Blanced Brackets

	//problem :
	//https://www.hackerrank.com/challenges/balanced-brackets/problem
	//Solution
	#include <iostream.h>
	#include <string.h>
	using namespace std;
	// Complete the isBalanced function below.
	string isBalanced(string s) {
	stack <char> st;
	char x;
	if (s[0]==')'||s[0]=='}'||s[0]==']')
	{
	return "NO";
	}
	else
	{
	for(int i=0;i<s.length();i++)
	{
	if(s[i]=='('||s[i]=='{'||s[i]=='[')
	{
	st.push(s[i]);
	continue;
	}
	switch(s[i])
	{
	case ')':
	x=st.top();
	st.pop();
	if (x=='{' || x=='['){return "NO";}
	break;
	case '}':
	x=st.top();
	st.pop();
	if(x=='('||x=='['){return "NO";}
	break;
	case ']':
	x=st.top();
	st.pop();
	if(x=='('||x=='{'){return "NO";}
	break;
	}
	}
	if(st.empty()){return "YES";}
	else {return "NO";}
	}
	}

view raw BalancedBrackets.cpp hosted with ❤ by GitHub

Neural Network Tutorial

this tutorial about classifiation in machine learning -using neural network- about patient medical record data for Pima Indians and whether they had an onset of diabetes within five years

	# the tutorial about patient medical record data for Pima Indians and whether they had an onset of diabetes within five years
	# so we have a classification problem
	from keras.models import Sequential
	from keras.layers import Dense
	import numpy
	# fix random seed for reproducibility
	numpy.random.seed(7)
	# load pima indians dataset
	dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
	# split into input (X) and output (Y) variables
	X = dataset[:,0:8]
	Y = dataset[:,8]
	# create model
	model = Sequential()
	model.add(Dense(12, input_dim=8, activation='relu'))
	model.add(Dense(8, activation='relu'))
	model.add(Dense(1, activation='sigmoid'))
	# Compile model
	model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
	# Fit the model
	model.fit(X, Y, epochs=150, batch_size=10)
	# evaluate the model
	scores = model.evaluate(X, Y)
	print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

view raw nn.py hosted with ❤ by GitHub

Mini-Max Sum

Problem :
https://www.hackerrank.com/challenges/mini-max-sum/problem

Solution:

	import math
	import os
	import random
	import re
	import sys
	# Complete the miniMaxSum function below.
	def miniMaxSum(arr):
	sum_list=[0,0,0,0,0]
	for i in range(5):
	for j in range(i,i+4):
	sum_list[i]+=arr[j%5]
	print(min(sum_list),max(sum_list))
	if __name__ == '__main__':
	arr = list(map(int, input().rstrip().split()))
	miniMaxSum(arr)

view raw mini_max sum.py hosted with ❤ by GitHub

Side Diagonal

Statement

Given an integer n, create a two-dimensional array of size (n×n) and populate it as follows, with spaces between each character:

• The positions on the minor diagonal (from the upper right to the lower left corner) receive 1 .
• The positions above this diagonal recieve 0 .
• The positions below the diagonal receive 2 .

Print the elements of the resulting array

Solution

	#Problem : https://snakify.org/lessons/two_dimensional_lists_arrays/problems/secondary_diagonal/
	n = int(input())
	a=[[0 for x in range(n)]for y in range(n)]
	for i in range(n):
	for j in range(n):
	a[i][n-i-1]=1
	for y in range (n-i,n):
	a[i][y]=2
	print(a[i][j],end=" ")
	print('\n')

view raw Side diagonal.py hosted with ❤ by GitHub

 

The diagonal parallel to the main

Statement

Given an integer n$n$, produce a two-dimensional array of size (n×n)and complete it according to the following rules, and print with a single space between characters:

• On the main diagonal write 0 .
• On the diagonals adjacent to the main, write 1 .
• On the next adjacent diagonals write 2 and so forth.

Print the elements of the resulting array.

sample 

n=7

0 1 2 3 4 5 6 1 0 1 2 3 4 5 2 1 0 1 2 3 4 3 2 1 0 1 2 3 4 3 2 1 0 1 2 5 4 3 2 1 0 1 6 5 4 3 2 1 0

	n= int(input())
	b=[[abs(x-y) for x in range(n)]for y in range(n)]
	for i in range(n):
	for j in range(n):
	print(b[i][j],end=" ")
	print('\n')

view raw diagonal.py hosted with ❤ by GitHub

Chess Board

Given two numbers n and m. Create a two-dimensional array of size (n×m)and populate it with the characters "." and "*" in a checkerboard pattern. The top left corner should have the character "." 



above figure shows the main chess board pattern, note that "." means white and "*" means black

	n,m = map(int,input().split())
	b=[['. ' for x in range(m)]for y in range(n)]
	for i in range(n):
	for j in range(m):
	if(i%2==0 and j%2!=0):
	b[i][j]='* '
	elif(i%2!=0 and j%2==0):
	b[i][j]='* '
	for i in range(n):
	for j in range(m):
	print(b[i][j],end="")
	print('\n')

view raw chess board.py hosted with ❤ by GitHub

Unique elements

Given a list of numbers, find and print the elements that appear in the list only once
example 
input: 
4 3 5 2 5 1 3 5
output:
1
2
4

	from collections import Counter
	a = [int(x) for x in input().split()]
	c=Counter(a)
	for key in c.keys():
	if c[key]==1:print(key)

view raw unique lelemnts.py hosted with ❤ by GitHub

Implementing Linear Regression Algorithm in Python

in this PDF created by me , a short and good introduction into one of the most popular machine learning algorithms it is called Linear regression
in this tutorial we deal with linear regression with single variable
check attached file

Linear Regression.PDF 

	import matplotlib.pyplot as plt
	import pandas as pnds
	import numpy as np
	# First step we may consider while implementing Machine Learning algorithm is to visualize our data set
	# reading data from csv file
	dataset=pnds.read_csv('TV.csv')
	# read_csv() return pandas data frame
	# Data Frame in pandas can be considered for simplicity a two dimensional array (matrix) composed from rows and columns
	# then we may print all data set items to check that every thing is ok
	# output will be in matrix from
	print(dataset.iloc[:,:])
	# next we will scatter plot money spent on TV advertising vs sales
	dataset.plot(kind='scatter',x='TV',y='sales')
	l1=pnds.Series.tolist(dataset)
	# Define Our main variables
	x=np.zeros((200,1))
	Y=np.zeros((200,1))
	X=np.zeros((200,2))
	i=0
	for item in l1:
	x[i,0]=item[0]
	Y[i,0]=item[1]
	i=i+1
	X=np.concatenate((np.ones((200,1)),x),axis=1)
	theta=np.linalg.lstsq(X,Y)[0] # solving the system of linear equation using least square method
	print(theta)
	plt.plot(x,np.dot(X,theta),color="red")
	plt.show()

view raw linear_reg.py hosted with ❤ by GitHub

Reverse words in a given string

Problem

https://practice.geeksforgeeks.org/problems/reverse-words-in-a-given-string/0

Solution

	#problem link
	# https://practice.geeksforgeeks.org/problems/reverse-words-in-a-given-string/0
	#code
	T=int(input())#Test cases
	for t in range(0,T):#looping through all test cases
	string=[x for x in input().split(".")]
	print(*string[::-1],sep='.')

view raw T1.py hosted with ❤ by GitHub

 

Key Pair
Problem :
 https://practice.geeksforgeeks.org/problems/key-pair/0
Solution

	#code
	T=int(input())#Test cases
	for t in range(0,T):#looping through all test cases
	N_X = [int(x) for x in input().split()]
	flag=0
	A=[int(y) for y in input().split()]#read elements separated by space
	for a in range(N_X[0]):
	if N_X[1]-A[a] in A:#if number complement found in A[] print yes
	print("Yes")
	flag=1
	break
	if(flag==0):
	print("No")

view raw KeyPair.py hosted with ❤ by GitHub