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#Using Keras | |
model = keras_model_sequential() | |
model %>% | |
layer_dense(units = 3, input_shape = c(5), activation = 'relu') %>% | |
layer_dense(units = 2, activation = 'relu') %>% | |
layer_dense(units = 1, activation = 'sigmoid') | |
model %>% compile( | |
loss = 'binary_crossentropy', | |
optimizer = optimizer_adam(), |
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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 |
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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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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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#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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#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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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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/* 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 | |
{ |
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#include <stdio.h> | |
int main() | |
{ | |
int a = 10, b = 0, c = 10; | |
char* str = "TFy!QJu ROo TNn(ROo)SLq SLq ULo+UHs UJq " |
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#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; |
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