GaussianDiscriminantAnalysis.jl

Installation

] add https://github.com/mossr/GaussianDiscriminantAnalysis.jl

Quadratic discriminant analysis

using GaussianDiscriminantAnalysis

# 𝒟::Vector{Tuple{Array, Int}}
𝒟 = generate_example_data(100, seed=0xC0FFEE)
predict, mv₀, mv₁ = qda(𝒟)

Linear discriminant analysis

predict, mv₀, mv₁ = lda(𝒟)

Plotting

gdaplot(𝒟;
        soft=true,            # soft/hard prediction boundary
        use_qda=true,         # QDA or LDA
        k=1,                  # specify which class k to share covariance (LDA only)
        rev=false,            # reverse "positive" and "negative"
        heatmap=false,        # use heatmap instead of filled contours
        levels=100,           # number of levels for the filled contours
        show_axes=true,       # toggle displaying of axes
        subplots=false,       # include single-dimensional Gaussian fits in subplots
        show_svm=false,       # show SVM decision boundary
        show_analysis=false,  # print out goodness of prediction
        show_legend=true,     # toggle showing of legend
        return_predict=false) # return (fig, predict) instead of just (fig)

Plotting examples

gdaplot(𝒟)

gdaplot(𝒟, heatmap=true)

gdaplot(𝒟, rev=true)

gdaplot(𝒟, subplots=true)

gdaplot(𝒟, show_svm=true)

gdaplot(𝒟, soft=false)

gdaplot(𝒟, use_qda=false)

gdaplot(𝒟, use_qda=false, k=2)

gdaplot(𝒟, use_qda=false, k=2, soft=false

Robert Moss