Synthetic Data (Univariate)

With the fitted model it allows to generate synthetic data which can be easily done with the generate function.

Lets see how generating samples works:

# import library
from distfit import distfit

# Generate random normal distributed data
X = np.random.normal(0, 2, 10000)

# Initialize model
dfit = distfit()

# Fit distribution on data X
dfit.fit_transform(X)

# The fitted distribution can now be used to generate new samples.
Xgenerate = dfit.generate(n=1000)

Synthetic Data (Multivariate)

# import library
from distfit import distfit

# Generate random normal distributed data
rng = np.random.default_rng(42)
mean = [0, 0]
cov = [[1, 0.6],
       [0.6, 1]]
X = rng.multivariate_normal(mean, cov, size=2000)


# Initialize model
dfit = distfit(multivariate=True)

# Fit distribution on data X
dfit.fit_transform(X)

# The fitted distribution can now be used to generate new samples.
Xgenerate = dfit.generate(n=1000)


array([[-1.34065007, -0.71402875],
       [-1.02143281, -0.11668787],
       [-1.34764167, -0.7042604 ],
       ...,
       [-0.52385614, -0.62469689],
       [-0.43946412,  0.19682527],
       [ 1.22849301,  0.11507201]], shape=(1000, 2))

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