NOTE: I RECOMMEND YOU TO DO THIS ASSIGNMENT ON VISUAL STUDIO
SINCE I HAVE NEVER TAKING C++, SO I MAY HAVE SOME DIFFICULTY FOR THIS ASSIGNMENT. THEREFORE, I HOPE YOU CAN DO THIS ON MICROSOFT VISUAL STUDIO.
BELOW IS ONE OF THE SAMPLES TO TEST THE PROGRAM ON, WHICH IS "emailx.txt" WITH THE FORMAT AS ".txt", WITH AN x AS THE NUMBER(1,2,3...). I JUST POST ONE OF THEM SINCE THERE ARE MORE THAN ONE, SO I POST IT HERE IN ORDER FOR YOU TO HAVE AN IDEA TO WRITE THE CODE ON THIS.
SAMPLE .TXT
dear all,
on behalf of thrill company, i am glad to invite you for a luncheon party with all the senior employees, team members, and other staff members associated with the company. since according to associations policies, we have five working days, therefore we have planned to set a lunch party for saturday, 13th january 2012.
please mark your presence on this party. together, we would get an opportunity to interact with our boss, expand our contacts, learn more about our field and of course eat some mouth watering dishes. this luncheon would be held in a new york cafe situated at park lane. kindly be present by 12:00 noon so that your taste buds do not miss any of the tempting dishes being served!
i request you to confirm your presence latest by wednesday, 9th january 2012 so that we make appropriate bookings.
looking forward to see you on this thrills luncheon party!
sincerely, jacob thomas hr head thrill company
PROMPT:
Lab 2:Spam Filter In this lab, you will implement part of a naive Bayes' spam classifier. To illustrate how this filter works, consider the following email: Hey! This is the best link I found. I thought you would want to see it! www.somelink. com/example Best, Sus We want to classify this email as either spam or not spam. Typically, the filter will consider the emails. For our filter, we will entire email and look for multiple words that are common in spam consider a single word For this example, we will classify the email based on the word "best". Assume the probability that any particular email is spam is 0.25, and the probability that any particular email is not spam is 0.75 To classify the mystery email (above), we want to compute the probability that this email is spam given that it contains the word "best". Then we want to compute the probability that this email is NOT spam given that it contains the word "best". We then classify based on which probability is higher Let's define a couple of variables 1. C: email contains the word "best" 2T: email does NOT contain the word "best" 3. S: email is spam 4. S: email is NOT spam Hence, we want to compute P(SC) and P(SC) Computing the Probability of "best" First, we need to figure out how common "best" is in spam emails and how common "best" is in emails that are not spam. To do this, we have to use sample emails. This is called training data. For this example, we'll use the following emails. These are the sample spam emails 1. vou've been selected as a winner! click now to get the best anti-virus canner! 2. you're a winner! reply immediately to claim your access to the best weight loss system ever. Lab 2:Spam Filter In this lab, you will implement part of a naive Bayes' spam classifier. To illustrate how this filter works, consider the following email: Hey! This is the best link I found. I thought you would want to see it! www.somelink. com/example Best, Sus We want to classify this email as either spam or not spam. Typically, the filter will consider the emails. For our filter, we will entire email and look for multiple words that are common in spam consider a single word For this example, we will classify the email based on the word "best". Assume the probability that any particular email is spam is 0.25, and the probability that any particular email is not spam is 0.75 To classify the mystery email (above), we want to compute the probability that this email is spam given that it contains the word "best". Then we want to compute the probability that this email is NOT spam given that it contains the word "best". We then classify based on which probability is higher Let's define a couple of variables 1. C: email contains the word "best" 2T: email does NOT contain the word "best" 3. S: email is spam 4. S: email is NOT spam Hence, we want to compute P(SC) and P(SC) Computing the Probability of "best" First, we need to figure out how common "best" is in spam emails and how common "best" is in emails that are not spam. To do this, we have to use sample emails. This is called training data. For this example, we'll use the following emails. These are the sample spam emails 1. vou've been selected as a winner! click now to get the best anti-virus canner! 2. you're a winner! reply immediately to claim your access to the best weight loss system ever
