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s Note Why aren t we automating the download Unfortunately, it s because there s no web service available to download it programmatically. It s possible to script the download using, say, the Ruby net/http.rb download library and an HTML parsing library like Hpricot, but unless you have explicit permission from the web site in question, that s typically not a good idea. Potentially, you could be blocked from PayPal or, in an extreme situation, get sued. However, if you do decide to take that route, you might want to employ a library that is easier to use than Ruby s built-in libraries, such as scRUBYt! (http://scrubyt.org/).

let paul_graham word goodMap badMap ngood nbad = match ngood,nbad with 0,0 -> raise (Invalid_argument "Database Must Be Trained First!\n") | 0,_ -> raise (Invalid_argument "Ham Token count cannot be Zero\n") | _,0 -> raise (Invalid_argument "Spam Token count cannot be Zero\n") | _,_ -> let g = try 2 * (StringMap.find word goodMap) with Not_found -> 0 in let b = try StringMap.find word badMap with Not_found -> 0 in let numerator = try min 1.0 ((float_of_int b) /. (float_of_int nbad)) with Division_by_zero -> 1.0 in let denom = try (min 1.0 ((float_of_int g) /. (float_of_int ngood))) with Division_by_zero -> 1.0 in if ((numerator = 0.0) && (denom = 0.0)) then 0.5 else let targ = min 0.99 (numerator /. (numerator +. denom) ) in max 0.01 targ;; The next function is a helper function as well. It returns the first n elements of a sorted list in a somewhat crude way by converting the list into an array and then taking a subarray. The function after that actually calculates the probability of a given email being spam by finding the probabilities of each token being spam, taking the top 20 tokens (those with the highest probability of being spam), and combining them. There is no danger of this function returning Not A Number because you ensured that the scoring function (the paul_graham function) always returns some nonzero value. let top_n n lst = try let ar = Array.of_list lst in Array.to_list (Array.sub ar 0 n) with Invalid_argument("Array.sub") -> lst;; let calc_email_prob lbuf = let email = buildmap StringMap.empty lbuf in let scored = StringMap.mapi ( fun x va -> paul_graham x goodmap badmap goodcount badcount ) email in let top_vals = top_n 20 (List.rev (List.sort compare (StringMap.fold (fun x y z -> y :: z) scored []))) in

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Our DropDownList is only interested in a read-only set of data, so there s no need to worry about the INSERT, UPDATE, or DELETE queries. We simply enter our SQL code in the appropriate window, as shown in Figure 20-12, and click Next.

Now that you have a CSV file, you can analyze it. It s possible to analyze the data file directly in Ruby, but you might not want to write your own statistical code. Instead, you can load the data into MySQL, and then use MySQL s built-in aggregation functions to analyze it in detail. Many companies use CSV files to transfer financial, inventory, and other data, so this approach can work with data from organizations other than PayPal.

let n = List.fold_left (fun x y -> x *. y) 1.0 top_vals in let dn = List.fold_left (+.) 0. (List.map (fun x -> 1.0 -. x) top_vals) in try n /. (n +. dn) with Division_by_zero -> 0.0;; That is pretty much it for the actual calculating functions. The remaining functions are utility functions and training functions. First look at the three calling functions that enable you to calculate the probability of a channel, string, or file being spam according to the stored data. let spam_prob_of_channel ch = calc_email_prob (Lexing.from_channel ch);; let spam_prob_of_string st = calc_email_prob (Lexing.from_string st);; let spam_prob_of_file f = let ic = open_in f in let sp = spam_prob_of_channel ic in close_in ic; sp;; The next two functions are the training functions that train the database when given a ham or a spam data file. They write the data file out to the user s home directory. This value, the home directory environment variable, might not work correctly on Windows systems. let train_spam ch = let newbad = buildmap badmap (Lexing.from_channel ch) in let oc = open_out (Filename.concat (Sys.getenv "HOME") ".spamdb") in Marshal.to_channel oc (goodmap,newbad) []; close_out oc;; let train_ham ch = let newgood = buildmap goodmap (Lexing.from_channel ch) in let oc = open_out (Filename.concat (Sys.getenv "HOME") ".spamdb") in Marshal.to_channel oc (newgood,badmap) []; close_out oc;;

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