cosine_similarity | : pencil : small ruby script to calculate cosine similarity | Learning library

by levthedev Ruby Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | cosine_similarity Summary

cosine_similarity is a Ruby library typically used in Tutorial, Learning, Example Codes applications. cosine_similarity has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

small ruby script to calculate cosine similarity of words/phrases, used for dictionary autosuggestions. adds a method called compare to String that calculates the cosine similarity of the the string it was called on and the argument passed to compare.

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cosine_similarity has a low active ecosystem.

It has 2 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

cosine_similarity has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cosine_similarity is current.

Quality

cosine_similarity has 0 bugs and 0 code smells.

Security

cosine_similarity has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cosine_similarity code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cosine_similarity does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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cosine_similarity releases are not available. You will need to build from source code and install.

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cosine_similarity Key Features

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cosine_similarity Examples and Code Snippets

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Community Discussions

Trending Discussions on cosine_similarity

efficiently calculate cosine similarity between vector shape (768,) and matrix (n, 768)

How to store Cosine Similarity values individually?

How to get average pairwise cosine similarity per group in Pandas

Cosine Simiarlity scores for each array combination in a list of arrays Python

Pipreqs: SyntaxError: invalid non-printable character U+FEFF

Efficient Pairwise jaccard score with two dataframes

Keras / Tensorflow incompatible shape

Print texts that have cosine similarity score less than 0.90

How to calculate the cosine similarity of two string list by sklearn?

Doc2Vec results not as expected

QUESTION

efficiently calculate cosine similarity between vector shape (768,) and matrix (n, 768)

Asked 2022-Apr-15 at 14:25

i have following target embeddings vector u shape of (768,) and target embeddings matrix v shape of (23, 768). I need to calculate cosine similarity between target vector and matrix. I know how to do it in cycle:

...

ANSWER

Answered 2022-Apr-15 at 14:25

import numpy as np

np.random.seed(1)
u = np.random.random(768)
v = np.random.random((23,768))
w = np.array([u.dot(v[i])/(np.linalg.norm(u)*np.linalg.norm(v[i])) for i in range(23)])
print(w)

Source https://stackoverflow.com/questions/71884847

QUESTION

How to store Cosine Similarity values individually?

Asked 2022-Apr-14 at 20:10

I know this is a very basic question, but please forgive me. I have a python script which is calculating cosine similarity of sentences. The result the script is returning is like this: [[0.72894156 0.96235985 0.61194754]]. I want to store these three values into an array or list individually, so I can find the minimum and maximum values. When I store them in an array, it stores them altogether in a single value. Here is the script:

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ANSWER

Answered 2022-Apr-14 at 17:43

This may help. I came across something similar and treated the output like an array. To get the specific score based on the texts I compared I did the following:

Source https://stackoverflow.com/questions/71875078

QUESTION

How to get average pairwise cosine similarity per group in Pandas

Asked 2022-Mar-29 at 20:51

I have a sample dataframe as below

...

ANSWER

Answered 2022-Mar-29 at 18:47

Remove the .vocab here in model_glove.vocab, this is not supported in the current version of gensim any more: Edit: also needs split() to iterate over words and not characters here.

Source https://stackoverflow.com/questions/71666450

QUESTION

Cosine Simiarlity scores for each array combination in a list of arrays Python

Asked 2022-Mar-26 at 11:41

I have list of arrays and I want to calculate the cosine similarity for each combination of arrays in my list of arrays.

My full list comprises 20 arrays with 3 x 25000. A small selection below

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ANSWER

Answered 2022-Mar-26 at 11:41

If I understand correctly, what you are trying to do is to get he cosine distance when using each matrix as an 1Xn dimensional vector. The easiest thing in my opinion will be to vectorially implement the cosine similarity with numpy functions. As a reminder, given two 1D vectors x and y, the cosine similarity is given by:

Source https://stackoverflow.com/questions/71627087

QUESTION

Pipreqs: SyntaxError: invalid non-printable character U+FEFF

Asked 2022-Mar-22 at 01:33

When I try to run pipreqs /path/to/project it comes back with

...

ANSWER

Answered 2022-Mar-21 at 23:52

Are you on Windows? Your file contains a Unicode byte-order mark. Some services don't like that. If you remove the BOM, it should work.

Source https://stackoverflow.com/questions/71565071

QUESTION

Efficient Pairwise jaccard score with two dataframes

Asked 2022-Mar-21 at 11:08

I am calculating the jaccard score of two vectors to create a user-item matrix. The vectors are stored in separate dataframes.

user dataframe is 166 x 1083, it looks like this. Each row contains the vectors of the users

index col1 col2 ... col1083 0 1 1 1 1 ... ... ... ... ... 165 1 0 1 0

item dataframe is 1083 x 1083, it looks like this. Each row contains the vectors of the items

index col1 col2 ... col1083 0 1 1 1 1 1 1 0 1 0 ... ... ... ... ... 1082 1 1 1 0

I tried to calculate the jaccard score for each user vector against each item vector using list comprehensions to save the result as a list of lists to be able to store the output in a dataframe.

...

ANSWER

Answered 2022-Mar-21 at 11:08

Referring to the comment above, there is already an existing library that can efficiently compute the jaccard for two vectors. The pairwise distances method from the sklearn library can be used.

Source https://stackoverflow.com/questions/71554288

QUESTION

Keras / Tensorflow incompatible shape

Asked 2022-Mar-15 at 08:38

I'm pretty new to tensorflow / keras and I can't find a fix to this problem. I have a training data set of ~4000 20-dimensional vectors that each describe a document. I also have those same document-vectors at a later state. I want to predict how the document-vector will be at the end from the initial state. I compared the document vectors at state 0 with their final state using cosine similarity and got about .5. The goal is to improve that with a simple model. Currently i am doing:

...

ANSWER

Answered 2021-Dec-03 at 10:18

Try np.expand_dims to add the batch dimension to your array:

Source https://stackoverflow.com/questions/70212594

QUESTION

Print texts that have cosine similarity score less than 0.90

Asked 2022-Feb-22 at 15:38

I want to create deduplication process on my database. I want to measure cosine similarity scores with Pythons Sklearn lib. between new texts and texts that are already in the database.

I want to add only documents that have cosine similarity score less than 0.90. This is my code:

...

ANSWER

Answered 2022-Feb-22 at 12:41

My suggestion would be as follows. You only add those texts with a score less than (or equal) 0.9.

Source https://stackoverflow.com/questions/71221256

QUESTION

How to calculate the cosine similarity of two string list by sklearn?

Asked 2022-Feb-17 at 05:41

I have two lists with string like that,

...

ANSWER

Answered 2022-Feb-17 at 05:41

It seems it needs

word-vectors,
two dimentional data (list with many word-vectors)

Source https://stackoverflow.com/questions/71151624

QUESTION

Doc2Vec results not as expected

Asked 2022-Feb-11 at 19:38

I'm evaluating Doc2Vec for a recommender API. I wasn't able to find a decent pre-trained model, so I trained a model on the corpus, which is about 8,000 small documents.

...

ANSWER

Answered 2022-Feb-11 at 18:11

Without seeing your training code, there could easily be errors in text prep & training. Many online code examples are bonkers wrong in their Doc2Vec training technique!

Note that min_count=1 is essentially always a bad idea with this sort of algorithm: any example suggesting that was likely from a misguided author.

Is a mere .split() also the only tokenization applied for training? (The inference list-of-tokens should be prepped the same as the training lists-of-tokens.)

How was "not very good" and "oddly even worse" evaluated? For example, did the results seem arbitrary, or in-the-right-direction-but-just-weak?

"8,000 small documents" is a bit on the thin side for a training corpus, but it somewhat depends on "how small" – a few words, a sentence, a few sentences? Moving to smaller vectors, or more training epochs, can sometimes make the best of a smallish training set - but this sort of algorithm works best with lots of data, such that dense 100d-or-more vectors can be trained.

Source https://stackoverflow.com/questions/71083740

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install cosine_similarity

You can download it from GitHub.
On a UNIX-like operating system, using your system’s package manager is easiest. However, the packaged Ruby version may not be the newest one. There is also an installer for Windows. Managers help you to switch between multiple Ruby versions on your system. Installers can be used to install a specific or multiple Ruby versions. Please refer ruby-lang.org for more information.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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