Created
February 28, 2022 14:09
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OLS_ML_3
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dim_input=2 #dim of input, without the intercept | |
dim_output=1 | |
# Normal noise | |
d = Normal() | |
# True parameters | |
beta = rand(d, dim_input + 1); | |
# Noise | |
e = rand(d, n_points); | |
# Input data: | |
X = rand(d, (n_points,dim_input)); | |
# Add the intercept: | |
X = hcat(ones(n_points),X); | |
#Linear Model | |
y = X*beta .+ e; | |
function obj_function(X,y,beta) | |
mean((y .- X*beta).^2 ) | |
end | |
beta_1_grid = collect(range(-10.0, 10.0, length=100)) | |
beta_2_grid = copy(beta_1_grid) | |
plot(beta_1_grid, beta_2_grid, (x,y) -> obj_function(X, y, [beta[1]; x; y]), st=:contour, colorbar_title=L"|X-y\hat{\beta}|^2") | |
scatter!([beta[2]], [beta[3]], markershape = :star5) | |
xlabel!(L"\beta_1") | |
ylabel!(L"\beta_2") | |
# refinement loop | |
beta_hat = [beta[1]; -9.0; 9.0] #fix the intercept at the true value. Random guess for beta_1 and beta_2 | |
grad_n = zeros(3) #initialize gradient | |
r = 1e-5 #learning rate | |
anim = @animate for i=1:50 | |
grad_OLS!(grad_n, beta_hat, X, y) | |
beta_hat[:] -= r*grad_n | |
scatter!([beta_hat[2]], [beta_hat[3]], legend=:none) | |
end | |
gif(anim,joinpath(dirname(@__FILE__),"convergence_GD_OLS_2d.gif"),fps=5) |
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