I have two tables of postal address information - the one is about 2 million records, the other roughly 40 million. They have quite bad quality, and also are not quite compatible with each other (different conventions in both sets, some fields cut off in an impractical way... - in other words, Real World Data). They may not be the largest ones around, but compared to the available hardware, they are non-trivial (I cannot simply spin up a lot of …

I'm a machine learning beginner and I tried to use the cosine similarity on fuzzy matching purpose. In the following example I want to compare 'data_dirty' with 'data_clean' : When I have to vectorize my data I do not really understand what is the purpose of fit_transform and WHY 'dirty_idf_matrix' has ONLY transform argument with SAME vectorizer than 'clean_idf_matrix' which has saved the value with fit if I understood well. Col_clean = 'fruits_normalized' Col_dirty = 'fruits' #read table data_dirty={f'{Col_dirty}':['I am …

I am trying to implement fuzzy logic system to classifiy dataset of 12 inputs and 1 ouput. I wanna understand as first taks to fuzzify inputs how Can we set intervals or we need to segment inputs first in order to fuzzify them below is an example of fuzzification but the chose of the intervals is not clear. Any suggestion or explication will be appreciated # Generate fuzzy membership functions qual_lo = fuzz.trimf(x_qual, [0, 0, 5]) qual_md = fuzz.trimf(x_qual, [0, …

I have recently begun studying different data science principles, and have had a particular interest as of late in fuzzy matching. For preface, I'd like to include smarter fuzzy searching in a proprietary language named "4D" in my workplace, so access to libraries is pretty much non existent. It's also worth noting that client side is single threaded currently, so taking advantage of multi-threaded matrix manipulations is out of the question. I began studying the levenshtein algorithm and got that …

I was trying to learn Fuzzy Cognitive Map by Active Hebbian Learning approach from here. What I have understand is that the model learns iteratively, at each step a new concept values enters and tune the weighs until the MSE score in output neurone is very small. I thaught that it is similar to stochastic gradient descent. But I don't see any convergence in output MSE value when a new input comes. import numpy as np import matplotlib.pyplot as plt …

Hi I'm trying to do a fuzzy c-means clustering on data that can be represented as line graphs(hourly electrical load profiles). I understand that I will cluster on each hour and to the next hour and so on. What I don't understand is how to relate these hourly clusters so that I can obtain the output that is composed of clustered line graphs. (Photos below).

I am in need of solving the below given requirement. Requirement: I have two datasets which has only one column called Name. That column contains a list of user names in both the datasets so from this dataset the requirement is when a user inputs a name from data 1 similar names from data 2 needs to be shown with their similarity score (Name matching score). So we need to solve this requirement and build an api using flask framework. …

I am using RapidFuzz for matching US Addresses from two separate datasets. I was able to get the results that I was hoping for using the below code: for address in EB_RATING_LIST: matches1.append(process.extractOne(address,CLAIMS_LIST, scorer = fuzz.ratio)) DAVE_EB_NO_DUPLICATES_ADDRESS['MATCHED_ADDRESS'] = matches1 But, I don't have a full confidence on the results I received. For example: 10 Washington Street has a 86% Match Ratio with: 102 Washington Street My Question is how can I proceed with Fuzzy matching at a more granular level? …

currently i'm doing classification model on FLVQ using IRIS dataset, but i was unable to get proper accuracy and it seems dependant to the initial vector which generated randomly. Mind helping me to crack where's wrong with the code? reference is here. def distance(self, clusterSblm) : n_kolom = self.n n = self.n nInput = self.nInput jarak = list() datatrain = np.array(self.x_train) dw = np.array(clusterSblm) jarak = list() for h in range(k) : for i in range(n) : data = list() …

The main challenge is the lack of data. Input values come from tests results of patients. A patient takes a breath test at an interval during a timespan. The result values can range from 0 to ~200, and can be plotted for diagnose by a doctor based on the curve shape. I am looking for an algorithm that takes the values at every interval and comes up with a single output value from 0 to 1 that indicates a fuzzy …

I have found a lot of information about fuzzy logic, but less information about fuzzywuzzy. I would like to know more about this, the function which determines the logic, if possible, and understand what partial_ratio in Python does. Any information will be well welcome.

I'm trying to get to a tool for Fuzzy Grouping as I do not have a reference column for matching the string. Is there any package on Python or R? I looked at a package called textpack but the results aren't good. found here: https://pypi.org/project/textpack/ I'd really appreciate if someone could suggest a tool or a package so I can go ahead and research.

Given a matrix of boolean values $\mathbf{X} \in \mathbb{B}^{M \times N} = \{\top, \bot\}^{M \times N}$, the binary/boolean matrix factorization (BMF) problem is to find $\mathbf{U} \in \mathbb{B}^{M \times K}$ and $\mathbf{V} \in \mathbb{B}^{K \times N}$ for some fixed $K$ that minimize $\sum_{i, j} d(x_{ij}, \hat{x}_{ij})$, where $\hat{x}_{ij} = \bigvee_k u_{ik} \land v_{kj}$ and $d$ is some boolean metric. BMF can be generalized to t-norm fuzzy logics (with involutive negation) by replacing $\mathbb{B}$ with the closed unit interval $[0, 1]$, …

Background Using Python, I need to score the existence of a quote, containing around 2-7 words, a longer text. The quote doesn't have to match the text precisely, but similar words should have the same order. For example, given the following long text: The most beautiful things in the world cannot be seen or touched, they are felt with the heart The following quotes should be scored high (say, above 80 / 100): The beautiful thing in our world World …

Is it just the between academics and practitioners in term usage? Or is theoretical difference of how we consider each sample: as belonging to multiple classes at once or to one fuzzy class? Or this distinction has some practical meaning of how we build model for classification?

I have to solve this question for my homework but I don't get how to formulate svm to FSVM. can someone please guide me? What is your idea to have a model of SVM classifier in which instances can belong to both classes with associated membership values? Model it in both primal and dual problem. Model an unsupervised version of SVM and solve it!

I have a dataset in which each feature is either 0 or 1 (like BBOW). I want to cluster the data such that one point can belong to more than one cluster(soft assignment). I searched about this and I found that fuzzy k-modes can be applied for this problem. Since I am new to ML coding, Is there any implementation available online for fuzzy k-modes or any other similar algorithm?

I am trying to design an FRBS using Matlab fuzzy tool box. The fuzzy system will be used to predict player's type based on the inputs and a set of rules defined by experts. I have 6 inputs and 4 outputs (types of players). The given rules do not concern all inputs. Specific inputs are used for each player type. Is it imperative to include all inputs and outputs in a rule? Also is there a min/max of rules that …

Problem Description I have several tables that are related but do not share any unique key. I've come across this problem several times with customer data in separate source systems that needs to be compared together. Lets say my data is multiple tables, Table A through Z. There may be columns where I'm 100% certain on a match. For example table A and B have the column tax ID which is a certain match joining A to B. Both A …

In many image processing papers, I've seen that they used fuzzy logic for segmentation I wonder how fuzzification impact the result that made Fuzzy-C-Means better than ordinary K-Means. PS. If possible could you provide me the sample data sets for case study