arkouda.random._legacy

Module Contents

Functions

randint(→ arkouda.pdarrayclass.pdarray)

Generate a pdarray of randomized int, float, or bool values in a

standard_normal(→ arkouda.pdarrayclass.pdarray)

Draw real numbers from the standard normal distribution.

uniform(, high, seed, ...)

Generate a pdarray with uniformly distributed random float values
arkouda.random._legacy.randint(low: arkouda.dtypes.numeric_scalars, high: arkouda.dtypes.numeric_scalars, size: arkouda.dtypes.int_scalars | Tuple[arkouda.dtypes.int_scalars, Ellipsis] = 1, dtype=akint64, seed: arkouda.dtypes.int_scalars | None = None) → arkouda.pdarrayclass.pdarray[source]

Generate a pdarray of randomized int, float, or bool values in a specified range bounded by the low and high parameters.

Parameters:

  • low (numeric_scalars) – The low value (inclusive) of the range

  • high (numeric_scalars) – The high value (exclusive for int, inclusive for float) of the range

  • size (int_scalars) – The length of the returned array

  • dtype (Union[int64, float64, bool]) – The dtype of the array

  • seed (int_scalars, optional) – Seed to allow for reproducible random number generation

Returns:

Values drawn uniformly from the specified range having the desired dtype

Return type:

pdarray

Raises:

  • TypeError – Raised if dtype.name not in DTypes, size is not an int, low or high is not an int or float, or seed is not an int

  • ValueError – Raised if size < 0 or if high < low

Notes

Calling randint with dtype=float64 will result in uniform non-integral floating point values.

Ranges >= 2**64 in size is undefined behavior because it exceeds the maximum value that can be stored on the server (uint64)

Examples

>>> ak.randint(0, 10, 5)
array([5, 7, 4, 8, 3])

>>> ak.randint(0, 1, 3, dtype=ak.float64)
array([0.92176432277231968, 0.083130710959903542, 0.68894208386667544])

>>> ak.randint(0, 1, 5, dtype=ak.bool)
array([True, False, True, True, True])

>>> ak.randint(1, 5, 10, seed=2)
array([4, 3, 1, 3, 4, 4, 2, 4, 3, 2])

>>> ak.randint(1, 5, 3, dtype=ak.float64, seed=2)
array([2.9160772326374946, 4.353429832157099, 4.5392023718621486])

>>> ak.randint(1, 5, 10, dtype=ak.bool, seed=2)
array([False, True, True, True, True, False, True, True, True, True])
arkouda.random._legacy.standard_normal(size: arkouda.dtypes.int_scalars, seed: None | arkouda.dtypes.int_scalars = None) → arkouda.pdarrayclass.pdarray[source]

Draw real numbers from the standard normal distribution.

Parameters:

  • size (int_scalars) – The number of samples to draw (size of the returned array)

  • seed (int_scalars) – Value used to initialize the random number generator

Returns:

The array of random numbers

Return type:

pdarray, float64

Raises:

  • TypeError – Raised if size is not an int

  • ValueError – Raised if size < 0

See also

randint

Notes

For random samples from \(N(\mu, \sigma^2)\), use:

(sigma * standard_normal(size)) + mu

Examples

>>> ak.standard_normal(3,1)
array([-0.68586185091150265, 1.1723810583573375, 0.567584107142031])
arkouda.random._legacy.uniform(size: arkouda.dtypes.int_scalars, low: arkouda.dtypes.numeric_scalars = float(0.0), high: arkouda.dtypes.numeric_scalars = 1.0, seed: None | arkouda.dtypes.int_scalars = None) → arkouda.pdarrayclass.pdarray[source]

Generate a pdarray with uniformly distributed random float values in a specified range.

Parameters:

  • low (float_scalars) – The low value (inclusive) of the range, defaults to 0.0

  • high (float_scalars) – The high value (inclusive) of the range, defaults to 1.0

  • size (int_scalars) – The length of the returned array

  • seed (int_scalars, optional) – Value used to initialize the random number generator

Returns:

Values drawn uniformly from the specified range

Return type:

pdarray, float64

Raises:

  • TypeError – Raised if dtype.name not in DTypes, size is not an int, or if either low or high is not an int or float

  • ValueError – Raised if size < 0 or if high < low

Notes

The logic for uniform is delegated to the ak.randint method which is invoked with a dtype of float64

Examples

>>> ak.uniform(3)
array([0.92176432277231968, 0.083130710959903542, 0.68894208386667544])

>>> ak.uniform(size=3,low=0,high=5,seed=0)
array([0.30013431967121934, 0.47383036230759112, 1.0441791878997098])