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.
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.
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), then
m * n * k samples are drawn. If size is None (default),
a single value is returned if left, mode, and right
are all scalars. Otherwise, mt.broadcast(left, mode, right).size
samples are drawn.
(m, n, k)
m * n * k
mt.broadcast(left, mode, right).size
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.
out – Drawn samples from the parameterized triangular distribution.
Tensor or scalar
The probability density function for the triangular distribution is
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
Wikipedia, “Triangular distribution”
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,