piyush01123

#include <bits/stdc++.h>
using namespace std;

int randGen (int i) {srand(time(0)); return rand()%i;}

int partition(int A[], int lo, int hi)
{
    int pivot = A[hi];
    int i = lo-1;
    for (int j=lo; j<=hi; j++)

piyush01123

#include <bits/stdc++.h>
using namespace std;

int randGen (int i) {srand(time(0)); return rand()%i;}
int ctr = 0;

int partition(int A[], int lo, int hi)
{
	int pivot = A[hi];
	int i = lo-1;

piyush01123

// ==UserScript==
// @name         Auto Wayback
// @namespace    http://tampermonkey.net/
// @version      3.1.1
// @description  Load latest snapshot from Wayback Machine
// @author       Piyush Singh
// @match        *://*/*
// @icon         data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==
// @grant        unsafeWindow
// ==/UserScript==

piyush01123

// ==UserScript==
// @name         Generate Strong Password
// @namespace    http://tampermonkey.net/
// @version      3.1.1
// @description  Strong password generator of size 12
// @author       Piyush Singh
// @match        *://*/*
// @icon         data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==
// @grant        unsafeWindow
// @grant        GM_setClipboard

piyush01123

Command Line Productivity Tools

In course of my everyday work, sometimes I need a tool which does not exist. So I build them myself and later release them. Here are 3 command line tools I have built that each serve some specific purpose:

Tool 1: Flask Image Gallery

Generates image gallery from your server. Very useful during ML experimentation. Link to repository.

piyush01123

from flask import Flask, request
from datetime import datetime
import json,os

app = Flask(__name__)
@app.route('/', methods=["GET","POST"])
def index():
	if request.method=="POST":
		ip_data = request.get_json()
		print(ip_data)

piyush01123

float fisr(float n){
	long x = 0x5f3759df - (*(long *)&n >> 1); //magic number = 1.5*2^23*(127-mu); mu represents log(1+x)=x+mu; mu=0.0450465
	float y = *(float *)&x;  // Solution from bit manipulation
	y = 1.5F*y - 0.5F*n*y*y*y; // Newton improvement 1
        y = 1.5F*y - 0.5F*n*y*y*y; // Newton improvement 2
	return y;
}

piyush01123

import numpy as np

def viterbi(mood_sequence, priors, transmission_probs, emission_probs):
    n = len(mood_sequence)
    weather_matrix = np.zeros((n, 2))
    history = [(None,None)]
    for i, mood in enumerate(mood_sequence):
        if i==0:
            weather_matrix[i] = priors['s']*emission_probs['s'+mood], priors['r']*emission_probs['r'+mood]
        else:

piyush01123

from sklearn.feature_extraction.text import TfidfTransformer
import numpy as np

counts = [[3, 0, 1],
          [2, 0, 0],
          [3, 0, 0],
          [4, 0, 0],
          [3, 2, 0],
          [3, 0, 2]]

piyush01123

import numpy as np, matplotlib.pyplot as plt

def KMeans(X, K, num_steps=100):
    L = len(X)
    cluster_assignments=np.concatenate([np.full((L//K,), k) for k in range(K-1)]+[np.full((L-(K-1)*(L//K),), K-1)])
    centroids = np.empty((K,*X.shape[1:]))
    for step in range(num_steps):
        # E step: centroid calculation
        for k in range(K):
            centroids[k] = X[cluster_assignments==k].mean(axis=0)