def prob_5_2_2(N, d, k, sigma, lam = 300, seed=12231):
# N = 50, d = 75, k = 5, sigma = 10
#lambda^* = 300
(y, X, wg, eps) = dg.generate_data(N, d, k, sigma, seed)
##########
w = randn(1 + d)
prob = ls.initialize_computation(X, w, y)
prob[3] = lam
ls.round_robin(prob, 1000)
return (prob, sum(abs(prob[0][1:k + 1]) > 0), sum(abs(prob[0][1:]) > 0))
def prob_5_2_1(N, d, k, sigma, rat=9/10., seed=12231):
# N = 50, d = 75, k = 5, sigma = 1
(y, X, wg, eps) = dg.generate_data(N, d, k, sigma, seed)
##########
w = randn(1 + d)
prob = ls.initialize_computation(X, w, y)
ret = []
coeffs = []
for i in range(100):
ls.round_robin(prob, 1000)
ret.append([prob[3], sum(abs(prob[0][1:k + 1]) > 0), sum(abs(prob[0][1:]) > 0)])
coeffs.append(prob[0].copy())
prob[3] *= rat
ret = array(ret)
coeffs = array(coeffs)
#Precision
ret[:, 2] = ret[:, 1]/ret[:, 2]
#Recall
ret[:, 1] = ret[:, 1]/float(k)
return (prob, array(ret), coeffs)