GMMN_trained: Trained Generative Moment Matching Networks

Description Usage Format Author(s) Source References See Also Examples

Description

Trained generative moment matching networks (GMMNs); see also the demo GMMN_QMC or the vignette GMMN_QMC.

Usage

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data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_C_tau_0.25")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_C_tau_0.5")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_C_tau_0.75")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_G_tau_0.25")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_G_tau_0.5")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_G_tau_0.75")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_eqmix_C_tau_0.5_rot90_t4_tau_0.5")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_eqmix_G_tau_0.5_rot90_t4_tau_0.5")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_eqmix_MO_0.75_0.6_rot90_t4_tau_0.5")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_MO_0.75_0.6")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.25")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.5")
data("GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.75")
data("GMMN_dim_3_300_3_ntrn_60000_nbat_5000_nepo_300_NC21_tau_0.25_0.5")
data("GMMN_dim_3_300_3_ntrn_60000_nbat_5000_nepo_300_NG21_tau_0.25_0.5")
data("GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_C_tau_0.5")
data("GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_G_tau_0.5")
data("GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_NC23_tau_0.25_0.5_0.75")
data("GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_NG23_tau_0.25_0.5_0.75")
data("GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.5")
data("GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_C_tau_0.5")
data("GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_G_tau_0.5")
data("GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_NC55_tau_0.25_0.5_0.75")
data("GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_NG55_tau_0.25_0.5_0.75")
data("GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.5")

Format

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_C_tau_0.25

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a bivariate Clayton copula (with parameter chosen such that Kendall's tau equals 0.25).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_C_tau_0.5

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a bivariate Clayton copula (with parameter chosen such that Kendall's tau equals 0.5).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_C_tau_0.75

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a bivariate Clayton copula (with parameter chosen such that Kendall's tau equals 0.75).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_G_tau_0.25

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a bivariate Gumbel copula (with parameter chosen such that Kendall's tau equals 0.25).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_G_tau_0.5

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a bivariate Gumbel copula (with parameter chosen such that Kendall's tau equals 0.5).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_G_tau_0.75

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a bivariate Gumbel copula (with parameter chosen such that Kendall's tau equals 0.75).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_eqmix_C_tau_0.5_rot90_t4_tau_0.5

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a bivariate half-half mixture of a Clayton copula (with parameter chosen such that Kendall's tau equals 0.5) and a rotated (by 90 degree) $t$ copula (with 4 degrees of freedom and correlation parameter chosen such that Kendall's tau equals 0.5); see vignette("GMMN_QRNG", package = "gnn").

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_eqmix_G_tau_0.5_rot90_t4_tau_0.5

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a bivariate half-half mixture of a Gumbel copula (with parameter chosen such that Kendall's tau equals 0.5) and a rotated (by 90 degree) $t$ copula (with 4 degrees of freedom and correlation parameter chosen such that Kendall's tau equals 0.5).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_eqmix_MO_0.75_0.6_rot90_t4_tau_0.5

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a bivariate half-half mixture of a Marshall–Olkin copula (with alpha_1 = 0.75 and alpha_2 = 0.60) and a rotated (by 90 degree) $t$ copula (with 4 degrees of freedom and correlation parameter chosen such that Kendall's tau equals 0.5).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_MO_0.75_0.6

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a Marshall–Olkin copula (with alpha_1=0.75 and alpha_2=0.60).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.25

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a two-dimensional $t$ copula (with 4 degrees of freedom and equi-correlation parameter chosen such that Kendall's tau equals 0.25).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.5

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a two-dimensional $t$ copula (with 4 degrees of freedom and equi-correlation parameter chosen such that Kendall's tau equals 0.5).

GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.75

raw R object representing a GMMN (input and output layer are two-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a two-dimensional $t$ copula (with 4 degrees of freedom and equi-correlation parameter chosen such that Kendall's tau equals 0.75).

GMMN_dim_3_300_3_ntrn_60000_nbat_5000_nepo_300_NC21_tau_0.25_0.5

raw R object representing a GMMN (input and output layer are three-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a three-dimensional nested Clayton copula (with sector dimensions 2 and 1, corresponding Kendall's tau 0.5 within the first sector and Kendall's tau 0.25 between the two sectors).

GMMN_dim_3_300_3_ntrn_60000_nbat_5000_nepo_300_NG21_tau_0.25_0.5

raw R object representing a GMMN (input and output layer are three-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a three-dimensional nested Gumbel copula (with sector dimensions 2 and 1, corresponding Kendall's tau 0.5 within the first sector and Kendall's tau 0.25 between the two sectors).

GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_C_tau_0.5

raw R object representing a GMMN (input and output layer are five-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a five-dimensional Clayton copula (with parameter chosen such that Kendall's tau equals 0.5).

GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_G_tau_0.5

raw R object representing a GMMN (input and output layer are five-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a five-dimensional Gumbel copula (with parameter chosen such that Kendall's tau equals 0.5).

GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_NC23_tau_0.25_0.5_0.75

raw R object representing a GMMN (input and output layer are five-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a five-dimensional nested Clayton copula (with sector dimensions 2 and 3, corresponding Kendall's tau 0.5 and 0.75, and Kendall's tau 0.25 between the two sectors).

GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_NG23_tau_0.25_0.5_0.75

raw R object representing a GMMN (input and output layer are five-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a five-dimensional nested Gumbel copula (with sector dimensions 2 and 3, corresponding Kendall's tau 0.5 and 0.75, and Kendall's tau 0.25 between the two sectors); see vignette("GMMN_QRNG", package = "gnn").

GMMN_dim_5_300_5_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.5

raw R object representing a GMMN (input and output layer are five-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a five-dimensional $t$ copula (with 4 degrees of freedom and equi-correlation parameter chosen such that Kendall's tau equals 0.5); see vignette("GMMN_QRNG", package = "gnn").

GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_C_tau_0.5

raw R object representing a GMMN (input and output layer are 10-dimensional, the single hiddenlayer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a 10-dimensional Clayton copula (with parameter chosen such that Kendall's tau equals 0.5).

GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_G_tau_0.5

raw R object representing a GMMN (input and output layer are 10-dimensional, the single hiddenlayer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a 10-dimensional Gumbel copula (with parameter chosen such that Kendall's tau equals 0.5).

GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_NC55_tau_0.25_0.5_0.75

raw R object representing a GMMN (input and output layer are 10-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a 10-dimensional nested Clayton copula (with sector dimensions 5 and 5, corresponding Kendall's tau 0.5 and 0.75, and Kendall's tau 0.25 between the two sectors).

GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_NG55_tau_0.25_0.5_0.75

raw R object representing a GMMN (input and output layer are 10-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a 10-dimensional nested Gumbel copula (with sector dimensions 5 and 5, corresponding Kendall's tau 0.5 and 0.75, and Kendall's tau 0.25 between the two sectors).

GMMN_dim_10_300_10_ntrn_60000_nbat_5000_nepo_300_t4_tau_0.5

raw R object representing a GMMN (input and output layer are 10-dimensional, the single hidden layer is 300-dimensional) trained on 60000 pseudo-samples (with batch size 5000 and 300 epochs) from a 10-dimensional $t$ copula (with 4 degrees of freedom and equi-correlation parameter chosen such that Kendall's tau equals 0.5).

Author(s)

Marius Hofert and Avinash Prasad

Source

GPU server with NVIDIA Tesla P100 GPUs.

References

Hofert, M., Prasad, A. and Zhu, M. (2018). Quasi-Monte Carlo for multivariate distributions via generative neural networks. (See https://arxiv.org/abs/1811.00683 for an early version)

See Also

GMMN_model(), to_callable()

Examples

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 # to avoid win-builder error "Error: Installation of TensorFlow not found"
## Load a trained GMMN (see train_once())
NNname <- "GMMN_dim_2_300_2_ntrn_60000_nbat_5000_nepo_300_eqmix_C_tau_0.5_rot90_t4_tau_0.5"
NN <- read_rda(NNname, package = "gnn")
GMMN1 <- to_callable(NN)
str(GMMN1)

## Alternative
NNnm <- data(list = NNname)
GMMN2 <- to_callable(get(NNnm))
str(GMMN2)

## Check (the check-able components)
stopifnot(identical(GMMN1[names(GMMN1) != "model"],
                    GMMN2[names(GMMN2) != "model"]))

## Evaluate
set.seed(271)
N.prior <- matrix(rnorm(2000 * 2), ncol = 2)
X <- predict(GMMN1[["model"]], x = N.prior)
plot(X, xlab = expression(X[1]), ylab = expression(X[2]))

gnn documentation built on March 13, 2020, 5:07 p.m.

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