I have two networkx DiGraph, both have the same nodes that are of two different type: One is saved as a positive integer and is called address (for example in a graph i have address 1,2,3,4) The other one is a negative one, called transaction ( for example -1,-2) Now I have a real graph ..

#### Category : similarity

In python: I have a dictionary of 1000 products like this: products={p1:"apples",p2:"oranges",…,p1000:"bananas"} I now have 20.000 old shopping orders (dictionary) that look like this: orders={ "order_1":{"p1":100,"p7":30,…,"p560":126}, "order_2":{"p6":1300,"p7":51,…,"p423":3000}, …, "order_20000":{"p1":700,"p4":5,…,"p942":178} } Each order has different number of unique products (100-200 products) For each order I have the time it took to gather all products: time={"order1":15days,"order2":34days",…,"order20000":7days} When ..

I am doing a project related to comparing the similarity of 2 shapes, I have drawn contours for 2 images but can’t find any perfect method to compare the similarity between the two contours? (And the shapes in the images are different.) I am a newbie, can anyone help me? Thank you so much! Source: ..

Im trying to compute the time it takes for an algorithm to compute the similarity between large documents as lists containing bag of words (with their frequencies), but I’m struggling to figure out how I should slice the documents: for i in range(start stop step): A = my_doc[:i] B = my_doc[i:2*i] Source: Python..

I have a matrix in python that looking like this: [[-3,-1,2],[-1,8,1],[2,1,3]] I want to find it’s similar matrix. I tried to do some research but i couldn’t find abything. I know that A and B considered similar if exists inverted matrix M so B = (M^-1) * A * M. Source: Python..

I have a selection of the brown.words() and Wish to obtain a computation of all pair similarities from a collection of documents. Using jaccard and Cos-sim. The code I have right now is : my attempt my error : error Source: Python..

As part of a past interview task, I’m working with a sports streaming dataset that looks like this: pd.DataFrame({‘away_contestant_country’: {0: ‘Japan’, 1: ‘Canada’}, ‘competition_name’: {0: ‘NPB’, 1: ‘FIBA AmeriCup (W)’}, ‘customer_country’: {0: ‘Japan’, 1: ‘Canada’}, ‘device_category’: {0: ‘Web’, 1: ‘Unknown’}, ‘home_contestant_country’: {0: ‘Japan’, 1: ‘Brazil’}, ‘live_or_on_demand’: {0: ‘Live’, 1: ‘Live’}, ‘match_date’: {0: ‘2021-06-11T08:45:00.000Z’, 1: ‘2021-06-13T19:10:00.000Z’}, ..

Trying to find 3 closest matches with a given string like "_e_ul" and the correct match from my list would be Mehul, but difflib.get_close_matches seems to be getting very weird matches that don’t match my word at all and look very random. Also if this helps at all, I have a list with all the ..

background: I have a machine learning model in which given an object returns an embedding vector with dimension d, the model is trained in a way such that the semantic similarity of two embedding vectors is very close. Now, the verification process is relatively simple, I can take something like the cosine similarity of the ..

I have the following function, dataframe and vector, why I am getting an error? import pandas as pd import numpy as np def vanilla_vec_similarity(x, y): x.drop(‘request_id’, axis=1, inplace=True).values.flatten().tolist() y.drop(‘request_id’, axis=1, inplace=True).values.flatten().tolist() res = (np.array(x) == np.array(y)).astype(int) return res.mean() test_df = pd.DataFrame({‘request_id’: [55, 42, 13], ‘a’: [‘x’,’y’,’z’], ‘b’:[1,2,3], ‘c’: [1.0, -1.8, 19.113]}) test_vec = pd.DataFrame([[123,’x’,1.1, -1.8]], ..

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