umap | UEFI bootkit for driver manual mapping
kandi X-RAY | umap Summary
kandi X-RAY | umap Summary
Windows UEFI bootkit that loads a generic driver manual mapper without using a UEFI runtime driver.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of umap
umap Key Features
umap Examples and Code Snippets
Community Discussions
Trending Discussions on umap
QUESTION
I am plotting different umaps. I have a part of the code that worked yesterday, however today I get the error message: Error in do.call(c, lapply(2:ncol(nn_idx), function(i) as.vector(rbind(nn_idx[, : 'what' must be a function or character string"
My code is the following:
...ANSWER
Answered 2022-Apr-07 at 17:52Maybe you have overwritten the primitive function c ? R lets you do that, I was able to replicate your error bellow, and to fix it you can just remove c and it will revert back to the primitive function, so you can try that, please let me know if it fixes your problem.
QUESTION
Consider the following dash app which is used inside a flask app:
...ANSWER
Answered 2022-Apr-01 at 21:17I had to filter data before using it in callbacks. Now it looks like below:
QUESTION
I am trying to understand how Top2Vec works. I have some questions about the code that I could not find an answer for in the paper. A summary of what the algorithm does is that it:
- embeds words and vectors in the same semantic space and normalizes them. This usually has more than 300 dimensions.
- projects them into 5-dimensional space using UMAP and cosine similarity.
- creates topics as centroids of clusters using HDBSCAN with Euclidean metric on the projected data.
what troubles me is that they normalize the topic vectors. However, the output from UMAP is not normalized, and normalizing the topic vectors will probably move them out of their clusters. This is inconsistent with what they described in their paper as the topic vectors are the arithmetic mean of all documents vectors that belong to the same topic.
This leads to two questions:
How are they going to calculate the nearest words to find the keywords of each topic given that they altered the topic vector by normalization?
After creating the topics as clusters, they try to deduplicate the very similar topics. To do so, they use cosine similarity. This makes sense with the normalized topic vectors. In the same time, it is an extension of the inconsistency that normalizing topic vectors introduced. Am I missing something here?
...ANSWER
Answered 2022-Feb-16 at 16:13I got the answer to my questions from the source code. I was going to delete the question but I will leave the answer any way.
It is the part I missed and is wrong in my question. Topic vectors are the arithmetic mean of all documents vectors that belong to the same topic. Topic vectors belong to the same semantic space where words and documents vector live.
That is why it makes sense to normalize them since all words and documents vectors are normalized, and to use the cosine metric when looking for duplicated topics in the higher original semantic space.
QUESTION
I have this code:
...ANSWER
Answered 2022-Feb-05 at 07:04Result of std::next(itr1, 3)
is beyond umap.end()
and is the source of segfault. More of, umap.end()
and umap.begin()
would be invalidated by content of unordered_map
. This would work but wouldn't allow use of std::distance
anyway:
QUESTION
I am trying to use the Isolation Forest algorithm in the Solitude package to identify anomalous rows in my data.
I'm using the examples in the documentation to learn about the algorithm, this example uses the Pima Indians Diabetes dataset.
At the end of the example it provides a dataframe of ids, average_depth and anomaly_score sorted from highest score to lowest.
How can I tie back the results of the model to the original dataset to see the rows with the highest anomaly score?
Here's the example from the package documentation
...ANSWER
Answered 2022-Jan-22 at 01:29Well this was a bit hard.
Let me know if this code helps you:
QUESTION
Am using intelij and Ideavim.
I remapped Esc to gh
by typing inoremap gh
in .ideavimrc
and its been working fine. Recently, I found out that while typing a word like "High
light", the gh
inbetween will always be read as gh
and then vim will move out of insert mode.
I've tried to revert it by deleting the content in .ideavim
but the old gh
is still working.
I've also tried changing it to gg
and then to jj
in an attempt to may be erase the old gh
, but they seem to be pilling up.
When I type in :imap, I see this
And all three of them, gg
, gh
, and jj
are all working as Esc when in insert mode.
How do I get rid of the rest and leave only jj
?
I've also tried a lot of suggestions, like using iumap or umap but it doesn't seem to be working. Am on windows.
...ANSWER
Answered 2022-Jan-07 at 18:30The solution was imapclear
. This cleared all the previous remaps I made.
Note, it will clear only the remaps that are effective in insert mode only.
Here is a very usefully resource that helped me https://vim.fandom.com/wiki/Mapping_keys_in_Vim_-_Tutorial_(Part_1)
QUESTION
I am trying to compare different regression stategies for a forecasting problem:
- Using algorithms that support multiple input output regression by default (i.e Linear Regression, Trees etc..).
- Using algorithms a wrapper to do multiple input output regression (i.e SVR, XGboost)
- Using the chained regressor to exploit correlations between my targets (as my forecast at t+1 is auto-correlated with the target at t+2).
The documentation of scikit for the multiple input output wrappers is actually not that good but it is mentioned that:
https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html
...ANSWER
Answered 2021-Dec-15 at 09:19Dear colleagues it seems that this was due to a problem in XGB.Regressor in any case the right way of creating parameters for the MultiOutput Regressor within a pipeline it would be:
QUESTION
Found a Solution, left it as an answer to this question down below :)
Info about the project: Classification task with 2 classes.
I am trying to get the output of the fully connected layer of my model for each image I put into the model during runtime. I plan to use them after the model is done training or testing all images to visualize with UMAP.
The model:
...ANSWER
Answered 2021-Dec-06 at 15:00x_hat
is this vector and is [batch_size, 2048]
. So just modify your training step to also return x_hat
.
QUESTION
I have the following table:
...ANSWER
Answered 2021-Nov-29 at 16:13You never define fill=
as an aesthetic; use guides(color=...)
instead.
Note: with this sample data, I needed to add another color to the scale_color_manual
; it shouldn't be necessary with your real data. The only change I'm adding to your code is one argument to guides
.
QUESTION
So let's say I have an array, 1 2 1 2 3 4 2 1
and I want to store all the (arr[i], arr[i-1)
such that arr[i] != arr[i-1]
as a pair in unordered_map for counting these pairs.
For e.g.
ANSWER
Answered 2021-Nov-26 at 17:29You can't just use unordered_map
with a pair
because there is no default hash implemented.
You can however use map
which should work fine for your purpose because pair
does implement <
.
See Why can't I compile an unordered_map with a pair as key? when you really want unordered_map
.
You can construct pair
with curly braces like this
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install umap
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page