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HWR_Lokesh.R
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HWR_Lokesh.R
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library(caret)
library(rpart)
library(rpart.plot)
library(randomForest)
#loading the HWR data
image.data = read.csv("/Users/lokeshpalacharla/Library/Mobile Documents/com~apple~CloudDocs/NEU/Classes/Summer 2020/Predictive Analytics/Week 3/MNIST-data/mnist_train.csv", header = F)
#Viewing the HWR data
View(image.data)
#summary(image.data$V778)
#summary(model_j778$y)
#Structure of the HWR datset
str(image.data)
image.data$V1 = as.factor(image.data$V1)
image.data = data.frame(image.data)
dim(image.data)
# Summary of the label column
summary(image.data$V1)
image.data.label = image.data[ , 1]
# plot function to verify the label column data
rotate <- function(x) {
return(t(apply(x, 2, rev)))
}
plot_matrix <- function(vec) {
q <- matrix(vec, 28, 28, byrow = TRUE)
nq <- apply(q, 2, as.numeric)
image(rotate(nq), col = gray((0:255)/255))
}
plot_matrix(image.data[6, 2:785])
i = 0
#rm(model)
model_j = 0
#rm(fmla)
#looing with column names in the dataset
for (i in colnames(image.data[, -1])){ #colnames(image.data)
#model_j = rpart(image.data[, i] ~ image.data$V1, data=image.data, method = "anova" )
#hwr_model=assign(paste0("model_j",i),model_j,.GlobalEnv)
#model_j = rpart(i~V1, data = image.data, method = "anova")
#formula <- as.formula(paste(i,"V1", sep = "~"))
#model_j = rpart(formula, data = image.data, method = "anova")
formula = as.formula(paste(i,"V1", sep = "~"))
modl.name = paste("model", i, sep="_")
model_list = assign(modl.name, rpart(formula, data = image.data, method = "anova"))
}
class(model_V100)
i=0
#predicts=list()
for (i in colnames(image.data[, -1]) ){
pred_mod.name = paste("predict", i, sep = "_")
formula <- as.formula(paste(i,"V1", sep = "~"))
prediction_list =assign(pred_mod.name, predict(rpart(formula, data = image.data, method = "anova"), image.data))
}
summary(prediction_list)
View(predict_V100)
View(predict_V10)
#looping the model with respect to i values and creating new model in each iteration.
i=0
for( i in 2:785 ){
mod.name <- paste("model", i, sep="_")
model_list=assign(mod.name, rpart(image.data[, i] ~ image.data$V1, data = image.data, method="anova")) #, control=rpart.control(maxdepth=i)
}
summary(model_110)
i = 0
for (i in 2:785){
pixel[i]=predict(model, image.data$V1) #, newdata=data.frame(V1 = 5))
#pixel[i]=prediction(model_j[i], image.data$V1)
}
#for loop with a list
i=0
model = list()
for (i in 2:785){
model[i] = rpart( image.data[, i] ~ image.data$V1, image.data, method = "anova" )
}
str(model[100])
i = 0
for (i in 2:785){
pixel[i]=predict(model[i], newdata=data.frame(V1 = 5), type = "vector") #newdata=data.frame(V1 = 5)
#pixel[i]=prediction(model_j[i], image.data$V1)
}
df_model <- data.frame(matrix(unlist(model), nrow=length(model), byrow=T), stringsAsFactors=FALSE)
mat_model = matrix(unlist(model), ncol = length(model), byrow = T)
str(df_model)
i = 0
for (i in 2:784){
pixel[i]=predict(mat_model,data=image.data[i])
}
pred = predict(model_778,type = "vector")
summary(pred)
df= as.data.frame(image.data[,-1])
fitx=rpart(V2 ~ df$V3, data = image.data, method = "anova")
#control = rpart.control (minsplit = 30, cp = 0.001)))