# ndarray.random¶

Random distribution generator NDArray API of MXNet.

Functions

 uniform([low, high, shape, dtype, ctx, out]) Draw random samples from a uniform distribution. normal([loc, scale, shape, dtype, ctx, out]) Draw random samples from a normal (Gaussian) distribution. randn(*shape, **kwargs) Draw random samples from a normal (Gaussian) distribution. poisson([lam, shape, dtype, ctx, out]) Draw random samples from a Poisson distribution. exponential([scale, shape, dtype, ctx, out]) Draw samples from an exponential distribution. gamma([alpha, beta, shape, dtype, ctx, out]) Draw random samples from a gamma distribution. multinomial(data[, shape, get_prob, out, dtype]) Concurrent sampling from multiple multinomial distributions. negative_binomial([k, p, shape, dtype, ctx, out]) Draw random samples from a negative binomial distribution. generalized_negative_binomial([mu, alpha, …]) Draw random samples from a generalized negative binomial distribution. shuffle(data, **kwargs) Shuffle the elements randomly. randint(low, high[, shape, dtype, ctx, out]) Draw random samples from a discrete uniform distribution. exponential_like([data, lam, out, name]) Draw random samples from an exponential distribution according to the input array shape. gamma_like([data, alpha, beta, out, name]) Draw random samples from a gamma distribution according to the input array shape. generalized_negative_binomial_like([data, …]) Draw random samples from a generalized negative binomial distribution according to the input array shape. negative_binomial_like([data, k, p, out, name]) Draw random samples from a negative binomial distribution according to the input array shape. normal_like([data, loc, scale, out, name]) Draw random samples from a normal (Gaussian) distribution according to the input array shape. poisson_like([data, lam, out, name]) Draw random samples from a Poisson distribution according to the input array shape. uniform_like([data, low, high, out, name]) Draw random samples from a uniform distribution according to the input array shape.
mxnet.ndarray.random.uniform(low=0, high=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)[source]

Draw random samples from a uniform distribution.

Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high).

Parameters
• low (float or NDArray, optional) – Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.

• high (float or NDArray, optional) – Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0.

• shape (int or tuple of ints, optional) – The number of samples to draw. If shape is, e.g., (m, n) and low and high are scalars, output shape will be (m, n). If low and high are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [low, high) pair.

• dtype ({'float16', 'float32', 'float64'}, optional) – Data type of output samples. Default is ‘float32’

• ctx (Context, optional) – Device context of output. Default is current context. Overridden by low.context when low is an NDArray.

• out (NDArray, optional) – Store output to an existing NDArray.

Returns

An NDArray of type dtype. If input shape has shape, e.g., (m, n) and low and high are scalars, output shape will be (m, n). If low and high are NDArrays with shape, e.g., (x, y), then the return NDArray will have shape (x, y, m, n), where m*n uniformly distributed samples are drawn for each [low, high) pair.

Return type

NDArray

Examples

>>> mx.nd.random.uniform(0, 1)
[ 0.54881352]
<NDArray 1 @cpu(0)
>>> mx.nd.random.uniform(0, 1, ctx=mx.gpu(0))
[ 0.92514056]
<NDArray 1 @gpu(0)>
>>> mx.nd.random.uniform(-1, 1, shape=(2,))
[ 0.71589124  0.08976638]
<NDArray 2 @cpu(0)>
>>> low = mx.nd.array([1,2,3])
>>> high = mx.nd.array([2,3,4])
>>> mx.nd.random.uniform(low, high, shape=2)
[[ 1.78653979  1.93707538]
[ 2.01311183  2.37081361]
[ 3.30491424  3.69977832]]
<NDArray 3x2 @cpu(0)>

mxnet.ndarray.random.normal(loc=0, scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)[source]

Draw random samples from a normal (Gaussian) distribution.

Samples are distributed according to a normal distribution parametrized by loc (mean) and scale (standard deviation).

Parameters
• loc (float or NDArray, optional) – Mean (centre) of the distribution.

• scale (float or NDArray, optional) – Standard deviation (spread or width) of the distribution.

• shape (int or tuple of ints, optional) – The number of samples to draw. If shape is, e.g., (m, n) and loc and scale are scalars, output shape will be (m, n). If loc and scale are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [loc, scale) pair.

• dtype ({'float16', 'float32', 'float64'}, optional) – Data type of output samples. Default is ‘float32’

• ctx (Context, optional) – Device context of output. Default is current context. Overridden by loc.context when loc is an NDArray.

• out (NDArray, optional) – Store output to an existing NDArray.

Returns

An NDArray of type dtype. If input shape has shape, e.g., (m, n) and loc and scale are scalars, output shape will be (m, n). If loc and scale are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [loc, scale) pair.

Return type

NDArray

Examples

>>> mx.nd.random.normal(0, 1)
[ 2.21220636]
<NDArray 1 @cpu(0)>
>>> mx.nd.random.normal(0, 1, ctx=mx.gpu(0))
[ 0.29253659]
<NDArray 1 @gpu(0)>
>>> mx.nd.random.normal(-1, 1, shape=(2,))
[-0.2259962  -0.51619542]
<NDArray 2 @cpu(0)>
>>> loc = mx.nd.array([1,2,3])
>>> scale = mx.nd.array([2,3,4])
>>> mx.nd.random.normal(loc, scale, shape=2)
[[ 0.55912292  3.19566321]
[ 1.91728961  2.47706747]
[ 2.79666662  5.44254589]]
<NDArray 3x2 @cpu(0)>

mxnet.ndarray.random.randn(*shape, **kwargs)[source]

Draw random samples from a normal (Gaussian) distribution.

Samples are distributed according to a normal distribution parametrized by loc (mean) and scale (standard deviation).

Parameters
• loc (float or NDArray) – Mean (centre) of the distribution.

• scale (float or NDArray) – Standard deviation (spread or width) of the distribution.

• shape (int or tuple of ints) – The number of samples to draw. If shape is, e.g., (m, n) and loc and scale are scalars, output shape will be (m, n). If loc and scale are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [loc, scale) pair.

• dtype ({'float16', 'float32', 'float64'}) – Data type of output samples. Default is ‘float32’

• ctx (Context) – Device context of output. Default is current context. Overridden by loc.context when loc is an NDArray.

• out (NDArray) – Store output to an existing NDArray.

Returns

If input shape has shape, e.g., (m, n) and loc and scale are scalars, output shape will be (m, n). If loc and scale are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [loc, scale) pair.

Return type

NDArray

Examples

>>> mx.nd.random.randn()
2.21220636
<NDArray 1 @cpu(0)>
>>> mx.nd.random.randn(2, 2)
[[-1.856082   -1.9768796 ]
[-0.20801921  0.2444218 ]]
<NDArray 2x2 @cpu(0)>
>>> mx.nd.random.randn(2, 3, loc=5, scale=1)
[[4.19962   4.8311777 5.936328 ]
[5.357444  5.7793283 3.9896927]]
<NDArray 2x3 @cpu(0)>

mxnet.ndarray.random.poisson(lam=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)[source]

Draw random samples from a Poisson distribution.

Samples are distributed according to a Poisson distribution parametrized by lambda (rate). Samples will always be returned as a floating point data type.

Parameters
• lam (float or NDArray, optional) – Expectation of interval, should be >= 0.

• shape (int or tuple of ints, optional) – The number of samples to draw. If shape is, e.g., (m, n) and lam is a scalar, output shape will be (m, n). If lam is an NDArray with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each entry in lam.

• dtype ({'float16', 'float32', 'float64'}, optional) – Data type of output samples. Default is ‘float32’

• ctx (Context, optional) – Device context of output. Default is current context. Overridden by lam.context when lam is an NDArray.

• out (NDArray, optional) – Store output to an existing NDArray.

Returns

If input shape has shape, e.g., (m, n) and lam is a scalar, output shape will be (m, n). If lam is an NDArray with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each entry in lam.

Return type

NDArray

Examples

>>> mx.nd.random.poisson(1)
[ 1.]
<NDArray 1 @cpu(0)>
>>> mx.nd.random.poisson(1, shape=(2,))
[ 0.  2.]
<NDArray 2 @cpu(0)>
>>> lam = mx.nd.array([1,2,3])
>>> mx.nd.random.poisson(lam, shape=2)
[[ 1.  3.]
[ 3.  2.]
[ 2.  3.]]
<NDArray 3x2 @cpu(0)>

mxnet.ndarray.random.exponential(scale=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)[source]

Draw samples from an exponential distribution.

Its probability density function is

$f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),$

for x > 0 and 0 elsewhere. beta is the scale parameter, which is the inverse of the rate parameter lambda = 1/beta.

Parameters
• scale (float or NDArray, optional) – The scale parameter, beta = 1/lambda.

• shape (int or tuple of ints, optional) – The number of samples to draw. If shape is, e.g., (m, n) and scale is a scalar, output shape will be (m, n). If scale is an NDArray with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each entry in scale.

• dtype ({'float16', 'float32', 'float64'}, optional) – Data type of output samples. Default is ‘float32’

• ctx (Context, optional) – Device context of output. Default is current context. Overridden by scale.context when scale is an NDArray.

• out (NDArray, optional) – Store output to an existing NDArray.

Returns

If input shape has shape, e.g., (m, n) and scale is a scalar, output shape will be (m, n). If scale is an NDArray with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each entry in scale.

Return type

NDArray

Examples

>>> mx.nd.random.exponential(1)
[ 0.79587454]
<NDArray 1 @cpu(0)>
>>> mx.nd.random.exponential(1, shape=(2,))
[ 0.89856035  1.25593066]
<NDArray 2 @cpu(0)>
>>> scale = mx.nd.array([1,2,3])
>>> mx.nd.random.exponential(scale, shape=2)
[[  0.41063145   0.42140478]
[  2.59407091  10.12439728]
[  2.42544937   1.14260709]]
<NDArray 3x2 @cpu(0)>

mxnet.ndarray.random.gamma(alpha=1, beta=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)[source]

Draw random samples from a gamma distribution.

Samples are distributed according to a gamma distribution parametrized by alpha (shape) and beta (scale).

Parameters
• alpha (float or NDArray, optional) – The shape of the gamma distribution. Should be greater than zero.

• beta (float or NDArray, optional) – The scale of the gamma distribution. Should be greater than zero. Default is equal to 1.

• shape (int or tuple of ints, optional) – The number of samples to draw. If shape is, e.g., (m, n) and alpha and beta are scalars, output shape will be (m, n). If alpha and beta are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [alpha, beta) pair.

• dtype ({'float16', 'float32', 'float64'}, optional) – Data type of output samples. Default is ‘float32’

• ctx (Context, optional) – Device context of output. Default is current context. Overridden by alpha.context when alpha is an NDArray.

• out (NDArray, optional) – Store output to an existing NDArray.

Returns

If input shape has shape, e.g., (m, n) and alpha and beta are scalars, output shape will be (m, n). If alpha and beta are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [alpha, beta) pair.

Return type

NDArray

Examples

>>> mx.nd.random.gamma(1, 1)
[ 1.93308783]
<NDArray 1 @cpu(0)>
>>> mx.nd.random.gamma(1, 1, shape=(2,))
[ 0.48216391  2.09890771]
<NDArray 2 @cpu(0)>
>>> alpha = mx.nd.array([1,2,3])
>>> beta = mx.nd.array([2,3,4])
>>> mx.nd.random.gamma(alpha, beta, shape=2)
[[  3.24343276   0.94137681]
[  3.52734375   0.45568955]
[ 14.26264095  14.0170126 ]]
<NDArray 3x2 @cpu(0)>

mxnet.ndarray.random.multinomial(data, shape=_Null, get_prob=False, out=None, dtype='int32', **kwargs)[source]

Concurrent sampling from multiple multinomial distributions.

Note

The input distribution must be normalized, i.e. data must sum to 1 along its last dimension.

Parameters
• data (NDArray) – An n dimensional array whose last dimension has length k, where k is the number of possible outcomes of each multinomial distribution. For example, data with shape (m, n, k) specifies m*n multinomial distributions each with k possible outcomes.

• shape (int or tuple of ints, optional) – The number of samples to draw from each distribution. If shape is empty one sample will be drawn from each distribution.

• get_prob (bool, optional) – If true, a second array containing log likelihood of the drawn samples will also be returned. This is usually used for reinforcement learning, where you can provide reward as head gradient w.r.t. this array to estimate gradient.

• out (NDArray, optional) – Store output to an existing NDArray.

• dtype (str or numpy.dtype, optional) – Data type of the sample output array. The default is int32. Note that the data type of the log likelihood array is the same with that of data.

Returns

For input data with n dimensions and shape (d1, d2, …, dn-1, k), and input shape with shape (s1, s2, …, sx), returns an NDArray with shape (d1, d2, … dn-1, s1, s2, …, sx). The s1, s2, … sx dimensions of the returned NDArray consist of 0-indexed values sampled from each respective multinomial distribution provided in the k dimension of data.

For the case n=1, and x=1 (one shape dimension), returned NDArray has shape (s1,).

If get_prob is set to True, this function returns a list of format: [ndarray_output, log_likelihood_output], where log_likelihood_output is an NDArray of the same shape as the sampled outputs.

Return type

List, or NDArray

Examples

>>> probs = mx.nd.array([0, 0.1, 0.2, 0.3, 0.4])
>>> mx.nd.random.multinomial(probs)
[3]
<NDArray 1 @cpu(0)>
>>> probs = mx.nd.array([[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]])
>>> mx.nd.random.multinomial(probs)
[3 1]
<NDArray 2 @cpu(0)>
>>> mx.nd.random.multinomial(probs, shape=2)
[[4 4]
[1 2]]
<NDArray 2x2 @cpu(0)>
>>> mx.nd.random.multinomial(probs, get_prob=True)
[3 2]
<NDArray 2 @cpu(0)>
[-1.20397282 -1.60943794]
<NDArray 2 @cpu(0)>

mxnet.ndarray.random.negative_binomial(k=1, p=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)[source]

Draw random samples from a negative binomial distribution.

Samples are distributed according to a negative binomial distribution parametrized by k (limit of unsuccessful experiments) and p (failure probability in each experiment). Samples will always be returned as a floating point data type.

Parameters
• k (float or NDArray, optional) – Limit of unsuccessful experiments, > 0.

• p (float or NDArray, optional) – Failure probability in each experiment, >= 0 and <=1.

• shape (int or tuple of ints, optional) – The number of samples to draw. If shape is, e.g., (m, n) and k and p are scalars, output shape will be (m, n). If k and p are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [k, p) pair.

• dtype ({'float16', 'float32', 'float64'}, optional) – Data type of output samples. Default is ‘float32’

• ctx (Context, optional) – Device context of output. Default is current context. Overridden by k.context when k is an NDArray.

• out (NDArray, optional) – Store output to an existing NDArray.

Returns

If input shape has shape, e.g., (m, n) and k and p are scalars, output shape will be (m, n). If k and p are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [k, p) pair.

Return type

NDArray

Examples

>>> mx.nd.random.negative_binomial(10, 0.5)
[ 4.]
<NDArray 1 @cpu(0)>
>>> mx.nd.random.negative_binomial(10, 0.5, shape=(2,))
[ 3.  4.]
<NDArray 2 @cpu(0)>
>>> k = mx.nd.array([1,2,3])
>>> p = mx.nd.array([0.2,0.4,0.6])
>>> mx.nd.random.negative_binomial(k, p, shape=2)
[[ 3.  2.]
[ 4.  4.]
[ 0.  5.]]
<NDArray 3x2 @cpu(0)>

mxnet.ndarray.random.generalized_negative_binomial(mu=1, alpha=1, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)[source]

Draw random samples from a generalized negative binomial distribution.

Samples are distributed according to a generalized negative binomial distribution parametrized by mu (mean) and alpha (dispersion). alpha is defined as 1/k where k is the failure limit of the number of unsuccessful experiments (generalized to real numbers). Samples will always be returned as a floating point data type.

Parameters
• mu (float or NDArray, optional) – Mean of the negative binomial distribution.

• alpha (float or NDArray, optional) – Alpha (dispersion) parameter of the negative binomial distribution.

• shape (int or tuple of ints, optional) – The number of samples to draw. If shape is, e.g., (m, n) and mu and alpha are scalars, output shape will be (m, n). If mu and alpha are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [mu, alpha) pair.

• dtype ({'float16', 'float32', 'float64'}, optional) – Data type of output samples. Default is ‘float32’

• ctx (Context, optional) – Device context of output. Default is current context. Overridden by mu.context when mu is an NDArray.

• out (NDArray, optional) – Store output to an existing NDArray.

Returns

If input shape has shape, e.g., (m, n) and mu and alpha are scalars, output shape will be (m, n). If mu and alpha are NDArrays with shape, e.g., (x, y), then output will have shape (x, y, m, n), where m*n samples are drawn for each [mu, alpha) pair.

Return type

NDArray

Examples

>>> mx.nd.random.generalized_negative_binomial(10, 0.5)
[ 19.]
<NDArray 1 @cpu(0)>
>>> mx.nd.random.generalized_negative_binomial(10, 0.5, shape=(2,))
[ 30.  21.]
<NDArray 2 @cpu(0)>
>>> mu = mx.nd.array([1,2,3])
>>> alpha = mx.nd.array([0.2,0.4,0.6])
>>> mx.nd.random.generalized_negative_binomial(mu, alpha, shape=2)
[[ 4.  0.]
[ 3.  2.]
[ 6.  2.]]
<NDArray 3x2 @cpu(0)>

mxnet.ndarray.random.shuffle(data, **kwargs)[source]

Shuffle the elements randomly.

This shuffles the array along the first axis. The order of the elements in each subarray does not change. For example, if a 2D array is given, the order of the rows randomly changes, but the order of the elements in each row does not change.

Parameters
• data (NDArray) – Input data array.

• out (NDArray, optional) – Array to store the result.

Returns

A new NDArray with the same shape and type as input data, but with items in the first axis of the returned NDArray shuffled randomly. The original input data is not modified.

Return type

NDArray

Examples

>>> data = mx.nd.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
>>> mx.nd.random.shuffle(data)
[[ 0.  1.  2.]
[ 6.  7.  8.]
[ 3.  4.  5.]]
<NDArray 2x3 @cpu(0)>
>>> mx.nd.random.shuffle(data)
[[ 3.  4.  5.]
[ 0.  1.  2.]
[ 6.  7.  8.]]
<NDArray 2x3 @cpu(0)>

mxnet.ndarray.random.randint(low, high, shape=_Null, dtype=_Null, ctx=None, out=None, **kwargs)[source]

Draw random samples from a discrete uniform distribution.

Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high).

Parameters
• low (int, required) – Lower boundary of the output interval. All values generated will be greater than or equal to low.

• high (int, required) – Upper boundary of the output interval. All values generated will be less than high.

• shape (int or tuple of ints, optional) – The number of samples to draw. If shape is, e.g., (m, n) and low and high are scalars, output shape will be (m, n).

• dtype ({'int32', 'int64'}, optional) – Data type of output samples. Default is ‘int32’

• ctx (Context, optional) – Device context of output. Default is current context. Overridden by low.context when low is an NDArray.

• out (NDArray, optional) – Store output to an existing NDArray.

Returns

An NDArray of type dtype. If input shape has shape, e.g., (m, n), the returned NDArray will shape will be (m, n). Contents of the returned NDArray will be samples from the interval [low, high).

Return type

NDArray

Examples

>>> mx.nd.random.randint(5, 100)
[ 90]
<NDArray 1 @cpu(0)
>>> mx.nd.random.randint(-10, 2, ctx=mx.gpu(0))
[ -8]
<NDArray 1 @gpu(0)>
>>> mx.nd.random.randint(-10, 10, shape=(2,))
[ -5  4]
<NDArray 2 @cpu(0)>

mxnet.ndarray.random.exponential_like(data=None, lam=_Null, out=None, name=None, **kwargs)

Draw random samples from an exponential distribution according to the input array shape.

Samples are distributed according to an exponential distribution parametrized by lambda (rate).

Example:

exponential(lam=4, data=ones(2,2)) = [[ 0.0097189 ,  0.08999364],
[ 0.04146638,  0.31715935]]


Defined in src/operator/random/sample_op.cc:L243

Parameters
• lam (float, optional, default=1) – Lambda parameter (rate) of the exponential distribution.

• data (NDArray) – The input

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.random.gamma_like(data=None, alpha=_Null, beta=_Null, out=None, name=None, **kwargs)

Draw random samples from a gamma distribution according to the input array shape.

Samples are distributed according to a gamma distribution parametrized by alpha (shape) and beta (scale).

Example:

gamma(alpha=9, beta=0.5, data=ones(2,2)) = [[ 7.10486984,  3.37695289],
[ 3.91697288,  3.65933681]]


Defined in src/operator/random/sample_op.cc:L232

Parameters
• alpha (float, optional, default=1) – Alpha parameter (shape) of the gamma distribution.

• beta (float, optional, default=1) – Beta parameter (scale) of the gamma distribution.

• data (NDArray) – The input

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.random.generalized_negative_binomial_like(data=None, mu=_Null, alpha=_Null, out=None, name=None, **kwargs)

Draw random samples from a generalized negative binomial distribution according to the input array shape.

Samples are distributed according to a generalized negative binomial distribution parametrized by mu (mean) and alpha (dispersion). alpha is defined as 1/k where k is the failure limit of the number of unsuccessful experiments (generalized to real numbers). Samples will always be returned as a floating point data type.

Example:

generalized_negative_binomial(mu=2.0, alpha=0.3, data=ones(2,2)) = [[ 2.,  1.],
[ 6.,  4.]]


Defined in src/operator/random/sample_op.cc:L284

Parameters
• mu (float, optional, default=1) – Mean of the negative binomial distribution.

• alpha (float, optional, default=1) – Alpha (dispersion) parameter of the negative binomial distribution.

• data (NDArray) – The input

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.random.negative_binomial_like(data=None, k=_Null, p=_Null, out=None, name=None, **kwargs)

Draw random samples from a negative binomial distribution according to the input array shape.

Samples are distributed according to a negative binomial distribution parametrized by k (limit of unsuccessful experiments) and p (failure probability in each experiment). Samples will always be returned as a floating point data type.

Example:

negative_binomial(k=3, p=0.4, data=ones(2,2)) = [[ 4.,  7.],
[ 2.,  5.]]


Defined in src/operator/random/sample_op.cc:L268

Parameters
• k (int, optional, default='1') – Limit of unsuccessful experiments.

• p (float, optional, default=1) – Failure probability in each experiment.

• data (NDArray) – The input

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.random.normal_like(data=None, loc=_Null, scale=_Null, out=None, name=None, **kwargs)

Draw random samples from a normal (Gaussian) distribution according to the input array shape.

Samples are distributed according to a normal distribution parametrized by loc (mean) and scale (standard deviation).

Example:

normal(loc=0, scale=1, data=ones(2,2)) = [[ 1.89171135, -1.16881478],
[-1.23474145,  1.55807114]]


Defined in src/operator/random/sample_op.cc:L221

Parameters
• loc (float, optional, default=0) – Mean of the distribution.

• scale (float, optional, default=1) – Standard deviation of the distribution.

• data (NDArray) – The input

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.random.poisson_like(data=None, lam=_Null, out=None, name=None, **kwargs)

Draw random samples from a Poisson distribution according to the input array shape.

Samples are distributed according to a Poisson distribution parametrized by lambda (rate). Samples will always be returned as a floating point data type.

Example:

poisson(lam=4, data=ones(2,2)) = [[ 5.,  2.],
[ 4.,  6.]]


Defined in src/operator/random/sample_op.cc:L255

Parameters
• lam (float, optional, default=1) – Lambda parameter (rate) of the Poisson distribution.

• data (NDArray) – The input

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays

mxnet.ndarray.random.uniform_like(data=None, low=_Null, high=_Null, out=None, name=None, **kwargs)

Draw random samples from a uniform distribution according to the input array shape.

Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high).

Example:

uniform(low=0, high=1, data=ones(2,2)) = [[ 0.60276335,  0.85794562],
[ 0.54488319,  0.84725171]]


Defined in src/operator/random/sample_op.cc:L209

Parameters
• low (float, optional, default=0) – Lower bound of the distribution.

• high (float, optional, default=1) – Upper bound of the distribution.

• data (NDArray) – The input

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays