mars.tensor.random.triangular#
- mars.tensor.random.triangular(left, mode, right, size=None, chunk_size=None, gpu=None, dtype=None)[源代码]#
Draw samples from the triangular distribution over the interval
[left, right]
.The triangular distribution is a continuous probability distribution with lower limit left, peak at mode, and upper limit right. Unlike the other distributions, these parameters directly define the shape of the pdf.
- 参数
left (float or array_like of floats) – Lower limit.
mode (float or array_like of floats) – The value where the peak of the distribution occurs. The value should fulfill the condition
left <= mode <= right
.right (float or array_like of floats) – Upper limit, should be larger than left.
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifleft
,mode
, andright
are all scalars. Otherwise,mt.broadcast(left, mode, right).size
samples are drawn.chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension
gpu (bool, optional) – Allocate the tensor on GPU if True, False as default
dtype (data-type, optional) – Data-type of the returned tensor.
- 返回
out – Drawn samples from the parameterized triangular distribution.
- 返回类型
Tensor or scalar
备注
The probability density function for the triangular distribution is
\[\begin{split}P(x;l, m, r) = \begin{cases} \frac{2(x-l)}{(r-l)(m-l)}& \text{for $l \leq x \leq m$},\\ \frac{2(r-x)}{(r-l)(r-m)}& \text{for $m \leq x \leq r$},\\ 0& \text{otherwise}. \end{cases}\end{split}\]The triangular distribution is often used in ill-defined problems where the underlying distribution is not known, but some knowledge of the limits and mode exists. Often it is used in simulations.
引用
- 1
Wikipedia, “Triangular distribution” http://en.wikipedia.org/wiki/Triangular_distribution
示例
Draw values from the distribution and plot the histogram:
>>> import matplotlib.pyplot as plt >>> import mars.tensor as mt >>> h = plt.hist(mt.random.triangular(-3, 0, 8, 100000).execute(), bins=200, ... normed=True) >>> plt.show()