 = [(1, 1), (0, 1)] C7 = 3.9 ans7 = [(1, 1), (0, 1)] assert_equal(p.select_qua(X_c, X_t, lin7, qua7, C7), ans7) lin8 = [0, 1] qua8 = [(1, 1), (0, 1)] C8 = 3.8 ans8 = [(1, 1), (0, 1), (0, 0)] assert_equal(p.select_qua(X_c, X_t, lin8, qua8, C8), ans8)
def test_propensity(): import causalinference.core.data as d import causalinference.core.propensity as p from utils import random_data D = np.array([0, 0, 0, 1, 1, 1]) X = np.array([[7, 8], [3, 10], [7, 10], [4, 7], [5, 10], [9, 8]]) Y = random_data(D_cur=D, X_cur=X) print Y data = d.Data(Y, D, X) propensity = p.Propensity(data, [0, 1], []) print propensity
def test_est_propensity_s(): D = np.array([0, 0, 0, 1, 1, 1]) X = np.array([[7, 8], [3, 10], [7, 10], [4, 7], [5, 10], [9, 8]]) Y = random_data(D_cur=D, X_cur=X) causal = c.CausalModel(Y, D, X) causal.est_propensity_s() lin1 = [1] qua1 = [] coef1 = np.array([6.5424027, -0.7392041]) loglike1 = -3.627939 fitted1 = np.array( [0.6522105, 0.2995088, 0.2995088, 0.7970526, 0.2995088, 0.6522105]) se1 = np.array([6.8455179, 0.7641445]) keys = {'lin', 'qua', 'coef', 'loglike', 'fitted', 'se'} assert_equal(causal.propensity['lin'], lin1) assert_equal(causal.propensity['qua'], qua1) assert np.allclose(causal.propensity['coef'], coef1) assert np.allclose(causal.propensity['loglike'], loglike1) assert np.allclose(causal.propensity['fitted'], fitted1) assert np.allclose(causal.propensity['se'], se1) assert_equal(set(causal.propensity.keys()), keys) assert np.allclose(causal.raw_data['pscore'], fitted1) causal.est_propensity_s([0, 1]) lin2 = [0, 1] qua2 = [] coef2 = np.array([6.8066090, -0.0244874, -0.7524939]) loglike2 = -3.626517 fitted2 = np.array( [0.6491366, 0.3117840, 0.2911631, 0.8086407, 0.3013733, 0.6379023]) se2 = np.array([8.5373779, 0.4595191, 0.8106499]) assert_equal(causal.propensity['lin'], lin2) assert_equal(causal.propensity['qua'], qua2) assert np.allclose(causal.propensity['coef'], coef2) assert np.allclose(causal.propensity['loglike'], loglike2) assert np.allclose(causal.propensity['fitted'], fitted2) assert np.allclose(causal.propensity['se'], se2) assert np.allclose(causal.raw_data['pscore'], fitted2)
def test_est_propensity_s(): D = np.array([0, 0, 0, 1, 1, 1]) X = np.array([[7, 8], [3, 10], [7, 10], [4, 7], [5, 10], [9, 8]]) Y = random_data(D_cur=D, X_cur=X) causal = c.CausalModel(Y, D, X) causal.est_propensity_s() lin1 = [1] qua1 = [] coef1 = np.array([6.5424027, -0.7392041]) loglike1 = -3.627939 fitted1 = np.array([0.6522105, 0.2995088, 0.2995088, 0.7970526, 0.2995088, 0.6522105]) se1 = np.array([6.8455179, 0.7641445]) keys = {'lin', 'qua', 'coef', 'loglike', 'fitted', 'se'} assert_equal(causal.propensity['lin'], lin1) assert_equal(causal.propensity['qua'], qua1) assert np.allclose(causal.propensity['coef'], coef1) assert np.allclose(causal.propensity['loglike'], loglike1) assert np.allclose(causal.propensity['fitted'], fitted1) assert np.allclose(causal.propensity['se'], se1) assert_equal(set(causal.propensity.keys()), keys) assert np.allclose(causal.raw_data['pscore'], fitted1) causal.est_propensity_s([0,1]) lin2 = [0, 1] qua2 = [] coef2 = np.array([6.8066090, -0.0244874, -0.7524939]) loglike2 = -3.626517 fitted2 = np.array([0.6491366, 0.3117840, 0.2911631, 0.8086407, 0.3013733, 0.6379023]) se2 = np.array([8.5373779, 0.4595191, 0.8106499]) assert_equal(causal.propensity['lin'], lin2) assert_equal(causal.propensity['qua'], qua2) assert np.allclose(causal.propensity['coef'], coef2) assert np.allclose(causal.propensity['loglike'], loglike2) assert np.allclose(causal.propensity['fitted'], fitted2) assert np.allclose(causal.propensity['se'], se2) assert np.allclose(causal.raw_data['pscore'], fitted2)
def causal_ATE(): from causalinference import CausalModel from utils import random_data D = np.array([0, 0, 0, 1, 1, 1]) X = np.array([[7, 8], [3, 10], [7, 10], [4, 7], [5, 10], [9, 8]]) Y = random_data(D_cur=D, X_cur=X) print Y causal = CausalModel(Y, D, X) #causal.est_via_ols() #print causal.estimates causal.est_propensity_s() print causal.propensity # -*- coding: utf-8 -*- #プロペンシティスコアを元に自分でマッチングすれば良い。 #estimated propensity scores print causal.propensity['fitted']
def test_propensityselect(): D = np.array([0, 0, 0, 1, 1, 1]) X = np.array([[7, 8], [3, 10], [7, 10], [4, 7], [5, 10], [9, 8]]) Y = random_data(D_cur=D, X_cur=X) data = d.Data(Y, D, X) propensity1 = p.PropensitySelect(data, [], 1, 2.71) lin1 = [1] qua1 = [] coef1 = np.array([6.5424027, -0.7392041]) loglike1 = -3.627939 fitted1 = np.array([0.6522105, 0.2995088, 0.2995088, 0.7970526, 0.2995088, 0.6522105]) se1 = np.array([6.8455179, 0.7641445]) keys = {'lin', 'qua', 'coef', 'loglike', 'fitted', 'se'} assert_equal(propensity1['lin'], lin1) assert_equal(propensity1['qua'], qua1) assert np.allclose(propensity1['coef'], coef1) assert np.allclose(propensity1['loglike'], loglike1) assert np.allclose(propensity1['fitted'], fitted1) assert np.allclose(propensity1['se'], se1) assert_equal(set(propensity1.keys()), keys) propensity2 = p.PropensitySelect(data, [0, 1], 1, 2.71) lin2 = [0, 1] qua2 = [] coef2 = np.array([6.8066090, -0.0244874, -0.7524939]) loglike2 = -3.626517 fitted2 = np.array([0.6491366, 0.3117840, 0.2911631, 0.8086407, 0.3013733, 0.6379023]) se2 = np.array([8.5373779, 0.4595191, 0.8106499]) assert_equal(propensity2['lin'], lin2) assert_equal(propensity2['qua'], qua2) assert np.allclose(propensity2['coef'], coef2) assert np.allclose(propensity2['loglike'], loglike2) assert np.allclose(propensity2['fitted'], fitted2) assert np.allclose(propensity2['se'], se2)
def test_propensityselect(): D = np.array([0, 0, 0, 1, 1, 1]) X = np.array([[7, 8], [3, 10], [7, 10], [4, 7], [5, 10], [9, 8]]) Y = random_data(D_cur=D, X_cur=X) data = d.Data(Y, D, X) propensity1 = p.PropensitySelect(data, [], 1, 2.71) lin1 = [1] qua1 = [] coef1 = np.array([6.5424027, -0.7392041]) loglike1 = -3.627939 fitted1 = np.array( [0.6522105, 0.2995088, 0.2995088, 0.7970526, 0.2995088, 0.6522105]) se1 = np.array([6.8455179, 0.7641445]) keys = {'lin', 'qua', 'coef', 'loglike', 'fitted', 'se'} assert_equal(propensity1['lin'], lin1) assert_equal(propensity1['qua'], qua1) assert np.allclose(propensity1['coef'], coef1) assert np.allclose(propensity1['loglike'], loglike1) assert np.allclose(propensity1['fitted'], fitted1) assert np.allclose(propensity1['se'], se1) assert_equal(set(propensity1.keys()), keys) propensity2 = p.PropensitySelect(data, [0, 1], 1, 2.71) lin2 = [0, 1] qua2 = [] coef2 = np.array([6.8066090, -0.0244874, -0.7524939]) loglike2 = -3.626517 fitted2 = np.array( [0.6491366, 0.3117840, 0.2911631, 0.8086407, 0.3013733, 0.6379023]) se2 = np.array([8.5373779, 0.4595191, 0.8106499]) assert_equal(propensity2['lin'], lin2) assert_equal(propensity2['qua'], qua2) assert np.allclose(propensity2['coef'], coef2) assert np.allclose(propensity2['loglike'], loglike2) assert np.allclose(propensity2['fitted'], fitted2) assert np.allclose(propensity2['se'], se2)
def test_propensity(): D = np.array([0, 0, 0, 1, 1, 1]) X = np.array([[7, 8], [3, 10], [7, 10], [4, 7], [5, 10], [9, 8]]) Y = random_data(D_cur=D, X_cur=X) data = d.Data(Y, D, X) propensity = p.Propensity(data, [0, 1], []) lin = [0, 1] qua = [] coef = np.array([6.8066090, -0.0244874, -0.7524939]) loglike = -3.626517 fitted = np.array([0.6491366, 0.3117840, 0.2911631, 0.8086407, 0.3013733, 0.6379023]) se = np.array([8.5373779, 0.4595191, 0.8106499]) keys = {'lin', 'qua', 'coef', 'loglike', 'fitted', 'se'} assert_equal(propensity['lin'], lin) assert_equal(propensity['qua'], qua) assert np.allclose(propensity['coef'], coef) assert np.allclose(propensity['loglike'], loglike) assert np.allclose(propensity['fitted'], fitted) assert np.allclose(propensity['se'], se) assert_equal(set(propensity.keys()), keys)
def test_invite(self): """ """ self.ip.src = utils.random_ip() self.udp.sport = utils.random_port() self.message.uri = 'sip:{0}@{1}'.format(utils.random_data(20), utils.random_data(15)) self.message.method = 'INVITE' self.message.headers = { 'Call-ID': utils.random_tag(), 'CSeq': '0 INVITE', 'From': '"{0}" <sip:{1}@{2}>;tag={3}'.format(utils.random_data(10), utils.random_data(20), utils.random_data(15), utils.random_tag()), 'Max-Forwards': '{0}'.format(utils.random_number(2)), 'To': '<sip:{0}@{1}>'.format(utils.random_data(20), utils.random_data(15)), 'Via': 'SIP/2.0/UDP {0}:{1};branch={2};rport'.format( utils.random_ip(), utils.random_port(), utils.random_tag()), 'Content-Length': '0', 'User-Agent': '{0}'.format(utils.random_data(30)), 'Contact': '<sip:{0}@{1}:{2};transport=UDP>;' 'q=1.00;agentid="{3}";' 'methods="INVITE,NOTIFY,MESSAGE,ACK,BYE,CANCEL";' 'expires={4}'.format(utils.random_data(20), utils.random_ip(), utils.random_data(20), utils.random_tag(), utils.random_number(2)), 'Authorization': 'Digest username="******", ' 'realm="{2}", ' 'nonce="{3}", ' 'uri="sip:{4}", ' 'qop=auth, nc=00000001, ' 'cnonce="{5}", ' 'response="{6}", ' 'opaque=""'.format(utils.random_data(20), utils.random_data(15), utils.random_data(15), utils.random_tag(), utils.random_data(15), utils.random_tag(), utils.random_data(32)) }
train_writer = tf.summary.FileWriter(savepath + 'train', sess.graph) test_writer = tf.summary.FileWriter(savepath + 'test') """ training session """ weight = z_c_mean.get_shape().as_list()[1] * z_c_mean.get_shape().as_list( )[2] # this is to normalize the loss shown in the terminal against the receptive field for step in range(start_step, start_step + training_step): if step % 20000 == 0 or step == start_step: print('Starting @step %s' % step) batch = random_data(MRtrain, batchsize=batchsize)[:batchsize, 22:22 + imageshape[0], 17:17 + imageshape[1]] batch = np.expand_dims(batch, axis=-1) t_los, p_los, con_los, w_los, c_los, _ = sess.run( [t_loss, p_loss, con_loss, w_loss, c_loss, optimization], feed_dict={x: batch.reshape(batchsize, -1)}) if step == 0 or os.path.isdir(savepath + 'model') == 0: os.makedirs(os.path.join(savepath, 'model', '')) if step % 5000 == 0 and step >= start_step or step == start_step + training_step - 1: model_path = saver.save(sess, savepath + 'model/model.ckpt',