Task_5_Creative_exploration.R

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### Tasks to accomplish ###
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# 1. Explore new models and data to improve your predictive model.
# 2. Evaluate your new predictions on both accuracy and efficiency. 

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### Questions to consider ###
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# 1. What are some alternative data sets you could consider using? 
# 2. What are ways in which the n-gram model may be inefficient?
# 3. What are the most commonly missed n-grams? Can you think of a reason why they would be missed and fix that? 
# 4. What are some other things that other people have tried to improve their model? 
# 5. Can you estimate how uncertain you are about the words you are predicting? 

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### Script ###
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setwd('H:/Machine_Learning/SwiftKey/')
setwd('C:\\Users\\Ivan.Liuyanfeng\\Desktop\\Data_Mining_Work_Space\\SwiftKey')
setwd('/Users/ivan/Work_directory/SwiftKey')
Sys.setenv(JAVA_HOME="C:\\Program Files\\Java\\jre7\\")

rm(list=ls(all=TRUE));gc(reset=TRUE);par(mfrow=c(1,1))
require(tm); require(SnowballC); require(stringr);require(RWeka); 
require(qdap); require(scales); require(gridExtra); require(data.table)

load('completed/ngrams_model.RData') 
source('Swiftkey-Natural-language/func/Task_5.5_Predict_func.R')

## make prediction
input <- "me about his"
system.time(result <- predictNgrams(input))
result
Quatrgrams_model[list('me', 'about', 'his')]

object.size(Quatrgrams_model)/1024/1024; dim(Quatrgrams_model);head(Quatrgrams_model)
Bigrams_model <- Bigrams_all[-which(Bigrams_all$freq==2),]
Trigrams_model <- Trigram_all[-which(Trigram_all$freq==2),]
Quatrgrams_model <- Quatrgrams_all[-which(Quatrgrams_all$freq==2),]
Unigrams_model <- Unigrams_all[1:6,]
save(Unigrams_model, Bigrams_model, Trigrams_model, Quatrgrams_model,
     file='completed/ngrams_model.RData')