Example usage

[jaraheel]

`
using DelimitedFiles
using MLJLinearModels

#############################################
'''
wrapper for the logistic regression function with elastic
net penalty provided by MLJLinearModels
'''
#############################################
function elastic_net_logistic_regression(Z::Matrix{Float64},
y::Vector{Float64},
λ::Float64 = 1.0,
α::Float64 = 0.0)
model = LogisticRegression(λ, α, penalty=:en)
return MLJLinearModels.fit(model, Z, y)
end

#############################################
'''
as MLJLinearModels require the labels to be +1, -1 instead
of 0, +1, the following code will read the data and the
labels (0, +1). Then it shall convert the labels to +1, -1
'''
#############################################
M = readdlm("train_data.txt", Float64)
n, m = size(M)
X = Matrix(M[1:n,1:m-1])
y = M[1:end,m:end][:]
z = copy(y) # z shall be the vector of labels
for i = 1:n
if y[i] == 0.0
z[i] = -1.0
end
end

#############################################
'''
compute the parameter vector on the training data
'''
#############################################
function train(X, y)
m, n = size(X)
β_bar = elastic_net_logistic_regression(X, y, 0.0, 0.0)[1:n] # ignore the intercept
return β_bar
end

#############################################
'''
make predictions after training
'''
#############################################
function generate_predictions(X, β)
n, m = size(X)
pred_y = zeros(n)
for i = 1:n
p = sigmoid(X[i,:], β)
if isinf(p)
println("error. sigmoid returned Inf")
end
if p >= 0.5
pred_y[i] = 1.0
else
pred_y[i] = -1.0
end
end
return pred_y
end

`