using PyPlot
using NtToolBox

fs = 10
Xm = X -> X-repeat(mean(X,1), outer=(size(X,1),1))
Cov = X -> Xm(X)'*Xm(X);

A = readdlm("NtToolBox/src/data/quantum.csv", ',')
A = A[randperm(size(A,1)),:]
X = A[:,1:end-1]
y = A[:,end];

y = rescale(y,-1,1);

I = find(mean(abs(X),1).>1e-1); X = X[:,I]
X = X-repeat(mean(X,1), outer=(size(X,1),1))
X = X ./ repeat( sqrt(sum(X.^2,1)/size(X,1)), outer=(size(X,1),1));

n,p = size(X);

I = randperm(n); I = I[1:500]
plot_multiclasses(X[I,:],y[I],disp_dim=3,ms=5);

L = (s,y) -> 1/n * sum( log( 1 + exp(-s.*y) ) )
E = (w,X,y) -> L(X*w,y)
theta = v -> 1 ./ (1+exp(-v))
nablaL = (s,r) -> - 1/n * y.* theta(-s.*y)
nablaE = (w,X,y) -> X'*nablaL(X*w,y);

include("NtSolutions/ml_4_sgd/exo1.jl");

# Insert your code here.

nablaEi = (w,i) -> -y[i] .* X[i,:] * theta( -y[i] * (X[i,:]'*w)  );

l0 = 100
tau0 = .05;

include("NtSolutions/ml_4_sgd/exo2.jl");

# Insert your code here.

include("NtSolutions/ml_4_sgd/exo3.jl");

# Insert your code here.

include("NtSolutions/ml_4_sgd/exo4.jl");

# Insert your code here.