distance

my topic is similarity and clustering of (a bunch of) text(s). In a nutshell: I want to cluster collected texts together and they should appear in meaningful clusters at the end. To do this, my approach up to now is as follows, my problem is in the clustering. The current software is written in php. 1) Similarity: I treat every document as a "bag-of-words" and convert words into vectors. I use filtering (only "real" words) tokenization (split sentences into words) stemming (reduce words to their base form; Porter's stemmer) pruning (cut of words with too high & low frequency) as methods for

Trying to use ElasticSearch to create a search that uses distance from a centerpoint to influence relevance. I don't want to simply sort on distance from a point, which I know is possible, because I want relevance based on the searched query to also affect results. I'd like to pass in a search string, say "coffee", and a lat/lon, say "38, -77", and get my results ordered by a combination of how related they are to "coffee" and how close they are to "38, -77". Thanks! You can use distance function in the script of the Custom Score Query to modify _score based on the distance from a centerpoint.

My goal is to compute the KL distance between the following text documents: 1)The boy is having a lad relationship 2)The boy is having a boy relationship 3)It is a lovely day in NY I first of all vectorised the documents in order to easily apply numpy 1)[1,1,1,1,1,1,1] 2)[1,2,1,1,1,2,1] 3)[1,1,1,1,1,1,1] I then applied the following code for computing KL distance between the texts: import numpy as np import math from math import log v=[[1,1,1,1,1,1,1],[1,2,1,1,1,2,1],[1,1,1,1,1,1,1]] c=v[0] def kl(p, q): p = np.asarray(p, dtype=np.float) q = np.asarray(q, dtype=np.float) return np.sum(np.where

The Problem Imagine I am stood in an airport. Given a geographic coordinate pair, how can one efficiently determine which airport I am stood in? Inputs A coordinate pair (x,y) representing the location I am stood at. A set of coordinate pairs [(a1,b1), (a2,b2)...] where each coordinate pair represents one airport. Desired Output A coordinate pair (a,b) from the set of airport coordinate pairs representing the closest airport to the point (x,y) . Inefficient Solution Here is my inefficient attempt at solving this problem. It is clearly linear in the length of the set of airports. shortest