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Ramnath Vaidyanathan ramnathv

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ramnathv / bm25.py
Created Sep 8, 2020 — forked from koreyou/bm25.py
Implementation of OKapi BM25 with sklearn's TfidfVectorizer
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""" Implementation of OKapi BM25 with sklearn's TfidfVectorizer
Distributed as CC-0 (https://creativecommons.org/publicdomain/zero/1.0/)
"""
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy import sparse
class BM25(object):
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ramnathv / tweet_reply.md
Created Jul 27, 2018 — forked from arunsrinivasan/tweet_reply.md
automatic indexing vs between() on integer ranges
View tweet_reply.md

Updated June 16 with latest devel

data.table's automatic indexing:

Generating some data first:

# R version 3.3.0
require(data.table) ## 1.9.7, commit 2433, github
require(dplyr)      ## devel, commit 3189, github
View Effective_Engineer.md

Effective Engineer - Notes

What's an Effective Engineer?

  • They are the people who get things done. Effective Engineers produce results.

Adopt the Right Mindsets

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ramnathv / index.js
Created Jul 5, 2016 — forked from jimthedev/index.js
requirebin sketch
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var mobx = require('mobx');
var _ = require('lodash');
/*
MOBX in VANILLA ES5
Notes are based on Matt Ruby's Open Source North Talk:
Practical React with MobX
https://www.youtube.com/watch?v=XGwuM_u7UeQ
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ramnathv / orig.png
Created Mar 23, 2016 — forked from hrbrmstr/orig.png
Supreme Annotations - moar splainin here: http://rud.is/b/2016/03/16/supreme-annotations/ - NOTE: this requires the github version of ggplot2
View reactD3rescources.md

React <-> D3 Resources

This is a incomplete list of resources on how to link react & d3. To my experience, there are as many approaches as there are (enter something numerous here). These approaches mostly differ as to 'who' has control over the dom and does the transitions etc. That distinction either requires the user to know more about react or d3, vice versa. Some of the approaches (like react-d3 or react-d3-components) include prebuilt charts, other just provide frameworks to place your charts in.

The following list tries to summarize some of the approaches, hopefully there will be some convergence to a (set of) standard(s), at one point.

This list is UNSORTED.

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ramnathv / README.md
Created Jan 28, 2016 — forked from mbostock/.block
Inline Labels
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This example shows how to implement Ann K. Emery’s technique of placings labels directly on top of a line in D3 4.0 Alpha.

To construct the multi-series line chart, the data is first transformed into separate arrays for each series. (The series names are automatically derived from the columns in the TSV file, thanks to a new dsv.parse feature.)

var series = data.columns.slice(1).map(function(key) {
  return data.map(function(d) {
    return {
      key: key,
      date: d.date,
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ramnathv / README.md
Created Jan 28, 2016 — forked from kerryrodden/.block
Sequences sunburst
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This example shows how it is possible to use a D3 sunburst visualization (partition layout) with data that describes sequences of events.

A good use case is to summarize navigation paths through a web site, as in the sample synthetic data file (visit_sequences.csv). The visualization makes it easy to understand visits that start directly on a product page (e.g. after landing there from a search engine), compared to visits where users arrive on the site's home page and navigate from there. Where a funnel lets you understand a single pre-selected path, this allows you to see all possible paths.

Features:

  • works with data that is in a CSV format (you don't need to pre-generate a hierarchical JSON file, unless your data file is very large)
  • interactive breadcrumb trail helps to emphasize the sequence, so that it is easy for a first-time user to understand what they are seeing
  • percentages are shown explicitly, to help overcome the distortion of the data that occurs wh
View 0-react-hello-world.md
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