Fuzzy Logic Code In Python

[William Dupere]

WhatI am striving to complete is a program which reads in a file and will compare each sentence according to the original sentence. The sentence which is a perfect match to the original will receive a score of 1 and a sentence which is the total opposite will receive a 0. All other fuzzy sentences will receive a grade in between 1 and 0.

The package is built on top of difflib. Why not just use that, you ask? Apart from being a bit simpler, it has a number of different matching methods (like token order insensitivity, partial string matching) which make it more powerful in practice. The process.extract functions are especially useful: find the best matching strings and ratios from a set. From their readme:

The task is called Paraphrase Identification which is an active area of research in Natural Language Processing. I have linked several state of the art papers many of which you can find open source code on GitHub for.

Note that all the answered question assume that there is some string/surface similarity between the two sentences while in reality two sentences with little string similarity can be semantically similar.

Update in 2020There is this new model called BERT released by Google based on a deep learning framework called Tensorflow. There is also an implementation that many people find easier to use called Transformers. What these programs do, is that they accept two phrases or sentences, and they are able to be trained to say if these two phrases/sentences are the same or not. To train them, you need a number of sentences with labels 1 or 0 (if they have the same meaning or not). You train these models using your training data (already labelled data), and then you'll be able to use the trained model to make prediction for a new pair of phrases/sentences. You can find how to train (they call it fine-tune) these models on their corresponding github pages or in many other places such as this.

There are also already labelled training data available in English called MRPC (microsoft paraphrase identification corpus). Note that there multilingual or language-specific versions of BERT also exists so this model can be extended (e.g. trained) in other languages as well.

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In the realm of artificial intelligence, fuzzy logic stands out as a powerful method for handling uncertainty and imprecision. Leveraging this, I embarked on creating an interactive diagnostic tool to interpret respiratory symptoms using Python's skfuzzy library.

This project was developed within the Jupyter Notebook environment, a popular platform for data analysis and interactive computing. The core of our tool relies on the skfuzzy library, which allowed us to define membership functions for symptoms such as fever, headache, and cough. Each symptom was categorized into levels like "low", "medium", and "high", based on specific value ranges.

To enhance user interaction, we integrated the logic with an interactive UI using the ipywidgets library. This provides a seamless interface for users to input their symptoms and receive a suggested diagnosis.

In this article, I'll delve deep into the technicalities behind this tool, from setting up the environment to the step-by-step code implementation. For developers, AI enthusiasts, or anyone keen on exploring the intricacies of fuzzy logic in Python.

numpy (imported as np):Purpose: A fundamental package for scientific computing in Python. Key Features: Provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. skfuzzy:Purpose: A library for fuzzy logic and fuzzy systems in Python. Key Features: Offers functions to define, manipulate, and visualize fuzzy sets, membership functions, and fuzzy inference systems. control (from skfuzzy and imported as ctrl):Purpose: A submodule of skfuzzy specifically designed for fuzzy control systems. Key Features: Allows users to define antecedents and consequents, create rules, and simulate fuzzy control systems. matplotlib.pyplot (imported as plt):Purpose: A plotting library for creating static, animated, and interactive visualizations in Python. Key Features: Provides a MATLAB-like interface for making plots and charts, and is especially useful for visualizing data, results, and fuzzy membership functions.

- We've defined various potential diagnoses like 'infeccoes_virais_leves' (mild viral infections), 'resfriado_comum' (common cold), 'gripe' (flu), and so on. Each diagnosis has its own membership function to determine the likelihood of that particular diagnosis based on the input symptoms.

Using Python's ipywidgets and IPython.display, we've developed an interactive tool for health diagnosis simulations within a Jupyter notebook. Users can input symptoms like temperature, headache severity, and cough frequency through intuitive widgets. Upon pressing the "Simular Diagnstico" button, the tool computes a potential diagnosis based on the provided inputs. The diagnosis is then interpreted into textual descriptions of health conditions, which are displayed to the user. This tool offers a hands-on approach to understanding how symptoms can be mapped to potential health conditions using computational methods.

Throughout this article, we've delved into the intricacies of fuzzy logic and its application in health diagnosis simulations. By leveraging powerful Python libraries such as skfuzzy and ipywidgets, we've demonstrated how to construct an interactive and intuitive tool that maps symptoms to potential health conditions. This approach showcases the potential of computational methods in aiding decision-making processes in various domains, including healthcare. For those interested in exploring the code further and potentially adapting it for other use cases, the complete Jupyter notebook (.ipynb) is available on my GitHub repository. You can access it -Diagnosis-Simulation

I think it would be more helpful for us if you put all yours code and language which are you using. I suppose that's are you using Python. In this case try Python library scikit-fuzzy. Some examples at the link: -fuzzy/auto_examples/index.html

For instance, imagine a binary classification problem and one example where you are not sure whether it belongs to class 0 or 1. In that case, it may be detrimental to let your model learn a hard label (either 0 or 1), since you are not sure of its true value. So you may think to shift to consider fuzzy labels, which means you consider the example as belonging to different classes with given confidence (imagine this as degree of membership or fuzzy membership).

The code you provided refers to a commonly adopted family of functions for fuzzy membership called triangular membership and it works by assigning linearly increasing/decreasing membership values around a central point in the input. You can look at this picture for a more graphical explanation (see here for more details) :

The parameters determine the exact shape of the triangular function. Namely, the start of the rising path, the apex of its trajectory and the end point. Unfortunately, the choice of such parameters is application specific and there are no "magic" values that work always (at least to the best of my knowledge).

Then it will return TRUE or FALSE. However, the contains requires an exact match, but I need it to be a partial match (at least above 80% match) because the OCR does make mistakes with scanned documents.

Yes. The custom fuzzy logic function will return a positive result to the confidence level you set between 0 and 1 (0.8 in your case). I have used it exactly as shown in the forum post I linked and it worked a treat.

And UiPath integrates nicely with Python. You could write a simple program to take the string variable and the substring (with mistakes) to match. It would output the confidence % back to UiPath and you could work from there.

I need to realise a Python code program on Qiskit. It works on fuzzy logic. How should I realise the same using quantum processors using Qiskit? Is there any support on realising Python code for Fuzzy logic using quantum logic gates. Fuzzy logic uses IF THEN rules. How is this to be done?. Which logic gate would be appropriate for the circuit.

It is always difficult to force classical algorithms to quantum devices. And this is maybe a signal that quantum algorithms should be something different and new. Actually, quantum systems have unique properties like entanglement, that classical systems don't have.

i would like to make a line follower bot using fuzzy logic. I have seen the efll fuzzy library and tried to use it. As you can see im pretty much a noob right now with regards to using an arduino so if you can guide me in which direction I should proceed it would be much appreciated! Thanks!

Xander257:

i would like to make a line follower bot using fuzzy logic. I have seen the efll fuzzy library and tried to use it. As you can see im pretty much a noob right now with regards to using an arduino so if you can guide me in which direction I should proceed it would be much appreciated! Thanks!

I agree. I've been taking a look at the eFLL library since posting, out of interest since I'd never heard of it before.

Even the 'simple' example looks like quite a handful for a beginner with Arduino, if Xander257 has no real previous experience with C++.

The comments are in Portugese, too, making it even more difficult, (unless you happen to be Portugese).

The 'Brief' documentation, eFLL - A Fuzzy Library for Arduino and Embeded Systems, is just plain scary.

I'm a noob with regards to arduino since this is the first time I'm using it.

I have made a simple line follower using 8051 so i do have experience with line followers in general.

i do know that it is pretty complex and that's why i asked for guidance!

and yeah i have coded in C&C++ earlier so i can say that my programming and debugging skills are ok(not proficient in both lanuguages though!:) Sorry for not mentioning that in my original post!

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