does k-means clusterer of apache commons math contains a means method?
Date : March 29 2020, 07:55 AM
it should still fix some issue The output of the clustering algorithm must at least contain the cluster assignments, i.e. which cluster each point belongs to. If you have that, then the k-means clustering cluster centers are simply given by the mean of the points that belong to each cluster.
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Boxplot overlaid on dot plot + means, means in wrong position
Date : March 29 2020, 07:55 AM
this one helps. The help for ?position_dodge just says that dodging things with different widths is tricky - I usually tweak this manually. Trying a few values, it looks like you want the points to take a dodge width that is 3/4 of the boxplot width but I don't know why or if that holds for other geoms. I would try changing the width for the stat_summary call to 0.15
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Rolling means and applying means at beginning of a series of data
Date : March 29 2020, 07:55 AM
wish help you to fix your issue Use right aligment with partial=TRUE, i.e. rollapplyr(..., partial=TRUE) or rollapply(..., align = "right", partial=TRUE). Here we use rollapplyr: rollapplyr(df$a, 4, mean, partial = TRUE)
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pandas calculates column value means on groups and means across whole dataframe
Tag : python , By : Der Ketzer
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I have a df, df['period'] = (df['date1'] - df['date2']) / np.timedelta64(1, 'D') , pivot_table with margins=True piv = df.pivot_table(
index='code', columns='y_m', values='period', aggfunc='mean', margins=True
)
# housekeeping
(piv.reset_index()
.rename_axis(None, 1)
.rename({'code' : -1, 'All' : 0}, axis=1)
.sort_index(axis=1)
)
-1 0 201701 201702
0 1000 1.750000 1.5 2.0
1 2000 1.200000 1.5 1.0
2 All 1.444444 1.5 1.4
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Scikit Learn K-means Clustering & TfidfVectorizer: How to pass top n terms with highest tf-idf score to k-means
Date : March 29 2020, 07:55 AM
this will help I am clustering the text data based on TFIDF vectorizer. The code works fine. It takes entire TFIDF vectorizer output as input to the K-Means clustering and generate a scatter plots. Instead I would like to send only top n-terms based on TF-IDF scores as input to the k-means clustering. Is there a way to achieve that ? , use max_features in TfidfVectorizer to consider the top n features vect = TfidfVectorizer(ngram_range=(1,3),stop_words='english', max_features=n)
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