cosine-similarity

问题 I am using python. Now I have a mongoDB database collection, in which all documents have such a format: {"_id":ObjectId("53590a43dc17421e9db46a31"), "latlng": {"type" : "Polygon", "coordinates":[[[....],[....],[....],[....],[.....]]]} "self":{"school":2,"home":3,"hospital":6} } In which the field "self" indicates the venue types in the Polygon and the number of corresponding venue types. different documents have different self field, such as {"KFC":1,"building":2,"home":6}, {"shopping mall":1

问题 I am working on my first major data science project. I am attempting to match names between a large list of data from one source, to a cleansed dictionary in another. I am using this string matching blog as a guide. I am attempting to use two different data sets. Unfortunately, I can't seem to get good results and I think I am not applying this appropriately. Code: import pandas as pd, numpy as np, re, sparse_dot_topn.sparse_dot_topn as ct from sklearn.feature_extraction.text import

问题 I have a CSV file which have content as belows and I want to calculate the cosine similarity from one the remaining ID in the CSV file. I have load it into a dataframe of pandas as follows: old_df['Vector']=old_df.apply(lambda row: np.array(np.matrix(row.Vector)).ravel(), axis = 1) l=[] for a in old_df['Vector']: l.append(a) A=np.array(l) similarities = cosine_similarity(A) The output looks fine. However, i do not know how to find which the GUID (or ID)similar to other GUID (or ID), and I

问题 I have a Pandas Dataframe and a user input , i would require to compare the user input with each of the rows in the dataframe and get the Ranked list of rows in the dataframe based on Cosine Similarties. Department Country Age Grade Score Math India Young A 97 Math India Young B 86 Math India Young D 68 Science India Young A 92 Science India Young B 81 Science India Young C 76 Social India Young B 88 Social India Young D 62 Social India Young C 72 User input : Country Age Grade Score India

问题 I noticed that both scipy and sklearn have a cosine similarity/cosine distance functions. I wanted to test the speed for each on pairs of vectors: setup1 = "import numpy as np; arrs1 = [np.random.rand(400) for _ in range(60)];arrs2 = [np.random.rand(400) for _ in range(60)]" setup2 = "import numpy as np; arrs1 = [np.random.rand(400) for _ in range(60)];arrs2 = [np.random.rand(400) for _ in range(60)]" import1 = "from sklearn.metrics.pairwise import cosine_similarity" stmt1 = "[float(cosine

问题 I am interested in calculating similarity between vectors, however this similarity has to be a number between 0 and 1. There are many questions concerning tf-idf and cosine similarity, all indicating that the value lies between 0 and 1. From Wikipedia: In the case of information retrieval, the cosine similarity of two documents will range from 0 to 1, since the term frequencies (using tf–idf weights) cannot be negative. The angle between two term frequency vectors cannot be greater than 90°.

问题 I need to find the cosine similarity between two frequency vectors in MATLAB. Example vectors: a = [2,3,4,4,6,1] b = [1,3,2,4,6,3] How do I measure the cosine similarity between these vectors in MATLAB? 回答1: Take a quick look at the mathematical definition of Cosine similarity. From the definition, you just need the dot product of the vectors divided by the product of the Euclidean norms of those vectors. % MATLAB 2018b a = [2,3,4,4,6,1]; b = [1,3,2,4,6,3]; cosSim = sum(a.*b)/sqrt(sum(a.^2)

问题 As a part of a recommender system that I am building, I want to implement a item-item recommendation based on cosine similarity. Ideally, I would like to compute the cosine similarity on 1 million items represented by a DenseVector of 2048 features in order to get the top-n most similar items to a given one. My problem is that the solutions I've come across perform poorly on my dataset. I've tried : Calculating the cosine similarity between all the rows of a dataframe in pyspark Using

问题 As a part of a recommender system that I am building, I want to implement a item-item recommendation based on cosine similarity. Ideally, I would like to compute the cosine similarity on 1 million items represented by a DenseVector of 2048 features in order to get the top-n most similar items to a given one. My problem is that the solutions I've come across perform poorly on my dataset. I've tried : Calculating the cosine similarity between all the rows of a dataframe in pyspark Using

问题 As a part of a recommender system that I am building, I want to implement a item-item recommendation based on cosine similarity. Ideally, I would like to compute the cosine similarity on 1 million items represented by a DenseVector of 2048 features in order to get the top-n most similar items to a given one. My problem is that the solutions I've come across perform poorly on my dataset. I've tried : Calculating the cosine similarity between all the rows of a dataframe in pyspark Using