49 lines
1.2 KiB
Julia
49 lines
1.2 KiB
Julia
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using LinearAlgebra
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using Distributions
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using Random
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using Zygote
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using Plots
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Random.seed!(0);
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function normalgenerator(amount::Number,μ::Number,σ::Number,lowerbound::Number=0,upperbound::Number=1)
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return rand(Truncated(Normal(μ,σ),lowerbound,upperbound),amount)
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end
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function noisyline(intercept::Number,slope::Number,samples::Number,μ::Number,σ::Number,lb::Number=0,ub::Number=1)
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noise = normalgenerator(samples,μ,σ,lb,ub)
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exes = Array{Float64,1}(undef,samples)
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for i in 1:samples
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exes[i] = i
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end
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line = slope .* exes .+ intercept
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y = noise .+ line
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w = ones(samples)
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X = hcat(exes,w)
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return X,y
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end
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function MSE(X::Array,y::Array,w::Array)
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return mean((y - X * w).^2)
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end
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function gradient_descent(X::Array,y::Array,α::Number,w::Array,iter::Number)
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costs = Array{Float64,1}(undef,iter)
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for i in 1:iter
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costs[i] = MSE(X,y,w)
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∇X, ∇y, ∇w = gradient(MSE,X,y,w)
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w = w - α * ∇w
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end
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return w,costs
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end
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function get_res_line(X::Array,result::Array)
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return result[1] .* X[:,1] .+ result[2]
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end
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X,y = noisyline(2,4,100,0,1);
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N,D = size(X);
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w = ones(D);
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pred,cost = gradient_descent(X,y,0.0001,w,6);
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