Reference

These pages provide a reference for repliclust.

The diagram below shows our object-oriented software architecture from a big picture perspective.

A DataGenerator depends on one or several Archetype’s to produce synthetic data sets. Each Archetype generates MixtureModel’s which, in turn, sample ready-to-use synthetic data sets.

In the diagram, each box represents an object. A solid arrow A <– B means that B is an attribute of A, while a dashed arrow X - -> Y means that X randomly samples instances of Y. The objects of type CovarianceSampler, ClusterCenterSampler, GroupSizeSampler, and DistributionMix are the modular building blocks an archetype uses to sample mixture models.

The API reference below allows you to look up the definitions of individual classes and functions in repliclust.

• Core Framework
  • repliclust.base
    • Archetype
      • Archetype.sample_mixture_model()
      • Archetype.synthesize()
    • ClusterCenterSampler
      • ClusterCenterSampler.sample_cluster_centers()
    • CovarianceSampler
      • CovarianceSampler.sample_covariances()
    • DataGenerator
      • DataGenerator.synthesize()
    • DistributionMix
      • DistributionMix.assign_distributions()
    • GroupSizeSampler
      • GroupSizeSampler.sample_group_sizes()
    • MixtureModel
      • MixtureModel.sample_data()
    • SingleClusterDistribution
      • SingleClusterDistribution.sample_cluster()
    • get_supported_distributions()
    • set_seed()
• Max-Min Implementation
  • repliclust.maxmin
  • repliclust.maxmin.archetype
    • MaxMinArchetype
      • MaxMinArchetype.create_another_like_this()
      • MaxMinArchetype.describe()
      • MaxMinArchetype.edit_params()
      • MaxMinArchetype.from_verbal_description()
      • MaxMinArchetype.guess_learning_rate()
      • MaxMinArchetype.sample_hyperparams()
    • parse_distribution_selection()
    • validate_archetype_args()
    • validate_maxmin_ratios()
    • validate_overlaps()
    • validate_reference_quantity()
  • repliclust.maxmin.covariance
    • MaxMinCovarianceSampler
      • MaxMinCovarianceSampler.aspect_ref
      • MaxMinCovarianceSampler.aspect_maxmin
      • MaxMinCovarianceSampler.radius_maxmin
      • MaxMinCovarianceSampler.make_axis_lengths()
      • MaxMinCovarianceSampler.make_cluster_aspect_ratios()
      • MaxMinCovarianceSampler.make_cluster_radii()
      • MaxMinCovarianceSampler.sample_covariances()
      • MaxMinCovarianceSampler.validate_k()
  • repliclust.maxmin.groupsizes
    • MaxMinGroupSizeSampler
      • MaxMinGroupSizeSampler.imbalance_ratio
      • MaxMinGroupSizeSampler.__init__()
      • MaxMinGroupSizeSampler.make_group_sizes()
      • MaxMinGroupSizeSampler.sample_group_sizes()
• Cluster Overlap Control
  • repliclust.overlap.centers
    • ConstrainedOverlapCenters
      • ConstrainedOverlapCenters.sample_cluster_centers()
    • assess_obs_overlap()
    • overlap2quantile_vec()
    • quantile2overlap_vec()
• Probability Distributions
  • repliclust.distributions
    • DistributionFromNumPy
    • DistributionFromPDF
    • Exponential
    • FixedProportionMix
      • FixedProportionMix._distributions
      • FixedProportionMix._proportions
      • FixedProportionMix.assign_distributions()
    • Normal
    • StandardT
    • parse_distribution()
• Distortion
  • repliclust.distortion
    • NeuralNetwork
      • NeuralNetwork.forward()
      • NeuralNetwork.training
    • construct_near_ortho_matrix()
    • distort()
    • wrap_around_sphere()
• Visualization
  • repliclust.viz
    • plot()