Madhivarman

import java.io.*;
import java.util.Scanner;

class HashingTable{
	static String userchoice;
	static int capacity;
	static int array[];
	//constructor function called
	public  HashingTable(int capacity){
		this.capacity = nextPrime(capacity);

Madhivarman

//spiral matrix method is used
import java.io.*;
import java.util.Scanner;

public class BrainTeaser{
	static int[] array = new int[200];
	static int userchoice;

	//brainFunction called here

Madhivarman

class Node:

	#creation of node
	def __init__(self,data):

		self.data = data #assign data
		self.next = None #initialize null

class LinkedList:

Madhivarman

"""Sentence segmentation, means, to split a given paragraph of text into sentences, by identifying the sentence boundaries.
In many cases, a full stop is all that is required to identify the end of a sentence, but the task is not all that simple.
This is an open ended challenge to which there are no perfect solutions. Try to break up given paragraphs into text into 
individual sentences. Even if you don't manage to segment the text perfectly, the more sentences you identify and display 
correctly, the more you will score."""


import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

Madhivarman

import gym
import numpy as np

env = gym.make("Pong-v0")
observation = env.reset()

#hyperparameters
episode_number =  0
batch_size=10 #how many episodes to wait before moving the weights
gamma = 0.99 #discount factor for reward

Madhivarman

import random
import numpy as np

class Game:

	def __init__(self):
		self.reset()

	def reset(self):
		self.current_number = random.randrange(1,12)

Madhivarman

import tensorflow as tf 
import os

from tensorflow.python.keras.applications import ResNet50
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten, GlobalAveragePooling2D

from tensorflow.python.keras.applications.resnet50 import preprocess_input
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator

Madhivarman

import pandas as pd
import numpy as np

#create a dummy data
user_id = [x for x in range(10000)]
recency = np.random.randint(low=1, high=10, size=10000)
monetary = np.random.randint(low=1, high=10, size=10000)
frequency = np.random.randint(low=1, high=10, size=10000)

Madhivarman

from collections import defaultdict

class Graph():

	#initial declaration 
	def __init__(self):
		self.graph = defaultdict(list)

	#add edge between two vertices
	def addedge(self,src,dist):

Madhivarman

#train model to count number of 1's present in the string
import numpy as np
from random import shuffle
#import necessary libarary
import tensorflow as tf

training_data = ['{0:020b}'.format(i) for i in range(2**20)]
shuffle(training_data)
train_input = [map(int,i) for i in training_data]
ti = [] #list to store each tensor