unifrnd

Continuous uniform random numbers

Description

example

r = unifrnd(a,b) generates a random number from the continuous uniform distribution with the lower endpoints a and upper endpoint b.

example

r = unifrnd(a,b,sz1,...,szN) generates an array of uniform random numbers, where sz1,...,szN indicates the size of each dimension.

example

r = unifrnd(a,b,sz) generates an array of uniform random numbers, where the size vector sz specifies size(r).

Examples

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Generate a random number from the continuous uniform distribution with the lower parameter 0 and upper parameter 1.

r = unifrnd(0,1)
r = 0.8147

Generate 5 random numbers from the continuous uniform distributions on the intervals (0,1), (0,2),..., (0,5).

a1 = 0;
b1 = 1:5;
r1 = unifrnd(a1,b1)
r1 = 1×5

0.8147    1.8116    0.3810    3.6535    3.1618

By default, unifrnd generates an array that is the same size as a and b after any necessary scalar expansion so that all scalars are expanded to match the dimensions of the other inputs.

If you specify array dimensions sz1,...,szN, they must match the dimensions of a and b after any necessary scalar expansion.

Generate a 2-by-3 array of random numbers from the continuous uniform distribution with the lower parameter 0 and upper parameter 1.

sz = [2 3];
r2 = unifrnd(0,1,sz)
r2 = 2×3

0.0975    0.5469    0.9649
0.2785    0.9575    0.1576

Generate 6 random numbers on the intervals (0,1), (1,2),..., (5,6).

a3 = 0:5;
b3 = 1:6;
r3 = unifrnd(a3,b3,1,6)
r3 = 1×6

0.9706    1.9572    2.4854    3.8003    4.1419    5.4218

Input Arguments

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Lower endpoint of the uniform distribution, specified as a scalar value or an array of scalar values.

To generate random numbers from multiple distributions, specify a and b using arrays. If both a and b are arrays, then the array sizes must be the same. If either a or b is a scalar, then unifrnd expands the scalar argument into a constant array of the same size as the other argument. Each element in r is the random number generated from the distribution specified by the corresponding elements in a and b.

Example: [0 -1 7 9]

Data Types: single | double

Upper endpoint of the uniform distribution, specified as a scalar value or an array of scalar values.

To generate random numbers from multiple distributions, specify a and b using arrays. If both a and b are arrays, then the array sizes must be the same. If either a or b is a scalar, then unifrnd expands the scalar argument into a constant array of the same size as the other argument. Each element in r is the random number generated from the distribution specified by the corresponding elements in a and b.

Example: [1 1 10 10]

Data Types: single | double

Size of each dimension, specified as separate arguments of integers.

If either a or b is an array, then the specified dimensions sz1,...,szN must match the common dimensions of a and b after any necessary scalar expansion. The default values of sz1,...,szN are the common dimensions.

• If you specify a single value sz1, then r is a square matrix of size sz1-by-sz1.

• If the size of any dimension is 0 or negative, then r is an empty array.

• Beyond the second dimension, unifrnd ignores trailing dimensions with a size of 1. For example, unifrnd(–3,5,3,1,1,1) produces a 3-by-1 vector of random numbers from the uniform distribution with lower endpoint –3 and upper endpoint 5.

Example: 2,3

Data Types: single | double

Size of each dimension, specified as a row vector of integers.

If either a or b is an array, then the specified dimensions sz must match the common dimensions of a and b after any necessary scalar expansion. The default values of sz are the common dimensions.

• If you specify a single value [sz1], then r is a square matrix of size sz1-by-sz1.

• If the size of any dimension is 0 or negative, then r is an empty array.

• Beyond the second dimension, unifrnd ignores trailing dimensions with a size of 1. For example, unifrnd(–3,5,[3 1 1 1]) produces a 3-by-1 vector of random numbers from the uniform distribution with lower endpoint –3 and upper endpoint 5.

Example: [2 3]

Data Types: single | double

Output Arguments

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Uniform random numbers, returned as a scalar value or an array of scalar values with the dimensions specified by sz1,...,szN or sz. Each element in r is the random number generated from the distribution specified by the corresponding elements in a and b.

Alternative Functionality

• unifrnd is a function specific to the continuous uniform distribution. Statistics and Machine Learning Toolbox™ also offers the generic function random, which supports various probability distributions. To use random, create a UniformDistribution probability distribution object and pass the object as an input argument or specify the probability distribution name and its parameters. Note that the distribution-specific function unifrnd is faster than the generic function random.

• Use rand to generate numbers from the uniform distribution on the interval (0,1).

• To generate random numbers interactively, use randtool, a user interface for random number generation.