calculate-cosine-similarity | calculate cosine similarity with two array | Topic Modeling library

by kretawiweka JavaScript Version: Current License: No License

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calculate-cosine-similarity is a JavaScript library typically used in Artificial Intelligence, Topic Modeling, Example Codes applications. calculate-cosine-similarity has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Calculate Cosine Similarity is a package for calculate similarity between two arrays. This project from my undergraduate thesis’project. When i develope hoax analyze system i need package for calculate similarity between two arrays and i did not find it. So i create package for calculate similarity two arrays with cosine similarity algorithm.

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Community Discussions

Trending Discussions on calculate-cosine-similarity

unexpected division by zero error when dividing by the product of two arrays in python

How to go from a tsv with feature list strings to a csr matrix in python?

How to create a distributed sparse matrix in Spark from DataFrame in Scala

Convert org.apache.spark.mllib.linalg.Matrix to spark dataframe in Scala

QUESTION

unexpected division by zero error when dividing by the product of two arrays in python

Asked 2021-Apr-22 at 13:03

I suspect this is something very fundamental I don't know or understand about this code; my only excuse is that I am a complete beginner in python.

I am trying some of the cosine similarity matrix calculations from this post:

What's the fastest way in Python to calculate cosine similarity given sparse matrix data?

One of them requires the calculation of the reciprocal of the diagonal of the initial matrix product.
Say that he initial matrix is m, each row of which represents an 'object', whose 'coordinates' are in the columns of the matrix. So you want to calculate cosine similarities between rows.
Then, to use the matrix product method, you do something like mp = numpy.dot(m, m.T).

Now, if there are no rows with only 0's in m, the diagonal of mp can never have any zero values, as each of its elements is the sum of the squared elements of the corresponding row of m.
The m I am using in my calculations has indeed no rows with all 0's.
And indeed, when I do:

...

ANSWER

Answered 2021-Apr-22 at 13:03

I think the problem is dtype

uint8 : Unsigned integer (0 to 255)

Source https://stackoverflow.com/questions/67213360

QUESTION

How to go from a tsv with feature list strings to a csr matrix in python?

Asked 2021-Apr-19 at 15:21

I have been working with some R packages that calculate (cosine) (sparse) similarity matrices from sparse binary matrices, e.g. proxyC.

As I am now starting (and learning) to use python as well, and I was told it might even be faster, I would like to try and run the same calculations there.

I found this interesting post:

What's the fastest way in Python to calculate cosine similarity given sparse matrix data?

which describes a few methods.

I did try some of them out after writing out a small test matrix myself by hand.
Now I would like to try on 'real' data.
And that's where I encounter a problem I currently cannot solve.

My data come in tsv files that associate objects (ID's) to comma-separated lists of features (FP's). E.g.:

...

ANSWER

Answered 2021-Apr-19 at 15:21

import pandas as pd
df = pd.DataFrame({'ID':[1,2,3], 'FP':["A,B,C","A,D","C,D,F"]})

>>> df
   ID     FP
0   1  A,B,C
1   2    A,D
2   3  C,D,F

Source https://stackoverflow.com/questions/67158157

QUESTION

How to create a distributed sparse matrix in Spark from DataFrame in Scala

Asked 2020-Mar-01 at 11:41

Question

Please help finding the ways to create a distributed matrix from the (user, feature, value) records in a DataFrame where features and their values are stored in a column.

Excerpts of the data is below but there are large number of users and features, and no all features are tested for users. Hence lots of feature values are null and to be imputed to 0.

For instance, a blood test may have sugar level, cholesterol level, etc as features. If those levels are not acceptable, then 1 is set as the value. But not all the features will be tested for the users (or patients).

...

ANSWER

Answered 2019-Nov-20 at 15:26

Maybe you could transform each row into json representation, e.g:

Source https://stackoverflow.com/questions/58957444

QUESTION

Convert org.apache.spark.mllib.linalg.Matrix to spark dataframe in Scala

Asked 2018-Jun-27 at 13:02

I have an input dataframe input_df as:

...

ANSWER

Answered 2018-Jun-27 at 09:56

To convert the Matrix to a dataframe as specified, do the following. It first converts the matrix to a dataframe containing a single column with an array. Then foldLeft is used to break the array into separate columns.

Source https://stackoverflow.com/questions/51046018

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