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You need a very large corpus of email to train a Bayesian classifier like this one. Luckily, the spamassassin developers have released a public corpus of email that can be used to develop antispam utilities. (A command-line client for this module is presented at the end of this chapter.) This corpus, broken up into ham and spam of varying stripes, can be downloaded from http://spamassassin.apache.org/publiccorpus/. Running the code on these corpuses gives results not nearly as good as Paul Graham says he got, but they are still very good. When tested on the files that made up the training corpus, the results are quite effective, especially with regard to false positives (nonspam mail was not flagged as spam). However, testing it against my own email (but not having it trained on that email) yielded what you might expect. This

The first half of the application is complete. The remaining half is fairly quick and straightforward. Now that we have a state lookup control, let s add a GridView to the page that will provide more detail for our selection process. Drag a GridView control onto the page just below the DropDownList, as shown in Figure 20-17.

module, although it will be slower. See the Ruby documentation at http://www.ruby-doc.org/stdlib/ libdoc/csv/rdoc/index.html for details.

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kind of classifier must be trained with data from your own email if you want it to work for your own email. The files "test" and "test2" were my email (spam and ham, respectively); the rest are clearly marked. The more email messages in your training files, the better. # # # # # # # # # # # #load "spam.cmo";; open Spam;; let ic = open_in "hamfile_training.txt" in train_ham ic;close_in ic;; : unit = () let ic = open_in "spamfile_training.txt" in train_spam ic;close_in ic;; : unit = () paul_graham "sex" goodmap badmap goodcount badcount;; : float = 0.216727266836569121 paul_graham "diet" goodmap badmap goodcount badcount;; : float = 0.773374476628347907 spam_prob_of_file "spam/0250.80b7bd444753246734e015af7b6d2d65";; : float = 0.8035183840360689 spam_prob_of_file "test";; : float = 0.00369466636815619486 spam_prob_of_file "test2";; : float = 1.54243567250543923e-06 spam_prob_of_file "spam/0494.a0865131f55d26362a8efad99c37de01";; : float = 0.8035183840360689 spam_prob_of_file "easy_ham/1954.5e99943978d64989611d5bd4814126ab";; : float = 3.79551940380821694e-11

Also, you need to create a MySQL database to store the data. You can do so by using the following command:

With the GridView now on the page, click the drop-down for Choose Data Source and select New Data Source. We ll be following much the same process we used for the DropDownList as we configure this data source. Select Database and accept SqlDataSource2 as the ID for the source. Click OK to continue. We will be able to reuse our previously configured connection string, so find the previously saved connection information on the drop-down, as shown in Figure 20-18, and click Next.

In this code segment, you ll notice a couple of extra keywords. We will cover these later, but for now you should focus on the similarities with the previous interactive examples. You can cut and paste the following lines into a text file by using your favorite editor and then save it. Under either Linux or Windows, compile the file into a byte-code executable using the OCaml byte-code compiler ocamlc. let _ = Printf.printf "Hello World\n";; Let s say you saved this file as firstprog.ml in your user directory on Windows. You would then compile the file into a byte-code executable and run it. The command is ocamlc o firstprog.exe firstprog.ml. This code uses the byte-code compiler (ocamlc) and outputs the executable file into firstprog.exe (the .exe extension does not need to be present on Linux). This code would create an output file called firstprog.exe (or ./firstprog if you are on Linux), which can be executed on the same command line, as shown in Figure 2-3.

To give you an idea of how FasterCSV works, let s look at the simplest use of FasterCSV: when you have a string consisting of CSV and you would like to parse it one row at a time. Here s an example:

Congratulations you have created your first OCaml program!

Once again, we ll be declaring our own custom SQL string, so we ll select the appropriate option, as shown in Figure 20-19. After clicking Next, we ll now need to add the actual SQL string to the wizard: SELECT Sales.Store.CustomerID, Sales.Store.Name, Person.Address.PostalCode, Person.StateProvince.Name AS State FROM Sales.Store join Sales.CustomerAddress ON Sales.Store.CustomerID = Sales.CustomerAddress.CustomerID JOIN Person.Address ON Person.Address.AddressID = Sales.CustomerAddress.AddressID JOIN Person.StateProvince ON Person.StateProvince.StateProvinceID = Person.Address.StateProvinceID WHERE Person.Address.StateProvinceID = @stateid

require "fastercsv" csvdata = "moonrock,10000,safe\n" csvdata << "collectible spoon,10,cupboard\n" csvdata << "scratched Billy Joel CD,1,desk\n" FasterCSV.parse(csvdata) do |row| item, value, location = *row puts "I own a #{item}, it's worth $#{value}, and I keep it in my #{location}." end

OCaml commonly uses other file extensions, too. The most common extensions are listed in Table 2-2 (they will be covered in more depth later in the book).

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