using Pkg; Pkg.activate("../."); Pkg.instantiate();
using IJulia; try IJulia.clear_output(); catch _ end

using Plots, LinearAlgebra, LaTeXStrings

# Parameters
Σ = 1e5 * Diagonal(I,3) # Covariance matrix of prior on w
σ2 = 2.0                # Noise variance

# Generate data set
w = [1.0; 2.0; 0.25]
N = 30
z = 10.0*rand(N)
x_train = [[1.0; z; z^2] for z in z] # Feature vector x = [1.0; z; z^2]
f(x) = (w'*x)[1]
y_train = map(f, x_train) + sqrt(σ2)*randn(N) # y[i] = w' * x[i] + ϵ
scatter(z, y_train, label="data", xlabel=L"z", ylabel=L"f([1.0, z, z^2]) + \epsilon")

using RxInfer, Random
# Build model
@model function linear_regression(y,x, N, Σ, σ2)

    w ~ MvNormalMeanCovariance(zeros(3),Σ)
    
    for i in 1:N
        y[i] ~ NormalMeanVariance(dot(w , x[i]), σ2)
    end
end
# Run message passing algorithm 
results = infer(
    model      = linear_regression(N=length(x_train), Σ=Σ, σ2=σ2),
    data       = (y = y_train, x = x_train),
    returnvars = (w = KeepLast(),),
    iterations = 20,
);
# Plot result
w = results.posteriors[:w]
println("Posterior distribution of w: $(w)")
plt = scatter(z, y_train, label="data", xlabel=L"z", ylabel=L"f([1.0, z, z^2]) + \epsilon")
z_test = collect(0:0.2:12)
x_test = [[1.0; z; z^2] for z in z_test]
for i=1:10
    w_sample = rand(results.posteriors[:w])
    f_est(x) = (w_sample'*x)[1]
    plt = plot!(z_test, map(f_est, x_test), alpha=0.3, label="");
end
display(plt)

println("Forward message on Z:")
@call_rule typeof(+)(:out, Marginalisation) (m_in1 = NormalMeanVariance(1.0, 1.0), m_in2 = NormalMeanVariance(2.0, 1.0))

println("Backward message on X:")
@call_rule typeof(+)(:in1, Marginalisation) (m_out = NormalMeanVariance(3.0, 1.0), m_in2 = NormalMeanVariance(2.0, 1.0))

println("Forward message on Y:")
@call_rule typeof(*)(:out, Marginalisation) (m_A = PointMass(4.0), m_in = NormalMeanVariance(1.0, 1.0))

println("Backward message on X:")
@call_rule typeof(*)(:in, Marginalisation) (m_out = NormalMeanVariance(2.0, 1.0), m_A = PointMass(4.0))

# Data
y1_hat = 1.0
y2_hat = 2.0

# Construct the factor graph
@model function my_model(y1,y2)

    # `x` is the hidden states
    x ~ NormalMeanVariance(0.0, 4.0)

    # `y1` and `y2` are "clamped" observations
    y1 ~ NormalMeanVariance(x, 1.0)
    y2 ~ NormalMeanVariance(x, 2.0)
    
    return x
end

result = infer(model=my_model(), data=(y1=y1_hat, y2 = y2_hat,))
println("Sum-product message passing result: p(x|y1,y2) = 𝒩($(mean(result.posteriors[:x])),$(var(result.posteriors[:x])))")

# Calculate mean and variance of p(x|y1,y2) manually by multiplying 3 Gaussians (see lesson 4 for details)
v = 1 / (1/4 + 1/1 + 1/2)
m = v * (0/4 + y1_hat/1.0 + y2_hat/2.0)
println("Manual result: p(x|y1,y2) = 𝒩($(m), $(v))")

open("../../styles/aipstyle.html") do f display("text/html", read(f, String)) end