i'm a newbie in programming. I try to use this library. it's very useful for me.
I want to show centroid in K-means clustering. how to show it? thank u so much..

https://github.com/rasbt/mlxtend/blob/115278bac14d7fc278885c0722da03f1c3b91604/mlxtend/frequent_patterns/apriori.py#L224

Processing 24785850 combinations | Sampling itemset size 6
Traceback (most recent call last):
File "***.py", line 116, in
frequent_itemsets = apriori(df, min_support=0.8, use_colnames=True, verbose=1)

File "C:\ProgramData\Anaconda3\lib\site-

Extend NaiveForecaster to include all common naive forecasting strategies. For an overview, see this chapter.

• introduce seasonal as boolean kwarg, refactor "seasonal_last" and implement "seasonal_mean", so that we can set seasonal=True and strategy="mean" for example
• add "drift" strategy, the forecasts should be

Frequently we need to extract part of dataset for test network for example.
We need to implement this feature in DataProducer, cause it'll make possible to not change dataset class. Also, DataProsucer has flushing and loading indices interfaces.

Requirements:

• Possible of flushing indices for reproducible
• Different strategies for indices selection: (from begin, from range, all of it with

fastText supervised model does not take into account of the document and words representation, it just learns bag of words and labels.
embeddings are computed only on the relation word->label. it would be interesting to learn jointly the semantic relation label<->document<->word<->context.
for now it is only possible to pre-train word embeddings and then use them as initial vectors for the clas

• In Section: 19.4 Denoising autoencoders
• Just under figure: Figure 19.8: Original digit sampled from the MNIST test set (left), corrupted data with on/off imputation (middle), and corrupted data with Gaussian imputation (right).
• On line #4: "have been corrupted with Gaussian noise (inputs_currupted_gaussian) and supply the original input"
  • "inputs_currupted_gaussian" => corrupted