In cristianmejia00/heatmaps3: Creates heatmpas form lists of keywords
knitr::opts_chunk$set(echo = TRUE)
Heatmap for topic models
We calculate the cosine similarity manually (not by using cosine functions)
To do so, first we have to normalize the vectors And then compute the "cross product" as defined by R... That is actualy the "DOT product" of two vectors!!!
Inputs
This code takes as inputs named lists of data frames. 1 list per axis
Each data frame containing keyword information of the clusters, where the first column is for the keyword(term) and the second, for the weigth (relevance) regardless is a probabiliy or tficf score.
No need to be normalized, as can normalize from here.
The keywords MUST BE IN DECREASING ORDER!!!
Outputs
It outputs:
The similarity matrix
The similirity scores from the highest
* An interactive visualization
cristianmejia00/heatmaps3 documentation built on May 24, 2019, 3:07 a.m.
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knitr::opts_chunk$set(echo = TRUE)
Heatmap for topic models
We calculate the cosine similarity manually (not by using cosine functions) To do so, first we have to normalize the vectors And then compute the "cross product" as defined by R... That is actualy the "DOT product" of two vectors!!!
Inputs
This code takes as inputs named lists of data frames. 1 list per axis Each data frame containing keyword information of the clusters, where the first column is for the keyword(term) and the second, for the weigth (relevance) regardless is a probabiliy or tficf score.
No need to be normalized, as can normalize from here. The keywords MUST BE IN DECREASING ORDER!!!
Outputs
It outputs: The similarity matrix The similirity scores from the highest * An interactive visualization
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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