Uniform

I'm working on a data mining algorithm where i want to pick a random direction from a particular point in the feature space. If I pick a random number for each of the n dimensions from [-1,1] and then normalize the vector to a length of 1 will I get an even distribution across all possible directions? I'm speaking only theoretically here since computer generated random numbers are not actually random. Bram Cohen One simple trick is to select each dimension from a gaussian distribution, then normalize: from random import gauss def make_rand_vector(dims): vec = [gauss(0, 1) for i in range(dims)]

I am using jQuery Uniform to style inputs/selects etcs. However, the checkbox has stopped working. I am appending data sent from an ajax call. Once it's loaded, I use $.uniform.update("input:checkbox") to update the new html. When attempting to (un)check the input it works only once. If I want to (un)check it again, it doesn't change at all. I've attempted changing the Uniform Javascript so that all the actions (ie. click , focus , blur etc) are under the .live function. (ie. .live("click", ). This just stops anything from working. Update: I've read through the Uniform JS entirely and

开发自定义阴影, 用Uniform把Projection与ViewMatrix的值传到Shader. 因为Uniform的值总是前一帧的, 所以阴影在像机有动作时会抖动 使用内置的值就没问题了 uniform mat4 osg_ViewMatrix; 但是Uniform为什么会慢一帧? 来源： oschina 链接： https://my.oschina.net/u/235558/blog/3060888

One common way of choosing a random number in [0, n) is to take the result of rand() modulo n : rand() % n . However, even if the results returned by the available rand() implementation are fully uniform, shouldn't there be a problem with the uniformity of the resulting [0, n) numbers when RAND_MAX + 1 does not divide evenly by n ? E.g. suppose RAND_MAX is 2, and n is 2. Then out of 3 possible rand() outputs: 0, 1 and 2, we get 0, 1 and 0 respectively when we use them modulo n . Therefore the output will not be uniform at all. Is this a real problem in practice? What is a better way of

I've written a C function that I think selects integers from a uniform distribution with range [rangeLow, rangeHigh], inclusive. This isn't homework--I'm just using this in some embedded systems tinkering that I'm doing for fun. In my test cases, this code appears to produce an appropriate distribution. I'm not feeling fully confident that the implementation is correct, though. Could someone do a sanity check and let me know if I've done anything wrong here? //uniform_distribution returns an INTEGER in [rangeLow, rangeHigh], inclusive. int uniform_distribution(int rangeLow, int rangeHigh) {

In this StackOverflow question: Generating random integer from a range the accepted answer suggests the following formula for generating a random integer in between given min and max , with min and max being included into the range: output = min + (rand() % (int)(max - min + 1)) But it also says that This is still slightly biased towards lower numbers ... It's also possible to extend it so that it removes the bias. But it doesn't explain why it's biased towards lower numbers or how to remove the bias. So, the question is: is this the most optimal approach to generation of a random integer