ASTGCN
sample = [] # [(week_sample),(day_sample),(hour_sample),target,time_sample]
sample.append(hour_sample) # (1, vertices, features, sequences)
time_sample # [[14]]
用当前12小时的数据预测下一12小时的数据
用到num_of_weeks, num_of_days, num_of_hours 3个维度的信息
train_x.shape
Out[8]: (10181, 307, 3, 12)
# (sequences, vertices, features, sub_sequences)
顶点信息
train_x[0,:,0,0]
Out[9]:
array([ 62., 56., 90., 32., 19., 68., 24., 24., 31., 31., 90.,
35., 124., 36., 27., 37., 68., 91., 75., 71., 30., 21.,
16., 30., 36., 37., 82., 17., 120., 72., 60., 14., 97.,
69., 50., 33., 38., 42., 31., 93., 47., 255., 85., 83.,
特征信息
train_x[0,0,:,0]
Out[10]: array([6.20e+01, 7.70e-03, 6.79e+01])
all_samples = [[hour_sample, target, timestamp]]
train_x = np.concatenate(training_set[:-2], axis=-1) # (B,N,F,T') == train_x = np.concatenate([training_set[0]], axis=-1)
training_set[0].shape
Out[17]: (10181, 307, 3, 12)
training_set[1].shape
Out[18]: (10181, 307, 12)
training_set[2].shape
Out[19]: (10181, 1)
所以0 1 2 三个特征到底啥意思
flow, occupy, speed
np.expand_dims(target, axis=0).transpose((0, 2, 3, 1))[:, :, 0, :]
Out[17]:
array([[[162., 126., 135., ..., 111., 116., 96.],
[102., 127., 129., ..., 128., 93., 82.],
[ 96., 109., 66., ..., 78., 146., 92.],
...,
[173., 154., 173., ..., 133., 127., 120.],
[ 96., 88., 62., ..., 57., 70., 70.],
[ 83., 100., 102., ..., 100., 74., 84.]]])
np.expand_dims(target, axis=0).transpose((0, 2, 3, 1))[:, :, 1, :]
Out[18]:
array([[[0.0435, 0.0165, 0.0179, ..., 0.015 , 0.0146, 0.0127],
[0.0246, 0.0348, 0.0319, ..., 0.0296, 0.0213, 0.0219],
[0.016 , 0.0176, 0.0112, ..., 0.0127, 0.0238, 0.015 ],
...,
[0.0292, 0.0255, 0.0302, ..., 0.022 , 0.0217, 0.0202],
[0.0159, 0.0146, 0.01 , ..., 0.0097, 0.0116, 0.0113],
[0.0205, 0.0258, 0.0259, ..., 0.0252, 0.018 , 0.0213]]])
np.expand_dims(target, axis=0).transpose((0, 2, 3, 1))[:, :, 2, :]
Out[19]:
array([[[59. , 63.5, 64.6, ..., 67.5, 66.2, 67. ],
[60.5, 58.9, 59.1, ..., 61.6, 62.5, 59.6],
[67.9, 67.8, 68.2, ..., 68.7, 68.2, 68.2],
...,
[66.2, 67. , 65.8, ..., 67.7, 67.2, 66.9],
[68.9, 68.8, 68.7, ..., 68. , 69. , 69. ],
[68.2, 68.2, 68. , ..., 68.7, 68.9, 68.5]]])
train_x.shape
Out[2]: (10181, 307, 3, 12)
val_x.shape
Out[3]: (3394, 307, 3, 12)
test_x.shape
Out[4]: (3394, 307, 3, 12)
看到归一化函数prepareData.normalization， mean = train.mean(axis=(0,1,3), keepdims=True)
看到axis=2的轴是特征轴，所以对每列特征分别进行归一化