To do this, multiply the output of rand by (b-a) then add a. Generate a uniform distribution of random numbers on a specified interval.
#Rand matlab code#
This code makes a random choice between two equally probable alternatives.Įxample 2. Resets it to a different state each time.
#Rand matlab generator#
Resets the generator to its initial state.įor integer j, resets the generator to its j-th state. Returns a 35-element vector containing the current state of the uniform generator. Returns an array of random entries that is the same size as A.īy itself, returns a scalar whose value changes each time it's referenced. Create a matrix of uniformly distributed random integers between 1 and 10 with the same size as an existing array. Returns an m-by- n matrix of random entries. An error message appears if n is not a scalar. Returns an n-by- n matrix of random entries. The rand function generates arrays of random numbers whose elements are uniformly distributed in the interval ( 0, 1). Uniformly distributed random numbers and arrays My knowledge of floating-point math and RNGs isn't up to explaining why, or what you might do about that.Rand (MATLAB Functions) MATLAB Function Reference See Variable-Sizing Restrictions for Code Generation of Toolbox Functions (MATLAB Coder). For example, rand(sz,'myclass') does not invoke myclass.rand(sz). For other classes, the static rand method is not invoked. īut I should give a word of caution: they've put a lot of effort into making sure that output of rand gives an appropriate distribution of any given double within (0,1), and I don't think you're going to get the same nice properties on if you apply the scaling I suggested. The data type (class) must be a built-in MATLAB numeric type. So x = (rand-eps/2)/(1-eps) should give you values on the closed interval. (Use format hex to display enough precision to check that at the bit level). You can scale this interval to by subtracting eps/2 and dividing by 1-eps. Therefore, it's equivalent to drawing from the closed interval, or. (See doc RandStream.list, and then "Choosing a Random Number Generator" for info on other generators). For Mersenne twister, the default algorithm, the possible values are all multiples of 2^(-53), within the open interval (0,1). The precise set varies depending on the RNG algorithm used. However, since it produces doubles, there are only a finite number of values that it will actually produce. This was more clearly documented in previous versions, but it's still stated in the help text for rand (type help rand rather than doc rand). Rand produces numbers from the open interval (0,1), which does not include 0 or 1, so you should never get those values. There are so many possible outputs, all of them equally likely, that the probability of any given output is virtually zero. Unless you need repeatability or uniqueness, it is usually advisable to simply generate random values without reseeding the generator. You "never" see rand ever outputting exactly 1/4, either. As with 'shuffle' there is a caveat when reseeding MATLAB's random number generator, because it affects all subsequent output from rand, randi, and randn. Even if rand were of type (1) above, and thus could produce 0 and 1, it would produce them with probability so small that you would "never" see those values.ĭoes that sound strange? Well, that happens with any number. Therefore, you shouldn't worry about that: in either case the probability is zero for practical purposes. So the probability is either strictly zero or so small it can be neglected. If the random generator produces values from the open interval (0, 1), the probability of getting a value 0, or 1, is strictly zero. If you have a random generator that produces values of type double on the closed interval, the probability of getting the value 0, or 1, is not zero, but it's so small it can be neglected. Mathematically, if you sample from a (continuous) uniform distribution on the closed interval, values 0 and 1 (or any value, in fact) have probability strictly zero.
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