venkatavamsy

/* Hash Table Implementations */
public class HashMap implements IMap {
	private ListNode[] htbl;
	private int size;

	public HashMap() {
		htbl = new ListNode[11];
		for (int i = 0; i < htbl.length; ++i)
			htbl[i] = new ListNode();
		size = 0;

venkatavamsy

import numpy as np
import cv2

cap = cv2.VideoCapture(0)
 
while(True):
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
    cv2.imshow('frame',gray)

venkatavamsy

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('images.jpg',cv2.IMREAD_GRAYSCALE)

plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')
plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
plt.plot([200,300,400],[100,200,300],'c', linewidth=5)
plt.show()

venkatavamsy

/**
 * The first commented line is your dabblet’s title
 */

background: #960;
min-height: 10%;

venkatavamsy

from pyelasticsearch import ElasticSearch
import pandas as pd
from time import time

root_path="<Root Path>"
raw_data_path=root_path+"data/"
csv_filename="predicted_data.csv"

t0=time()

venkatavamsy

Summary
Overall risk level:
High
Risk ratings:
High:	
1
Medium:	
2
Low:	
2

venkatavamsy

# Compiled source #
###################
*.com
*.class
*.dll
*.exe
*.o
*.so

# Packages #

venkatavamsy

#include <iostream>
#include <cstdio>
#include <string>
#include <vector>
#include <fstream>
#include <boost/lexical_cast.hpp>

using namespace std;
using namespace boost;

venkatavamsy

using namespace std;
using namespace boost;


enum class Color {red,green,blue};
enum class Size {large,medium,  small};

struct Product{

venkatavamsy

import json 
import requests

with open("saijson.json", "r") as file:
   json_data = json.loads(file)
   json_s_data = json_data['result']

   output_json = []
   tmp_json = []