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Top Forums Programming How can I not fall in "not responding " while doing long loop? Post 302880622 by fwrlfo on Saturday 21st of December 2013 09:46:47 AM
Old 12-21-2013
How can I not fall in "not responding " while doing long loop?

Hi .. I'm a student that I helped by you when I was learning c for unix..

I want to ask you about different thing this time and I know you can help me
be cause I like your style helping people..
I want to ask about .net c# ...

I did a simple program that make me control my windows system using c# like turning it off or lock it at certain time the user input.. it's like scheduler but a lot easier to use my problem is "sorry for the messy desktop"
Image
that my program will inter in a while loop until the system time = the input time.. and that cause a not responding and that is not efficient.. I tried to add "sleep" but that didn't work with it and still not responding on the while loop
any solutions to make it run normally.. this is a function of the code.."the others are same but a little different"

Code:
private void button1_Click(object sender, EventArgs e)
        {
            string clock;
            clock = textBox1.Text;
            System.Windows.Forms.MessageBox.Show("system will close in " + clock);
            if (radioButton1.Checked)
            {
                clock = clock + " AM";
            }
            if (radioButton2.Checked)
            {
                clock = clock + " PM";
            }
            while (DateTime.Now.ToString("h:mm tt") != clock) // here is the problem
            {
                if (DateTime.Now.ToString("h:mm tt") == clock)
                    break;
               

            }
            
            System.Diagnostics.Process.Start("shutdown", "/s /t 0");
            this.Close();
        }

 

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Mail::SpamAssassin::Plugin::AutoLearnThreshold(3)	User Contributed Perl Documentation	 Mail::SpamAssassin::Plugin::AutoLearnThreshold(3)

NAME
Mail::SpamAssassin::Plugin::AutoLearnThreshold - threshold-based discriminator for Bayes auto-learning SYNOPSIS
loadplugin Mail::SpamAssassin::Plugin::AutoLearnThreshold DESCRIPTION
This plugin implements the threshold-based auto-learning discriminator for SpamAssassin's Bayes subsystem. Auto-learning is a mechanism whereby high-scoring mails (or low-scoring mails, for non-spam) are fed into its learning systems without user intervention, during scanning. Note that certain tests are ignored when determining whether a message should be trained upon: o rules with tflags set to 'learn' (the Bayesian rules) o rules with tflags set to 'userconf' (user configuration) o rules with tflags set to 'noautolearn' Also note that auto-learning occurs using scores from either scoreset 0 or 1, depending on what scoreset is used during message check. It is likely that the message check and auto-learn scores will be different. USER OPTIONS
The following configuration settings are used to control auto-learning: bayes_auto_learn_threshold_nonspam n.nn (default: 0.1) The score threshold below which a mail has to score, to be fed into SpamAssassin's learning systems automatically as a non-spam message. bayes_auto_learn_threshold_spam n.nn (default: 12.0) The score threshold above which a mail has to score, to be fed into SpamAssassin's learning systems automatically as a spam message. Note: SpamAssassin requires at least 3 points from the header, and 3 points from the body to auto-learn as spam. Therefore, the minimum working value for this option is 6. bayes_auto_learn_on_error (0 | 1) (default: 0) With "bayes_auto_learn_on_error" off, autolearning will be performed even if bayes classifier already agrees with the new classification (i.e. yielded BAYES_00 for what we are now trying to teach it as ham, or yielded BAYES_99 for spam). This is a traditional setting, the default was chosen to retain backwards compatibility. With "bayes_auto_learn_on_error" turned on, autolearning will be performed only when a bayes classifier had a different opinion from what the autolearner is now trying to teach it (i.e. it made an error in judgement). This strategy may or may not produce better future classifications, but usually works very well, while also preventing unnecessary overlearning and slows down database growth. perl v5.16.3 2011-06-06 Mail::SpamAssassin::Plugin::AutoLearnThreshold(3)
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