uadapy package

Subpackages

• uadapy.dr package
  • uadapy.dr.uamds module
    • apply_uamds()
    • convert_xform_affine_to_uamds()
    • convert_xform_uamds_to_affine()
    • get_means_covs()
    • gradient()
    • iterate_simple_gradient_descent()
    • minimize_scipy()
    • mk_normal_distr_spec()
    • perform_projection()
    • precalculate_constants()
    • stress()
    • uamds()
  • uadapy.dr.uapca module
    • compute_ua_cov()
    • compute_uapca()
    • transform_uapca()
    • uapca()
  • Module contents
• uadapy.plotting package
  • Submodules
  • uadapy.plotting.distribution_plot module
    • InteractiveNormal
      • InteractiveNormal.get_ind_under_point()
      • InteractiveNormal.init_points()
      • InteractiveNormal.update()
    • confidence_ellipse()
  • uadapy.plotting.interactive_splom module
  • uadapy.plotting.plots2D module
    • generate_random_colors()
    • generate_spectrum_colors()
    • plot_contour()
    • plot_contour_bands()
    • plot_samples()
  • uadapy.plotting.plotsND module
    • plot_contour()
    • plot_contour_samples()
    • plot_samples()
  • uadapy.plotting.utils module
    • generate_random_colors()
    • generate_spectrum_colors()
  • Module contents
• uadapy.data package
  • uadapy.data.data module
    • load_iris()
    • load_iris_normal()

Module contents

class uadapy.Distribution(model, name='', n_dims=1)

Bases: object

The Distribution class provides a consistent interface to a variety of distributions.

model

The underlying concrete distribution model, a scipy.stats distribution object or an array of samples

Type:

str

name

Name of the distribution type, e.g. ‘Normal’

Type:

str

n_dims

Dimensionality of the distribution

Type:

int

cov() → ndarray | float

Covariance of the distribution.

Returns:

Covariance of the distribution.

Return type:

np.ndarray or float

kurt() → ndarray | float

Kurtosis of the distribution.

Returns:

Kurtosis of the distribution.

Return type:

np.ndarray or float

mean() → ndarray | float

Expected value of the distribution.

Returns:

Expected value of the distribution.

Return type:

np.ndarray or float

pdf(x: ndarray | float) → ndarray | float

Computes the probability density function.

Parameters:

x (np.ndarray or float) – The position where the pdf should be evaluated.

Returns:

Samples of the distribution.

Return type:

np.ndarray or float

sample(n: int, seed: int | None = None) → ndarray

Creates samples from the distribution.

Parameters:

• n (int) – Number of samples.

• seed (int, optional) – Seed for the random number generator for reproducibility, default is None.

Returns:

Samples of the distribution.

Return type:

np.ndarray

skew() → ndarray | float

Skewness of the distribution.

Returns:

Skewness of the distribution.

Return type:

np.ndarray or float