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github.com/asymmetric-effort/datascience

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Path	Synopsis
gpu Package gpu provides a compute backend abstraction for accelerated factor operations.	Package gpu provides a compute backend abstraction for accelerated factor operations.
graphgo Package graphgo provides graph data structures and algorithms including directed and undirected graphs, topological sort, d-separation, clique finding, and moralization.	Package graphgo provides graph data structures and algorithms including directed and undirected graphs, topological sort, d-separation, clique finding, and moralization.
numgo Package numgo provides n-dimensional array operations, linear algebra, and numerical primitives.	Package numgo provides n-dimensional array operations, linear algebra, and numerical primitives.
internal/blas
pgm
base Package base provides the foundational graph types used by all datascience models: DAG, PDAG, UndirectedGraph, ADMG, MAG, and SimpleCausalModel.	Package base provides the foundational graph types used by all datascience models: DAG, PDAG, UndirectedGraph, ADMG, MAG, and SimpleCausalModel.
ci_tests Package ci_tests provides conditional independence tests for discrete, continuous, and multivariate data used in structure learning.	Package ci_tests provides conditional independence tests for discrete, continuous, and multivariate data used in structure learning.
config
factors Package factors provides discrete and continuous factor representations for probabilistic graphical models.	Package factors provides discrete and continuous factor representations for probabilistic graphical models.
identification Package identification provides causal effect identification algorithms including back-door adjustment and front-door criterion.	Package identification provides causal effect identification algorithms including back-door adjustment and front-door criterion.
independencies Package independencies provides representations for conditional independence assertions and independence relations.	Package independencies provides representations for conditional independence assertions and independence relations.
inference Package inference provides exact and approximate inference algorithms including variable elimination and belief propagation.	Package inference provides exact and approximate inference algorithms including variable elimination and belief propagation.
learning Package learning provides parameter estimation and structure learning algorithms for probabilistic graphical models.	Package learning provides parameter estimation and structure learning algorithms for probabilistic graphical models.
metrics Package metrics provides model evaluation functions including structural Hamming distance, confusion matrices, correlation scores, and Fisher's C.	Package metrics provides model evaluation functions including structural Hamming distance, confusion matrices, correlation scores, and Fisher's C.
models Package models provides graphical model structures including Bayesian networks, Markov networks, and factor graphs.	Package models provides graphical model structures including Bayesian networks, Markov networks, and factor graphs.
prediction Package prediction provides causal prediction methods including DoubleML, naive adjustment regression, and instrumental variable regression.	Package prediction provides causal prediction methods including DoubleML, naive adjustment regression, and instrumental variable regression.
readwrite Package readwrite provides readers and writers for probabilistic model file formats: BIF, XMLBIF, NET, UAI, XDSL, PomdpX, XBN, CSV, JSON, and datascience-native XML.	Package readwrite provides readers and writers for probabilistic model file formats: BIF, XMLBIF, NET, UAI, XDSL, PomdpX, XBN, CSV, JSON, and datascience-native XML.
sampling Package sampling provides MCMC and other sampling-based methods for approximate inference.	Package sampling provides MCMC and other sampling-based methods for approximate inference.
structure_score Package structure_score provides scoring functions for structure learning including BIC, AIC, BDeu, BDs, K2, and log-likelihood variants for discrete, Gaussian, and conditional Gaussian data.	Package structure_score provides scoring functions for structure learning including BIC, AIC, BDeu, BDs, K2, and log-likelihood variants for discrete, Gaussian, and conditional Gaussian data.
utils Package utils provides shared utilities for datascience including parsing, optimization helpers, and compatibility functions.	Package utils provides shared utilities for datascience including parsing, optimization helpers, and compatibility functions.
scigo Package scigo provides scientific computing primitives including statistical distributions, optimization, and special functions.	Package scigo provides scientific computing primitives including statistical distributions, optimization, and special functions.
tabgo Package tabgo provides tabular data structures and operations for loading, filtering, grouping, and transforming columnar data.	Package tabgo provides tabular data structures and operations for loading, filtering, grouping, and transforming columnar data.
tensorflow
data Package data provides a data pipeline for feeding arrays to models, analogous to tf.data.Dataset.	Package data provides a data pipeline for feeding arrays to models, analogous to tf.data.Dataset.
gradtape Package gradtape implements reverse-mode automatic differentiation, analogous to tf.GradientTape.	Package gradtape implements reverse-mode automatic differentiation, analogous to tf.GradientTape.
image Package image provides image manipulation operations on NDArrays, analogous to tf.image.	Package image provides image manipulation operations on NDArrays, analogous to tf.image.
initializer Package initializer provides weight initialization strategies, analogous to tf.initializers / tf.keras.initializers.	Package initializer provides weight initialization strategies, analogous to tf.initializers / tf.keras.initializers.
io/model Package model provides model serialization (save/load) for go-tensorflow, analogous to tf.keras.models.save_model / load_model.	Package model provides model serialization (save/load) for go-tensorflow, analogous to tf.keras.models.save_model / load_model.
keras Package keras provides high-level model building and training APIs, analogous to tf.keras.	Package keras provides high-level model building and training APIs, analogous to tf.keras.
keras/callbacks Package callbacks provides training callbacks, analogous to tf.keras.callbacks.	Package callbacks provides training callbacks, analogous to tf.keras.callbacks.
keras/metrics Package metrics provides evaluation metrics for model performance, analogous to tf.keras.metrics.	Package metrics provides evaluation metrics for model performance, analogous to tf.keras.metrics.
keras/regularizers Package regularizers provides weight regularization functions, analogous to tf.keras.regularizers.	Package regularizers provides weight regularization functions, analogous to tf.keras.regularizers.
keras/schedule Package schedule provides learning rate schedule functions, analogous to tf.keras.optimizers.schedules.	Package schedule provides learning rate schedule functions, analogous to tf.keras.optimizers.schedules.
nn/activation Package activation provides activation functions for neural network layers, analogous to tf.nn.relu, tf.nn.sigmoid, tf.nn.softmax, tf.nn.tanh.	Package activation provides activation functions for neural network layers, analogous to tf.nn.relu, tf.nn.sigmoid, tf.nn.softmax, tf.nn.tanh.
nn/layer Package layer provides neural network layers, analogous to tf.keras.layers.	Package layer provides neural network layers, analogous to tf.keras.layers.
nn/loss Package loss provides loss functions for training neural networks, analogous to tf.keras.losses.	Package loss provides loss functions for training neural networks, analogous to tf.keras.losses.
nn/optimizer Package optimizer provides optimization algorithms for training neural networks, analogous to tf.keras.optimizers.	Package optimizer provides optimization algorithms for training neural networks, analogous to tf.keras.optimizers.
variable Package variable provides a trainable variable type, analogous to tf.Variable.	Package variable provides a trainable variable type, analogous to tf.Variable.