This particular utility is quite in demand nowadays due to the similarity computation requirements in many fields of Computer Science such as Machine Learning, A.I and web development domains, hence techniques to compute similarity between any given containers can be quite useful. Let’s discuss certain ways in which this can be done.
Method #1 : Using Naive Approach(
sum() + zip())
We can perform this particular task using the naive approach, using sum and zip functions we can formulate a utility function that can compute the similarity of both the strings.
The similarity between 2 strings is : 0.38461538461538464
Method #2 : Using
There’s an inbuilt method, that helps to perform this particular task and is recommended to achieve this particular task as it doesn’t require custom approach but uses built in constructs to perform task more efficiently.
The similarity between 2 strings is : 0.5555555555555556
- Python | Measure similarity between two sentences using cosine similarity
- Python | Percentage similarity of lists
- Measuring the Document Similarity in Python
- Python | Word Similarity using spaCy
- Python | Test list element similarity
- NLP | WuPalmer - WordNet Similarity
- PyQt5 QSpinBox - How to get the font metrics
- Python | Remove empty strings from list of strings
- ML | MultiLabel Ranking Metrics - Coverage Error
- NLP | Leacock Chordorow (LCH) and Path similarity for Synset
- Python | Tokenizing strings in list of strings
- Normalized Discounted Cumulative Gain - Multilabel Ranking Metrics | ML
- Multilabel Ranking Metrics-Label Ranking Average Precision | ML
- MultiLabel Ranking Metrics - Ranking Loss | ML
- C strings conversion to Python
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.