copulas_gaussian_multivariate_fit_and_sample_synthetic_data.py

1from copulas.datasets import sample_trivariate_xyz
2from copulas.multivariate import GaussianMultivariate
3from copulas.visualization import plot_3d
4
5# Load a dataset with 3 columns (x, y, z) that have dependencies
6data = sample_trivariate_xyz()
7
8# Center and scale the data (optional, but recommended for Gaussian Copula)
9# The copula will automatically handle marginal distributions
10copula = GaussianMultivariate()
11copula.fit(data)
12
13# Sample new data from the fitted model
14synthetic_data = copula.sample(len(data))
15
16# Plot the real data vs synthetic data to compare
17# (Note: This requires matplotlib installed)
18plot_3d(data, label='Real Data')
19plot_3d(synthetic_data, label='Synthetic Data')