Evaluate a trained GEV CDN model, resulting in a matrix with columns corresponding to the location, scale, and shape parameters of the GEV distribution.

1 |

`x` |
covariate matrix with number of rows equal to the number of samples and number of columns equal to the number of variables. |

`weights` |
list containing GEV CDN input-hidden and hidden-output layer weight matrices from |

a matrix with number of rows equal to that of `x`

and columns
corresponding to the GEV location, scale, and shape parameters.

Cannon, A.J., 2010. A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology. Hydrological Processes, 24: 673-685. DOI: 10.1002/hyp.7506

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# Generate synthetic data
x <- as.matrix(1:50)
y <- as.matrix(rgev(length(x), location = 0, scale = 1, shape = 0.2))
## Fit stationary model
weights <- gevcdn.fit(x = x, y = y, Th = gevcdn.identity,
fixed = c("location", "scale", "shape"))
## Evaluate GEV parameters
parms <- gevcdn.evaluate(x, weights)
cat("GEV parameters", parms[1,], "\n")
``` |

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