Databases
StackOverflow2013
Postsdbo
PO
 

Note that there are some explanatory texts on larger screens.

plurals
  1. PO
    primarykey
    data
    text
    <p>You can do this more directly than actually generating all these matrices, and it's pretty easy to do so, by thinking about the distribution of the final output.</p> <p>The probability of a random variable distributed by N(0, .2) lying between .2 and .3 is p ~= .092.</p> <p>Call the random variable of the final output of your matrix X, where you do this n (20) times. Then either (a) X lies between .2 and .3 and you stopped early, or (b) you didn't draw a number between .2 and .3 in the first n-1 draws and so you went with whatever you got on the nth draw.</p> <p>The probability of (b) happening is just b=(1-p)^(n-1): the independent events of drawing outside [.2, .3], which have probability 1-p, happend n-1 times. Therefore the probability of (a) is 1-b.</p> <p>If (b) happened, you just draw a number from <code>normrnd</code>. If (a) happened, you need the value of a normal variable, conditional on its being between .2 and .3. One way to do this is to find the cdf values for .2 and .3, draw uniformly from the range between there, and then use the inverse cdf to get back the original number.</p> <p>Code that does this:</p> <pre><code>mu = 0; sigma = .2; upper = .3; lower = .2; n = 20; sz = 15; cdf_upper = normcdf(upper, mu, sigma); cdf_lower = normcdf(lower, mu, sigma); p = cdf_upper - cdf_lower; b = (1-p) ^ (n - 1); results = zeros(sz, sz); mask = rand(sz, sz) &gt; b; % mask value 1 means case (a), 0 means case (b) num_a = sum(mask(:)); cdf_vals = rand(num_a, 1) * p + cdf_lower; results(mask) = norminv(cdf_vals, mu, sigma); results(~mask) = normrnd(mu, sigma, sz^2 - num_a, 1); </code></pre> <hr/> <p>If you want to simulate this directly for some reason (which is going to involve a lot of wasted effort, but apparently you don't like "the complications of statistics" -- by the way, this is probability, not statistics), you can generate the first matrix and then replace only the elements that don't fall in your desired range. For example:</p> <pre><code>mu = 0; sigma = .2; n = 10; m = 10; num_runs = 20; lower = .2; upper = .3; result = normrnd(mu, sigma, n, m); for i = 1 : (num_runs - 1) to_replace = (result &lt; lower) | (result &gt; upper); result(to_replace) = normrnd(mu, sigma, sum(to_replace(:)), 1); end </code></pre> <hr /> <p>To demonstrate that these are the same, here's a plots of the empirical CDFs of doing this for 1x1 matrices 100,000 times. (That is, I ran both functions 100k times and saved the results, then used <code>cdfplot</code> to plot values on the x axis vs portion of the obtained values that are less than that on the y axis.)</p> <p><img src="https://i.imgur.com/ZUR4I.png" /></p> <p>They're identical. (Indeed, a <a href="http://en.wikipedia.org/wiki/K-S_Test" rel="nofollow noreferrer">K-S test</a> for identity of distribution gives a p-value of .71.) But the direct way was a bunch faster to run.</p>
    singulars
    1. This table or related slice is empty.
    1. POconditional generation of random numbers using matlab
      singulars
      1. PTQuestion
    1. PTAnswer
    1. USDougal
    1. USDougal
    plurals
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. POconditional generation of random numbers using matlab
      singulars
      1. PTQuestion
    1. This table or related slice is empty.
    1. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    2. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    3. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTAcceptedByOriginator
    1. COthe code ended with the following error @Dougal: ??? Improper assignment with rectangular empty matrix.
      singulars
      1. PO
      1. USpac
    2. CO@pac I just ran the more direct approach and it works fine (though I fixed some small mistakes). That's going to be much faster and better for you to do, so I removed the other way from my answer. Note that the probability `b` for these parameters is .16, so 84% of your matrix is going to be "stopping early".
      singulars
      1. PO
      1. USDougal
    3. CO@pac I re-added a working version of the code that does it directly. I still think actually putting 30 seconds of thought into it is the better approach, but whatever. Also, I don't see how `rng` is related to this problem: are you trying to make the results reproducible, or are you hoping to use it somehow to do the generation for you?
      singulars
      1. PO
      1. USDougal
 

Querying!

 
Guidance
A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload