def train_loglin(trainX, trainY, reg_const):
D, V = trainX.shape
K = 2 #two classes
#print 'K={0}, D={1}, V={2}'.format(K, D, V)
assert trainX.shape[0] == len(trainY)
beta = loglinmm.get_beta(trainX)
gamma0 = np.random.rand(K, V)
#PYLBFGS stuff
bb = LBFGS()
bb.orthantwise_c = 2.0**reg_const
bb.linesearch = 'wolfe'
bb.epsilon = 1e-01
bb.delta = 1e-01
#find optimum
gamma_opt = bb.minimize(loglinmm.negll, gamma0, None,
[beta, trainX, trainY])
w_given_class = loglinmm.prob_w_given_k(beta, gamma_opt)
return w_given_class
