Fit the centre and population deviation on training values only. Reuse both statistics for validation; a constant training column uses scale one.

train = [0, 2]
mean = sum(train) / len(train)
print([(value-mean) / 1 for value in [1, 3]])

Fit the centre and population deviation on training values only. Reuse both statistics for validation; a constant training column uses scale one. The trace reads the local variables of your function as it executes. Change one input or parameter, predict the result, then run the checks again. A small deterministic example makes arithmetic mistakes easier to locate than a long training run. State the shape and meaning of the returned value before coding. Keep preprocessing, evaluation and model updates in their proper order, and examine the boundary case in each check. The companion lab lets you replay intermediate values while the same algorithm runs.

normalize(train, values) returns values standardized with the training mean and population standard deviation. train is nonempty. Use scale 1 for a constant column.

The explanation opens here once every check passes.