As title
I have one huge data to load, so I need to train it incrementally.
So I load data segment by segment and train segment by segment like:
MiniBatchKMeans.
In that condition, how to tune parameters? tune the first part of data or
every part of data?
federico vaggi 於 2019年6月7日 週五 上午1:08寫道：
> k-means isn't a convex problem, unless you freeze the initialization, you
> are going to get very different solutions (depending on the dataset) with
> different initializations.
>
>
Nope, I specify the random_state=0. u can try it.
>>> x = np.array([[1,
The clusters produces by your examples are actually the same (despite the
different labels).
I'd guess that "fit" and "partial_fit" draw a different amount of
random_numbers before actually assigning a label to the first (randomly
drawn) sample from "x" (in your code). This is why the labeling is
