DiscretePredictors.jl

A Julia package for online discrete sequence prediction.

API Overview:

• p = Predictor{SymbolType}(parameters...) creates a predictor instance p with prediction algorithm Predictor (see choices below) of symbols of SymbolType with specified the parameters
• add!( p, sym ) adds a symbol to the model
• predict( p ) computes a probability distribution over the symbols seen so far by the model and returns it as dictionary of Dict{SymbolType,Float64}
• info_string( p ) returns a human readable string about the predictor p
• unique_string( p ) returns a unique string for the predictor p with paramter configuration
• get_best_symbol( p ) returns the symbol of type SymbolType with highest probability under current context
• size( p ) returns the number of nodes in current prediction model p.model

Available Predictors:

• Adaptive MPP (AdaptiveMPP)
• Active LeZi (ALZ)
• Dependency Graph (DG)
• Discounted HEDGE on KOM (dHedgePPM)
• Error Weighted PPM (ewPPM)
• K-th Order Markov Model (KOM)
• LeZi-Update (LeZiUpdate)
• LeZi78 (LZ78)

Example

# Initialize a predictor
julia> p = KOM{Char}(4)
DiscretePredictors.KOM{Char}([*] (0)
, Char[], 4)

# Add some symbols
julia> add!( p, 'a' )

julia> add!( p, 'b' )

julia> add!( p, 'c' )

julia> add!( p, 'b' )

# Print out the model
julia> p
DiscretePredictors.KOM{Char}([*] (4)
+---[b] (2)
     +---[c] (1)
          +---[b] (1)
+---[a] (1)
     +---[b] (1)
          +---[c] (1)
               +---[b] (1)
+---[c] (1)
     +---[b] (1)
, ['a', 'b', 'c', 'b'], 4)

# Get prediction
julia> predict( p )
Dict{Char,Float64} with 3 entries:
  'b' => 0.25
  'a' => 0.125
  'c' => 0.625

# Get best symbol
julia> get_best_symbol( p )
'c': ASCII/Unicode U+0063 (category Ll: Letter, lowercase)

julia> info_string( p )
"KOM(4)"

julia> unique_string( p )
"KOM_04"

julia> size( p )
10