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# numpy.matlib.rand

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```Return a matrix of random values with given shape.

Create a matrix of the given shape and propagate it with
random samples from a uniform distribution over ``[0, 1)``.

Parameters
----------
\*args : Arguments
Shape of the output.
If given as N integers, each integer specifies the size of one(more...)
```

```        def rand(*args):
"""
Return a matrix of random values with given shape.

Create a matrix of the given shape and propagate it with
random samples from a uniform distribution over ``[0, 1)``.

Parameters
----------
\\*args : Arguments
Shape of the output.
If given as N integers, each integer specifies the size of one
dimension.
If given as a tuple, this tuple gives the complete shape.

Returns
-------
out : ndarray
The matrix of random values with shape given by `\\*args`.

--------
randn, numpy.random.rand

Examples
--------
>>> import numpy.matlib
>>> np.matlib.rand(2, 3)
matrix([[ 0.68340382,  0.67926887,  0.83271405],
[ 0.00793551,  0.20468222,  0.95253525]])       #random
>>> np.matlib.rand((2, 3))
matrix([[ 0.84682055,  0.73626594,  0.11308016],
[ 0.85429008,  0.3294825 ,  0.89139555]])       #random

If the first argument is a tuple, other arguments are ignored:

>>> np.matlib.rand((2, 3), 4)
matrix([[ 0.46898646,  0.15163588,  0.95188261],
[ 0.59208621,  0.09561818,  0.00583606]])       #random

"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.rand(*args))
```

```from numpy import r_, c_, arange, diff, mean, sqrt, log, mat
from numpy import asarray, nan
from numpy.matlib import ones, zeros, rand, eye, empty
from numpy.linalg import eigh, cholesky, solve, lstsq
# (lstsq also as tool to determine rank)
```
```# test cases:
if __name__ == '__main__':
from numpy.matlib import rand
data = rand((100, 3))
print getDeterministics(100, 'ctl', 0.3).shape
```
```    # check loop for sorting evals:
for run in range(100):
m1 = rand((10,5))
m2 = rand((10,5))
m1in = m1.T * m1
```

```from numpy import r_, c_, arange, diff, mean, sqrt, log, mat
from numpy import asarray, nan
from numpy.matlib import ones, zeros, rand, eye, empty
from numpy.linalg import eigh, cholesky, solve, lstsq
# (lstsq also as tool to determine rank)
```
```# test cases:
if __name__ == '__main__':
from numpy.matlib import rand
data = rand((100, 3))
print getDeterministics(100, 'ctl', 0.3).shape
```
```    # check loop for sorting evals:
for run in range(100):
m1 = rand((10,5))
m2 = rand((10,5))
m1in = m1.T * m1
```

```def test_rand():
x = np.matlib.rand(3)
# check matrix type, array would have shape (3,)
assert_(x.ndim == 2)

```

```def test_rand():
x = np.matlib.rand(3)
# check matrix type, array would have shape (3,)
assert_(x.ndim == 2)

```

```def test_rand():
x = np.matlib.rand(3)
# check matrix type, array would have shape (3,)
assert_(x.ndim == 2)

```

```def test_kfilter():
model = setup()
T = 100
U = [mb.rand(2,1) for t in range(T)]
Xo, Y = model.simulate(U)
```
```def test_rtssmoother():
model = setup()
T = 100
U = [mb.rand(2,1) for t in range(T)]
Xo, Y = model.simulate(U)
```
```def test_kfilter_nonstationary():
T, model = setup_nonstationary()
U = [mb.rand(2,1) for t in range(T)]
Xo, Y = model.simulate(U)
X, P, K, XPred, PPred = model.kfilter(Y, U)
```