similarity | Similarity is an optical as well as keyword based image | Computer Vision library

I would like to group keys in a dictionary based on their respective similarity. I want to look for similarity within different keys, and if they are similar enough, group them. Probably by using some sort of similarity score. I am thus specifically not interested in how they values within those dictionary match up (in the example below I kept them the same). I have been looking at similarity scores using sklearn cosine_similarity, but I could not find a way to apply this to keys in a dictionary. Anyone any clues on this?

I made a test dictionary to show what I mean. Some keys are very similar, and I would like to group those. How to group those is beyond the point now, but let's say I would like to add the numbers up.

As always, many thanks!

I have a need where I need to remove the semantic similarity string from a list of tuples.

Here is my input:

I am calculating the structure similarity profile between 2 moles using rdkit. When I am running the program in google colab (rdkit=2020.09.2 python=3.7) the program is working fine.

I am getting an error when I am running on my PC (rdkit=2021.03.2 python=3.8.5). The error is a bit strange. The dataframe contains 500 rows and the code is working only for the first 10 rows (0-9) and for later rows I am getting an error

I have two dataframes. df1['column'] has 70k unique text values. df2['column'] has 20 unique text values.

I want to find the closest synonym for all the 70k values by looking at the 20 values in df2['column']. and want an additional column in df1, which has the best synonym for that word.

I found a code where you could do semantic search and gives the top 5 synonyms with a score.

I'm doing a semantic search to find the closest synonym in two text columns, in two different dataframes.

The code is as below,

I'm trying to work out how to fix this ActiveMQ Artemis error.

Seems the occasional message is too big for SimpleString, and isn't sending, and it goes to the DLQ.

I am trying to use the DTW algorithm from the Similarity Measures library. However, I get hit with an error that states a 2-Dimensional Array is required. I am not sure I understand how to properly format the data, and the documentation is leaving me scratching my head.

https://github.com/cjekel/similarity_measures/blob/master/docs/similaritymeasures.html

According to the documentation the function takes two arguments (exp_data and num_data ) for the data set, which makes sense. What doesn't make sense to me is:

exp_data : array_like

Curve from your experimental data. exp_data is of (M, N) shape, where M is the number of data points, and N is the number of dimensions

This is the same for both the exp_data and num_data arguments.

So, for further clarification, let's say I am implementing the fastdtw library. It looks like this:

Scenario: Just created a simple book List app, which can accept some inputs and display it on the UI and has other functionalities such as the use of localStorage.

Just as I added more custom alerts (e.g. show alert - on adding a book, - if author name contain numbers etc.) The whole alert stuff has gone AWOL. (Previously defined alerts worked fine before)

Also none of the questions that showed similarity proved useful to me

Method which handles all the alerts in the page

I have a list of floats that I want to compare to other lists and get the similarity ratio in python :

The list that I want to compare: