cosine-similarity | 易学教程

Calculating the cosine similarity between all the rows of a dataframe in pyspark

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问题 I have a dataset containing workers with their demographic information like age gender,address etc and their work locations. I created an RDD from the dataset and converted it into a DataFrame. There are multiple entries for each ID. Hence, I created a DataFrame which contained only the ID of the worker and the various office locations' that he/she had worked. |----------|----------------| | **ID** **Office_Loc** | |----------|----------------| | 1 |Delhi, Mumbai, | | | Gandhinagar | |-------

Cosine similarity and tf-idf

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问题 I am confused by the following comment about TF-IDF and Cosine Similarity . I was reading up on both and then on wiki under Cosine Similarity I find this sentence "In case of of information retrieval, the cosine similarity of two documents will range from 0 to 1, since the term frequencies (tf-idf weights) cannot be negative. The angle between two term frequency vectors cannot be greater than 90." Now I'm wondering....aren't they 2 different things? Is tf-idf already inside the cosine

Cosine Similarity between 2 Number Lists

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I need to calculate the cosine similarity between two lists , let's say for example list 1 which is dataSetI and list 2 which is dataSetII . I cannot use anything such as numpy or a statistics module. I must use common modules (math, etc) (and the least modules as possible, at that, to reduce time spent). Let's say dataSetI is [3, 45, 7, 2] and dataSetII is [2, 54, 13, 15] . The length of the lists are always equal. Of course, the cosine similarity is between 0 and 1 , and for the sake of it, it will be rounded to the third or fourth decimal with format(round(cosine, 3)) . Thank you very much

What's the fastest way in Python to calculate cosine similarity given sparse matrix data?

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Given a sparse matrix listing, what's the best way to calculate the cosine similarity between each of the columns (or rows) in the matrix? I would rather not iterate n-choose-two times. Say the input matrix is: A= [0 1 0 0 1 0 0 1 1 1 1 1 0 1 0] The sparse representation is: A = 0, 1 0, 4 1, 2 1, 3 1, 4 2, 0 2, 1 2, 3 In Python, it's straightforward to work with the matrix-input format: import numpy as np from sklearn.metrics import pairwise_distances from scipy.spatial.distance import cosine A = np.array( [[0, 1, 0, 0, 1], [0, 0, 1, 1, 1], [1, 1, 0, 1, 0]]) dist_out = 1-pairwise_distances(A,

Can someone give an example of cosine similarity, in a very simple, graphical way?

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Cosine Similarity article on Wikipedia Can you show the vectors here (in a list or something) and then do the math, and let us see how it works? I'm a beginner. Here are two very short texts to compare: Julie loves me more than Linda loves me Jane likes me more than Julie loves me We want to know how similar these texts are, purely in terms of word counts (and ignoring word order). We begin by making a list of the words from both texts: me Julie loves Linda than more likes Jane Now we count the number of times each of these words appears in each text: me 2 2 Jane 0 1 Julie 1 1 Linda 1 0 likes

What's the fastest way in Python to calculate cosine similarity given sparse matrix data?

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问题 Given a sparse matrix listing, what\'s the best way to calculate the cosine similarity between each of the columns (or rows) in the matrix? I would rather not iterate n-choose-two times. Say the input matrix is: A= [0 1 0 0 1 0 0 1 1 1 1 1 0 1 0] The sparse representation is: A = 0, 1 0, 4 1, 2 1, 3 1, 4 2, 0 2, 1 2, 3 In Python, it\'s straightforward to work with the matrix-input format: import numpy as np from sklearn.metrics import pairwise_distances from scipy.spatial.distance import

Cosine Similarity between 2 Number Lists

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问题 I need to calculate the cosine similarity between two lists , let\'s say for example list 1 which is dataSetI and list 2 which is dataSetII . I cannot use anything such as numpy or a statistics module. I must use common modules (math, etc) (and the least modules as possible, at that, to reduce time spent). Let\'s say dataSetI is [3, 45, 7, 2] and dataSetII is [2, 54, 13, 15] . The length of the lists are always equal. Of course, the cosine similarity is between 0 and 1 , and for the sake of

Calculate cosine similarity given 2 sentence strings

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问题 From Python: tf-idf-cosine: to find document similarity , it is possible to calculate document similarity using tf-idf cosine. Without importing external libraries, are that any ways to calculate cosine similarity between 2 strings? s1 = \"This is a foo bar sentence .\" s2 = \"This sentence is similar to a foo bar sentence .\" s3 = \"What is this string ? Totally not related to the other two lines .\" cosine_sim(s1, s2) # Should give high cosine similarity cosine_sim(s1, s3) # Shouldn\'t give