Types
We'll work with may types in this package, including some abstract types that will be used to define the structure of our models.
VLDataScienceMachineLearningPackage.MySarcasmRecordModel
— TypeMySarcasmRecordModel <: AbstractTextRecordModel
Model for a record in the Sarcasm dataset.
Fields
data::Array{String, Any}
- The data found in the record in the order they were found
VLDataScienceMachineLearningPackage.MySarcasmRecordCorpusModel
— TypeMySarcasmRecordCorpusModel <: AbstractTextDocumentCorpusModel
Model for a collection of records in the Sarcasm dataset.
Fields
records::Dict{Int, MySarcasmRecordModel}
- The records in the document (collection of records)tokens::Dict{String, Int64}
- A dictionary of tokens in alphabetical order (key: token, value: position) for the entire document
VLDataScienceMachineLearningPackage.MySMSSpamHamRecordModel
— TypeMySMSSpamHamRecordModel <: AbstractTextRecordModel
Model for a record in the SMS Spam Ham dataset.
Fields
isspam::Bool
- a boolean value indicating if the message is spam.message::String
- the content of the SMS message.
VLDataScienceMachineLearningPackage.MySMSSpamHamRecordCorpusModel
— TypeMySMSSpamHamRecordCorpusModel <: AbstractTextDocumentCorpusModel
Model for a collection of records in the SMS Spam Ham dataset.
Fields
records::Dict{Int, MySMSSpamHamRecordModel}
- The records in the document (collection of records)tokens::Dict{String, Int64}
- A dictionary of tokens in alphabetical order (key: token, value: position) for the entire document
VLDataScienceMachineLearningPackage.MyFullGeneralAdjacencyTree
— Typemutable struct MyFullGeneralAdjacencyTree <: AbstractTreeModel
The MyFullGeneralAdjacencyTree
type is a model of a full general adjacency tree that uses a dictionary to store the tree structure. There is a build and populate! method to build the tree and populate it with data.
Fields
data::Union{Nothing, Dict{Int64,NamedTuple}}
- A dictionary that stores the node data for the tree.connectivity::Dict{Int64,Array{Int64,1}}
- A dictionary that stores the connectivity information between nodes.h::Int64
- The height of the tree.n::Int64
- The branching factor of the tree.
VLDataScienceMachineLearningPackage.MyAdjacencyRecombiningCommodityPriceTree
— Typemutable struct MyAdjacencyRecombiningCommodityPriceTree <: AbstractPriceTreeModel
The MyAdjacencyRecombiningCommodityPriceTree
type is a model of a commodity price tree that uses an dictionary to store the price data. This model stores the connectivity information between nodes.
Fields
data::Union{Nothing, Dict{Int64,NamedTuple}}
- A dictionary that stores the price data and path informationfor the tree.connectivity::Dict{Int64,Array{Int64,1}}
- A dictionary that stores the connectivity information between nodes.
VLDataScienceMachineLearningPackage.MyOneDimensionalElementaryWolframRuleModel
— Typemutable struct MyOneDimensionalElementaryWolframRuleModel <: AbstractRuleModel
The MyOneDimensionalElementarWolframRuleModel
mutable struct represents a one-dimensional elementary Wolfram rule model.
Fields
index::Int
- The index of the ruleradius::Int
- The radius, i.e, the number of cells that influence the next state for this rulerule::Dict{Int,Int}
- A dictionary that holds the rule where thekey
is the binary representation of the neighborhood and thevalue
is the next state
VLDataScienceMachineLearningPackage.MyGraphNodeModel
— Typemutable struct MyGraphNodeModel
A lightweight mutable node model used in simple graph representations.
Fields
id::Int64
- Unique integer identifier for the node.
VLDataScienceMachineLearningPackage.MyGraphEdgeModel
— Typemutable struct MyGraphEdgeModel
A mutable edge model representing a directed or undirected connection between nodes. The model stores a numeric id, endpoint indices, and an optional numeric weight.
Fields
id::Int64
- Unique integer identifier for the edge.source::Int64
- Identifier of the source node (or one endpoint).target::Int64
- Identifier of the target node (or the other endpoint).weight::Union{Nothing, Any}
- Optional edge weight; nothing indicates an unweighted edge.
VLDataScienceMachineLearningPackage.MySimpleDirectedGraphModel
— Typemutable struct MySimpleDirectedGraphModel
A minimal mutable directed graph container that keeps node and edge registries and a children adjacency map for fast traversal of outgoing neighbors.
Fields
nodes::Union{Nothing, Dict{Int64, MyGraphNodeModel}}
- Optional mapping from node id to MyGraphNodeModel. Use nothing when uninitialized.edges::Union{Nothing, Dict{Tuple{Int, Int}, Int64}}
- Optional mapping from (source, target) tuple to edge id. Use nothing when uninitialized.children::Union{Nothing, Dict{Int64, Set{Int64}}}
- Optional adjacency map from a node id to the set of its child (outgoing) node ids.
VLDataScienceMachineLearningPackage.MySimpleUndirectedGraphModel
— Typemutable struct MySimpleUndirectedGraphModel
A minimal mutable undirected graph container that keeps node and edge registries and a children adjacency map for fast traversal of outgoing neighbors.
Fields
nodes::Union{Nothing, Dict{Int64, MyGraphNodeModel}}
- Optional mapping from node id to MyGraphNodeModel. Use nothing when uninitialized.edges::Union{Nothing, Dict{Tuple{Int, Int}, Int64}}
- Optional mapping from (source, target) tuple to edge id. Use nothing when uninitialized.children::Union{Nothing, Dict{Int64, Set{Int64}}}
- Optional adjacency map from a node id to the set of its child (outgoing) node ids.