python：导航 4-D numpy 阵列

Question

I am working with a 4-D array input to a CNN network. The input array has the following shape

print('X_train shape: ', X_train.shape)
X_train shape:  (47204, 1, 100, 4)

Data description:

The input data consists of a 47204 instances (fixed-length segments as far CNN requirement). Each instance (1, 100, 4) ie 1 segment contains 100-GPS points , and for each point, 4- corresponding point kinematics (max_speed, avg_speed, max_acc, avg_acc) are stored, thus the (1, 100, 4) . Labels are stored in a separate y_train array of shape (47204,) for 5 classes [0..4] .

print(y_train)
[3 3 0 ... 2 3 4]

To get a better sense of my X_train array, I show the first 3 elements below:

print(X_train[1:3])
[
 [[[ 3.82280987e+00 2.16802350e-01  7.49917451e-02  3.44416369e-04]
   [ 3.38707371e+00 2.02210055e-01  1.61751110e-03  1.93745950e-03]
   [ 2.49202215e+00 1.60605262e-01  8.43561351e-03  2.40057917e-03]
   ...
   [ 2.00022316e+00 2.70020923e-01  5.40441673e-02  3.57212151e-03]
   [ 3.25199744e-01 9.06990382e-02  1.46808316e-02  1.65841315e-03]
   [2.96587589e-01  0.00000000e+00  6.13293351e-04 4.16518187e-03]]]

 [[[ 1.07209176e+00 7.27038312e-02 6.62777026e-03  2.04611951e-04]
   [ 1.06194285e+00 5.05005456e-02 4.05676569e-03  3.72293433e-04]
   [ 1.02849748e+00 2.12558178e-02 2.95477005e-03  5.56584054e-04]
   ...
   [ 4.51962909e-03 5.63125736e-04 5.98474074e-04  1.63036715e-05]
   [ 2.83026181e-03 2.35855075e-03  1.25789358e-03 2.15331510e-06]
   [8.49078543e-03  2.16840434e-19 9.43423077e-04 1.29198906e-05]]]

 [[[ 7.51127665e+00 3.14033478e-01  6.85170617e-02  7.73415075e-04]
   [ 7.42307262e+00 1.33868251e-01  4.10564823e-02  1.16131460e-03]
   [ 7.35818066e+00  1.23886976e-02  3.02312582e-02  1.28312101e-03]
   ...
   [ 7.40826167e+00 1.19388656e-01 4.00874715e-02  2.04909489e-04]
   [ 7.23779176e+00 1.33269965e-01  1.20430502e-02  1.58195900e-04]
   [ 7.11697001e+00 4.68002105e-02  5.42478400e-02  3.58101318e-05]]]
]

Task:

I am required to create a machine learning model (eg random forest) using the 4 kinematics (max_speed, avg_speed, max_acc, avg_acc) as features. This requires navigating each instance and getting these features for the 100-points in the instance.

Clearly, the number of samples will then be 4720400 (ie 47204 x 100 ), so would also match each value to the corresponding label of its instances, ie y_train will then be (4720400,) .

The expected input would then be like:

      max_speed     avg_speed         max_acc       avg_acc   class
0 3.82280987e+00 2.16802350e-01  7.49917451e-02  3.44416369e-04 3
1 3.38707371e+00 2.02210055e-01  1.61751110e-03  1.93745950e-03 3
2 2.49202215e+00 1.60605262e-01  8.43561351e-03  2.40057917e-03 3
...

I have being thinking about how to do this all through the week, all ideas evaporated. How do I do this, please?

Answer 1

You can reshape your X_train array from (47204, 1, 100, 4) to (4720400, 4) simply with:

X_train_reshaped = X_train.reshape(4720400, 4)

It preserves the data order and the total number of elements will be the same.

Similarly, you can expand y_train array using repeat command:

Y_train_reshaped = numpy.repeat(Y_train, 100)

Note the 100 for repeat command. Since you had one label for 100 data points, we will expand these items 100 times. This command will preserve data order too so all instances will have the same original label.