def fit_model(self):
# 提前结束训练，避免过拟合，加快训练速度
# 如果连续若干轮迭代，模型都未能变得更好，就提前结束训练
# 连续patience轮迭代，准确率增加量都没有超过阈值min_delta，或者没有增加，则结束训练
early_stopping = EarlyStopping(monitor='acc',
patience=5,
min_delta=0.0001,
verbose=1,
mode='max')
# train network
# 逼近隐藏函数 => loss最小，优化问题 => 梯度下降，近似求解
history = self.model.fit(self.X,
self.y,
epochs=500,
batch_size=1,
verbose=1,
callbacks=[early_stopping],
shuffle=True)
print(
'\n========================== history ===========================')
acc = history.history.get('acc') # fit函数返回History对象，它有一个history字典
loss = history.history['loss'] # acc和loss都是列表
print('train data acc:', acc
) # 训练数据的loss和acc，验证集的val_loss和val_acc，见印象笔记“2018.1.1-2018.1.22”
print('train data loss:', loss)
print(
'\n======================= acc & loss ============================'
)
for i in range(len(acc)):
print('epoch {0:<4} | acc: {1:6.3f}% | loss: {2:<10.5f}'.format(
i + 1, acc[i] * 100, loss[i]))
plt_x = [x + 1 for x in range(len(acc))]
plt_acc = plt_x, acc
plt_loss = plt_x, loss
tools.plot_figure('acc & loss', plt_acc, plt_loss)
def fit_model_with_generator(self):
early_stopping = EarlyStopping(monitor='val_loss',
patience=5,
min_delta=0.0001,
verbose=1,
mode='min')
# 训练集、验证集和测试集的比例为7:2:1 => done
# steps应自适应于BATCH_SAMPLES_NUMBER => done
batch_samples_number = parameters.BATCH_SAMPLES_NUMBER
history = self.model.fit_generator(
tools.generate_batch_samples_from_corpus(self.train_data_path,
self.tokenizer,
self.vocab_size,
self.max_length),
validation_data=tools.generate_batch_samples_from_corpus(
self.val_data_path, self.tokenizer, self.vocab_size,
self.max_length),
validation_steps=self.val_samples_num / batch_samples_number,
steps_per_epoch=self.train_samples_num / batch_samples_number,
epochs=1000,
verbose=1,
callbacks=[early_stopping])
print(
'\n========================== history ===========================')
acc = history.history.get('acc')
loss = history.history['loss']
val_acc = history.history['val_acc']
val_loss = history.history['val_loss']
print('train data acc:', acc)
print('train data loss', loss)
print('val data acc', val_acc)
print('val data loss', val_loss)
print(
'\n======================= acc & loss & val_acc & val_loss ============================'
)
for i in range(len(acc)):
print('epoch {0:<4} | acc: {1:6.3f}% | loss: {2:<10.5f} |'
' val_acc: {3:6.3f}% | val_loss: {4:<10.5f}'.format(
i + 1, acc[i] * 100, loss[i], val_acc[i] * 100,
val_loss[i]))
# 训练完毕后，将每轮迭代的acc、loss、val_acc、val_loss以画图的形式进行展示 => done
plt_x = [x + 1 for x in range(len(acc))]
plt_acc = plt_x, acc
plt_loss = plt_x, loss
plt_val_acc = plt_x, val_acc
plt_val_loss = plt_x, val_loss
tools.plot_figure('acc & loss & val_acc & val_loss', plt_acc, plt_loss,
plt_val_acc, plt_val_loss)
= tools.extract_da_from_sol(fname_sf_p_1)
t, sf_p_100_pe_kf, sf_p_100_pn_kf, sf_p_100_err_p_kf, pe_ref, pn_ref, \
sf_p_100_err_p_kf, sf_p_100_sqt_cov_p_kf \
= tools.extract_da_from_sol(fname_sf_p_100)
t, sf_p_10000_pe_kf, sf_p_10000_pn_kf, sf_p_10000_err_p_kf, pe_ref, pn_ref, \
sf_p_10000_err_p_kf, sf_p_10000_sqt_cov_p_kf \
= tools.extract_da_from_sol(fname_sf_p_10000)
"""
Plot
"""
fig = tools.plot_figure([sf_p_0001_pe_kf, sf_p_1_pe_kf, sf_p_100_pe_kf, sf_p_10000_pe_kf, pe_ref],
[sf_p_0001_pn_kf, sf_p_1_pn_kf, sf_p_100_pn_kf, sf_p_10000_pn_kf, pn_ref], 5,
[len(sf_p_0001_pe_kf),len(sf_p_1_pe_kf),len(sf_p_100_pe_kf),len(sf_p_10000_pe_kf), len(pe_ref)],
["co-", "b*-", "rp-", "m+-", "g.-"],
["$s_{P0} =0.0001$", "$s_{P0} =1$",
"$s_{P0} =100$", "$s_{P0} =10000$", "ref"],
"Position", "East ($m$)",
"North ($m$)", '', 1, 1, 0, [], 0, [], fs=FS_PLOT,
if_use_shift=1, x_shift_legend=SHIFT_LEGEND_X+0.15)
pdf_P0.savefig(fig)
fig = tools.plot_figure([sf_p_0001_pe_kf, sf_p_1_pe_kf, sf_p_100_pe_kf, sf_p_10000_pe_kf, pe_ref],
[sf_p_0001_pn_kf, sf_p_1_pn_kf, sf_p_100_pn_kf, sf_p_10000_pn_kf, pn_ref], 5,
[len(sf_p_0001_pe_kf),len(sf_p_1_pe_kf),len(sf_p_100_pe_kf),len(sf_p_10000_pe_kf), len(pe_ref)],
["co-", "b*-", "rp-", "m+-", "g.-"],
["$s_{P0} =0.0001$", "$s_{P0} =1$",
"$s_{P0} =100$", "$s_{P0} =10000$", "ref"],
"Position", "East ($m$)",
"North ($m$)", '', 1, 0, 1, [-2,2], 1, [-2,20], fs=FS_PLOT,
if_use_shift=1, x_shift_legend=SHIFT_LEGEND_X+0.15,
vrw_001_err_p_kf, vrw_001_sqt_cov_p_kf \
= tools.extract_da_from_sol(fname_VRW_001)
t, vrw_01_pe_kf, vrw_01_pn_kf, vrw_01_err_p_kf, pe_ref, pn_ref, \
vrw_01_err_p_kf, vrw_01_sqt_cov_p_kf \
= tools.extract_da_from_sol(fname_VRW_01)
t, vrw_1_pe_kf, vrw_1_pn_kf, vrw_1_err_p_kf, pe_ref, pn_ref, \
vrw_1_err_p_kf, vrw_1_sqt_cov_p_kf \
= tools.extract_da_from_sol(fname_VRW_1)
"""
Plot
"""
fig = tools.plot_figure([vrw_0001_pe_kf, vrw_001_pe_kf, vrw_01_pe_kf, vrw_1_pe_kf, pe_ref],
[vrw_0001_pn_kf, vrw_001_pn_kf, vrw_01_pn_kf, vrw_1_pn_kf, pn_ref], 5,
[len(vrw_0001_pe_kf),len(vrw_001_pe_kf),len(vrw_01_pe_kf),len(vrw_1_pe_kf), len(pe_ref)],
["co-", "b*-", "r+-", "mp-", "g.-"],
["$q_a=0.001 m/s/\u221A s$","$q_a= 0.01 m/s/\u221A s$","$q_a= 0.1 m/s/\u221A s$", "$q_a= 1 m/s/\u221A s$", "ref"],
"Position", "East ($m$)",
"North ($m$)", '', 1, 1, 0, [], 0, [], fs=FS_PLOT,
if_use_shift=1, x_shift_legend=SHIFT_LEGEND_X+0.3)
pdf_Q_qa.savefig(fig)
fig = tools.plot_figure([t, t, t, t],
[vrw_0001_err_p_kf, vrw_01_err_p_kf, \
vrw_0001_sqt_cov_p_kf, vrw_01_sqt_cov_p_kf], 4,
[len(t),len(t),len(t),len(t)],
["bo-", "r+-", "c-.","m:"],
["$e_{p}$ ($q_a= 0.001 m/s/\u221A s$)",
"$e_{p}$ ($q_a= 0.1 m/s/\u221A s$)",
"$\sigma_{p}$ ($q_a= 0.001 m/s/\u221A s$)",
"$\sigma_{p}$ ($q_a= 0.1 m/s/\u221A s$)",
],
r_1_err_p_kf, r_1_sqt_cov_p_kf \
= tools.extract_da_from_sol(fname_R_1)
t, r_10_pe_kf, r_10_pn_kf, r_10_err_p_kf, pe_ref, pn_ref, \
r_10_err_p_kf, r_10_sqt_cov_p_kf \
= tools.extract_da_from_sol(fname_R_10)
t, r_100_pe_kf, r_100_pn_kf, r_100_err_p_kf, pe_ref, pn_ref, \
r_100_err_p_kf, r_100_sqt_cov_p_kf \
= tools.extract_da_from_sol(fname_R_100)
"""
Plot
"""
fig = tools.plot_figure([r_01_pe_kf, r_1_pe_kf, r_10_pe_kf, r_100_pe_kf, pe_ref],
[r_01_pn_kf, r_1_pn_kf, r_10_pn_kf, r_100_pn_kf, pn_ref], 5,
[len(r_01_pe_kf),len(r_1_pe_kf),len(r_10_pe_kf),len(r_100_pe_kf), len(pe_ref)],
["c*-", "bo-", "r+-", "mp-", "g.-"],
["$\sigma_{R}=0.1 m$","$\sigma_{R}= 1 m$","$\sigma_{R}= 10 m$", "$\sigma_{R}= 100 m$", "ref"],
"Position", "East ($m$)",
"North ($m$)", '', 1, 1, 0, [], 0, [], fs=FS_PLOT,
if_use_shift=1, x_shift_legend=SHIFT_LEGEND_X+0.1)
pdf_R.savefig(fig)
fig = tools.plot_figure([t, t, t, t],
[r_1_err_p_kf, r_10_err_p_kf, \
r_1_sqt_cov_p_kf, r_10_sqt_cov_p_kf], 4,
[len(t),len(t),len(t),len(t)],
["bo-", "r+-", "c-.","m:"],
["$e_{p}$ ($\sigma_{R}= 1 m$)",
"$e_{p}$ ($\sigma_{R}= 10 m$)",
"$\sigma_{p}$ ($\sigma_{R}= 1 m$)",
"$\sigma_{p}$ ($\sigma_{R}= 10 m$)",
],
t, bd_1_pe_kf, bd_1_pn_kf, bd_1_err_p_kf, pe_ref, pn_ref, \
bd_1_err_p_kf, bd_1_sqt_cov_p_kf \
= tools.extract_da_from_sol(fname_BD_1)
"""
Plot
"""
fig = tools.plot_figure(
[bd_0_pe_kf, bd_1_pe_kf, pe_ref], [bd_0_pn_kf, bd_1_pn_kf, pn_ref],
3, [len(bd_0_pe_kf), len(bd_1_pe_kf),
len(pe_ref)], [
"r*-",
"bo-",
"g.-",
], ["$p_{2D}$ (no BD)", "$p_{2D}$ (with BD)", "ref"],
"Position without/with blunder detection",
"East ($m$)",
"North ($m$)",
'',
1,
1,
0, [],
0, [],
fs=FS_PLOT,
if_use_shift=1,
x_shift_legend=SHIFT_LEGEND_X + 0.1)
pdf_BD.savefig(fig)
bd_0_cdf_x, bd_0_cdf_y = tools.cal_cdf(bd_0_err_p_kf)
bd_1_cdf_x, bd_1_cdf_y = tools.cal_cdf(bd_1_err_p_kf)
fig = tools.plot_figure([bd_0_cdf_x, bd_1_cdf_x],
# Simulate reference velocity and position
for i in range(int(T_TOTAL / D_T)):
t_cur = D_T * i
pe_ref, pn_ref, ve_ref, vn_ref, t_ref = \
put_pos_vel_t_2_list(pe, pn, ve, vn, t_cur,
pe_ref, pn_ref, ve_ref, vn_ref, t_ref)
theta = set_thera_square(t_cur)
pe, pn, ve, vn = update_vel_pos(pe, pn, theta, VEL, D_T)
fig = tools.plot_figure([t_ref, t_ref], [ve_ref, vn_ref],
2, [len(t_ref), len(t_ref)], ["b.-", "r+-"],
["Ve", "Vn"],
"Reference velocity",
"Time ($sec$)",
"Velocity ($m/s$)",
'',
1,
0,
0, [],
0, [],
fs=FS_PLOT)
pdf.savefig(fig)
#fig = tools.plot_figure([pe_ref], [pn_ref], 1, [len(pe_ref)], ["b.-"],
# ["p"], "Reference Position", "x ($m$)",
# "y ($m$)", '', 1, 1, 0, [], 0, [], fs=FS_PLOT)
#pdf.savefig(fig)
"""
Generate observations
"""
# Add measurement noises
