rvndbalaji

/*
You are given a 2D matrix, a, of dimension MxN and a positive integer R. You have to rotate the matrix R times and print the resultant matrix. Rotation should be in anti-clockwise direction.
Rotation of a 4x5 matrix is represented by the following figure. Note that in one rotation, you have to shift elements by one step only (refer sample tests for more clarity).

Matrix-rotation
It is guaranteed that the minimum of M and N will be even.
Input
First line contains three space separated integers, M, N and R, where M is the number of rows, N is number of columns in matrix, and R is the number of times the matrix has to be rotated. 
Then M lines follow, where each line contains N space separated positive integers. These M lines represent the matrix.

rvndbalaji

#include <math.h>
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <assert.h>
#include <limits.h>
#include <stdbool.h>
int main()
{
    int a0; 

rvndbalaji

#include <stdio.h>

int main()

{

	int a = 10, b = 0, c = 10;

	char* str = "TFy!QJu ROo TNn(ROo)SLq SLq ULo+UHs UJq "

rvndbalaji

/* package codechef; // don't place package name! */

import java.util.Scanner;
import java.util.Stack;

/* Name of the class has to be "Main" only if the class is public. */
class Codechef
{
    public static void main (String[] args) throws java.lang.Exception
    {

rvndbalaji

library(psych)
roomtrain = read.table('datatraining.txt',header = TRUE,sep = ',',stringsAsFactors = FALSE)
roomtest1= read.table('datatest.txt',header = TRUE,sep = ',',stringsAsFactors = FALSE)
roomtest2= read.table('datatest2.txt',header = TRUE,sep = ',',stringsAsFactors = FALSE)

#Combine all three
room = rbind(roomtrain,roomtest1,roomtest2)

#Remove date and convert occupancy to factor
room = room[-1]

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#Feature Scaling & Mean Normalization
#This function is EXACTLY equivalent to the scale() function in R

//Remember to exclude the last column of the data, we should not normalize what we predict.
room_n = scale(room[1:5])

//Or all this function
normalize = function(x)
{
  return ((x - mean(x))/sd(x))

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#Install the package, if you havn't already done so.
install.packages('psych')

#Randomply sample the data into two parts with replacement and probability
ind = sample(2,nrow(room_n),replace = TRUE,prob = c(0.8,0.2))
train = room_n[ind==1,]
test = room_n[ind==2,]

#Predictors 
X = as.matrix(train[,-6])

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model =  glm(Occupancy~.,train,family= binomial(link = "logit")
pred = predict(model,test[,-6])

#All values with probability less than 0.7 are considered occupied.
pred[pred>=0.7] = 1
pred[pred<0.7] = 0
pred = factor(pred)
plot(pred)

#Calculate R-sqaured value

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sigmoid = function(z)
{
  return(1/(1 + exp(-z)))
}

cost = function(T)
{
  h = sigmoid(X%*%T)
  m = nrow(X)
  J = (1/m) * sum((-Y*log(h)) - (1-Y)*log(1-h))

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#Install the pakages if you haven't already done so
install.packages('nnet')

nnet = neuralnet(Occupancy ~ Temperature + Humidity + Light + CO2 + HumidityRatio,data=train,linear.output = FALSE,hidden = c(3,2))

#Predicted values
pred = compute(nnet,test[,-6])
pred = pred$net.result

#From the graph, for all predictions greater than 0.7 1 else 0