kmeans | K-Means algorithm implementation in Go | Learning library
kandi X-RAY | kmeans Summary
kandi X-RAY | kmeans Summary
K-Means === kmeans is BSD licensed fast k-means implementation(with better initialization, known as k-means++) written in Go. It supports various distance functions out of the box for convenience and experimentation. It has large coverage for tests. The algorithm is tested on Iris dataset and distance functions have full test coverage.
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QUESTION
1. sort(arr1.begin(), arr1.end(), [](Point2f lhs, Point2f rhs) { return lhs.x
...ANSWER
Answered 2021-Jun-07 at 13:46There is a useful tool for converting short snippets of codes like that.
Notice that the free edition limits output to 100 lines per file (no limit on the number of files).
visit: https://www.tangiblesoftwaresolutions.com/product_details/cplusplus_to_csharp_converter_details.html
QUESTION
I have been trying to split the dataset into train and test data for deployment using Streamlit.
...ANSWER
Answered 2021-Jun-05 at 05:33To select a column, remove the () from df(['LTVCluster'])
:
QUESTION
I'm trying to plot a silhouette plot for a K-Means model I've run, however, I get the error: ImportError: cannot import name 'safe_indexing' from 'sklearn.utils
.
I was initially getting the NotFoundError issue described in this post here however I got this issue once I fixed it. My code is below:
...ANSWER
Answered 2021-Jan-09 at 03:58In python3.7 with yellowbrick v1.2 and scikit-learn v0.23.2 I don't get the error but when I bump scikit-learn to v0.24.0 I get the same ImportError.
I checked the API documentation and, like in the link you referenced, in sklearn v0.22.0 they deprecated utils.safe_indexing
to private (utils._safe_indexing
). But I don't get the import error with yellowbrick v1.2 and v0.22.0<=sklearn<=v0.23.2 installed. I think they just finally removed the public utils.safe_indexing
in v0.24.0 so if you just install a version of scikit-learn before v0.24.0 the import should work.
Edit: Here is yellowbrick's github issue if you want to track their progress on a workaround or update for this problem
QUESTION
I'm working on Kmeans clustering but unlike supervised learning I cannot figure the performance metrics for clustering algorithms. How to perform the accuracy after training the data?
...ANSWER
Answered 2021-Jun-03 at 19:39For kmeans
you can find the inertia_
of it. Which can give you an idea how well kmeans
algorithm has worked.
QUESTION
I have a 4 column dataframe which I extracted from the iris dataset. I use kmeans to plot 3 clusters from all possible combinations of 2 columns.
However, there seems to be something wrong with the output, especially since the cluster centers are not placed at the center of the clusters. I have provided examples of the output. Only cluster_1 seems OK but the other 3 look completely wrong .
How best can I fix my clustering? This is the sample code I am using
...ANSWER
Answered 2021-May-31 at 23:51You compute the clusters in four dimensions. Note this implies the centroids are four-dimensional points too. Then you plot two-dimensional projections of the clusters. So when you plot the centroids, you have to pick out the same two dimensions that you just used for the scatterplot of the individual points.
QUESTION
I am apply TSNE for dimensionality reduction. I have several features that I reduce to 2 features. After, I use Kmeans to cluster the data. Finally, I use seaborn to plot the clustering results.
To import TSNE I use:
...ANSWER
Answered 2021-May-24 at 14:53Is something wrong with the procedure I follow?
Yes.
Using TSNE projects data onto another space, on which you have no real control.
Doing so is supposed to keep close points close, and far points far.
You then use KNN on the projected space to determine the groups.
This part loses any grouping information you previously had [citation needed, need to see what the data was beforehand]!
It would make much more sense to color the groups according to some prior labeled data, not according to KNN
-OR-
to use KNN on the original space for grouping, and then to color the projected space according to that grouping.
What you did in fact is meaningless, as it loses all prior information - labels and spatial.
To conclude:
- If you have labels, use them.
- If you don't, use a more sophisticated clustering algorithm, starting with KNN on the original space, as you can see KNN on the projected space is not enough.
QUESTION
I would like to cluster below dataframe for column X3 and then for each cluster find mean of X3 then assign 3 for highest mean and 2 for lower and 1 for lowest mean. Below data frame
...ANSWER
Answered 2021-May-24 at 12:34While assigning ranks, Make sure to group it on the basis of month.
Complete code:
QUESTION
I would like to cluster below dataframe for each month for column X3. How can I do that?
...ANSWER
Answered 2021-May-24 at 07:40KMeans
of sklearn
often expect features to be a 2-d array, instead of a 1-d vector as you passed. So you need to modify your X
to be an array. Besides, if you want to rely on group-by-combine
mechanism, why not put column indexing within the to-apply function, since assigning from such an operation is cumbersome.
QUESTION
I am trying to use the KMeans clustering from faiss on a human pose dataset of body joints. I have 16 body parts so a dimension of 32. The joints are scaled in a range between 0 and 1. My dataset consists of ~ 900.000 instances. As mentioned by faiss (faiss_FAQ):
As a rule of thumb there is no consistent improvement of the k-means quantizer beyond 20 iterations and 1000 * k training points
Applying this to my problem I randomly select 50000 instances for training. As I want to check for a number of clusters k between 1 and 30.
Now to my "problem":
The inertia is increasing directly as the number of cluster increases (n_cluster on the x-axis):
I tried varying the number of iterations, the number of redos, verbose and spherical, but the results stay the same or get worse. I do not think that it is a problem of my implementation; I tested it on a small example with 2D data and very clear clusters and it worked.
Is it that the data is just bad clustered or is there another problem/mistake I have missed? Maybe the scaling of the values between 0 and 1? Should I try another approach?
...ANSWER
Answered 2021-May-20 at 16:46I found my mistake. I had to increase the parameter max_points_per_centroid. As I have so many data points it sampled a sub-batch for the fit. For a larger number of clusters this sub-batch is larger. See FAQ of faiss:
max_points_per_centroid * k: there are too many points, making k-means unnecessarily slow. Then the training set is sampled
The larger subbatch of course has a larger inertia as there are more points in total.
QUESTION
just a basic question concerning k-means clustering analysis on survival data, like this one:
I am doing k-means clustering to identify clusters which Gene influences the survival most... However do I include the survival time into my k-means function or should I leave it out? So should I put it into the kmeans() function e.g. in R?
Kind regards,
Hashriama
...ANSWER
Answered 2021-May-19 at 12:31I think that your approach is not the best one. Your goal is to select genes associated with censored/uncensored survival. The use of supervised methods seems the most suitable. Using a k-means will only cluster genes by similarities without regard to survival, and even if you wanted to add survival in your modeling it would not make sense because you are omitting censoring.
There are Cox regressions to which an L1 penalty is added, allowing variable selection without omitting censoring. This kind of approach seems more appropriate to accomplish your goal and fits better in your context. To learn more, here is an article from Jiang Gui & Hongzhe Li that uses penalized Cox regression (look at the R package biospear too if needed): https://academic.oup.com/bioinformatics/article/21/13/3001/196819
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