ndarray.random¶

print(dir(nd.random)):

[
 'normal',    从均匀分布采样（uniform）
 'poisson',   从泊松分布采样（poisson）
 'uniform'    从正态分布采样（normal）
 ...
 ]

uniform¶

说明:

Draw random samples from a uniform distribution.
从均匀分布中抽取随机样本。

格式:

mxnet.ndarray.random.uniform(low=0, high=1, shape=_Null, dtype=_Null,
    ctx=None, out=None, **kwargs)

实例:

>>> 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)>

normal¶

说明:

从正态（高斯）分布中抽取随机样本。
Draw random samples from a normal (Gaussian) distribution.

格式:

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

参数:

1. loc (float or NDArray, optional)
   – Mean (centre) of the distribution.
   - 均值
2. scale (float or NDArray, optional)
   – Standard deviation of the distribution.
   - 标准差
3. shape (int or tuple of ints, optional)
   – The number of samples to draw.
   - 实例数
4. dtype ({'float16', 'float32', 'float64'}, optional)
   – Data type of output samples. Default is ‘float32’
   - 数据类型
5. 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.

实例:

>>> 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)>
# 标准差为1的正态分布
>>> mx.nd.random.normal(scale=1, shape=(2, 3))
[[ 1.1017746   1.1337967   1.1405879 ]
 [ 1.2673576  -2.0345824  -0.32537818]]
<NDArray 2x3 @cpu(0)>

randn¶

格式:

mxnet.ndarray.random.randn(*shape, **kwargs)

实例:

>>> 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)>

poisson¶

说明:

Draw random samples from a Poisson distribution.
从泊松分布中抽取随机样本。

格式:

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

实例:

>>> 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)>