chaospy.Clayton¶
- class chaospy.Clayton(dist, theta=2.0)[source]¶
Clayton Copula.
- Examples:
>>> distribution = chaospy.Clayton( ... chaospy.Iid(chaospy.Uniform(-1, 1), 2), theta=2) >>> distribution Clayton(Iid(Uniform(lower=-1, upper=1), 2), theta=2) >>> samples = distribution.sample(3) >>> samples.round(4) array([[ 0.3072, -0.77 , 0.9006], [ 0.2736, -0.3015, 0.1539]]) >>> distribution.pdf(samples).round(4) array([0.3679, 0.1855, 0.2665]) >>> distribution.fwd(samples).round(4) array([[0.6536, 0.115 , 0.9503], [0.4822, 0.8725, 0.2123]]) >>> mesh = numpy.meshgrid([.4, .5, .6], [.4, .5, .6]) >>> distribution.inv(mesh).round(4) array([[[-0.2 , 0. , 0.2 ], [-0.2 , 0. , 0.2 ], [-0.2 , 0. , 0.2 ]], [[-0.2008, -0.0431, 0.0945], [-0.0746, 0.0928, 0.2329], [ 0.0636, 0.2349, 0.3713]]])
- __init__(dist, theta=2.0)[source]¶
- Args:
- dist (Distribution):
The distribution to wrap
- theta (float):
Copula parameter. Required to be above 0.
Methods
pdf
(x_data[, decompose, allow_approx, step_size])Probability density function.
cdf
(x_data)Cumulative distribution function.
fwd
(x_data)Forward Rosenblatt transformation.
inv
(q_data[, max_iterations, tollerance])Inverse Rosenblatt transformation.
sample
([size, rule, antithetic, ...])Create pseudo-random generated samples.
mom
(K[, allow_approx])Raw statistical moments.
ttr
(kloc)Three terms relation's coefficient generator.
Attributes
Flag indicating that return value from the methods sample, and inv should be interpreted as integers instead of floating point.
Lower bound for the distribution.
True if distribution contains stochastically dependent components.
Upper bound for the distribution.