def choose_lambda(self):
        '''train with different regularization parameters and choose
           the one that minimizes the cost in the evaluation set.'''        
        tinit = 0.005* np.random.rand(self.LABS, self.N)

        # initialize some working vars
        #rango = np.array([1e-3, 1e-2, 1e-1, 1, 10, 100])
        rango = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10])
        Jt, Je = np.array([]), np.array([])
        bestC, bestL = 1e+10, 0.0

        # cycle through lambdas and choose the one with lowest cost
        for chosen_lambda in rango:
            t = soft.optimizeThetas(tinit, self.xt, self.gt, \
                numLabels=self.LABS, l=chosen_lambda, visual=True)

            cost_t = soft.j(t, self.xt, self.gt, self.LABS, chosen_lambda)
            cost_e = soft.j(t, self.xe, self.ge, self.LABS, chosen_lambda)

            Jt = np.append(Jt, cost_t)
            Je = np.append(Je, cost_e)

            print 'chosen_lambda:', chosen_lambda
            if cost_e < bestC:
                bestC = cost_e
                bestL = chosen_lambda
                print '_______________new best is', bestL, 'with cost_e', cost_e
        print "\n\nthe best lambda is", bestL

        # plot
        #line1 = plt.plot(np.log10(rango), Jt)
        #line2 = plt.plot(np.log10(rango), Je)
        line1 = plt.plot(rango, Jt)
        line2 = plt.plot(rango, Je)
        plt.setp(line1, linewidth=2.0, label='training', color='b', solid_joinstyle='round')
        plt.setp(line2, linewidth=2.0, label='training', color='r', solid_joinstyle='round')
        plt.xlabel('Lambda')
        plt.ylabel('J')
        plt.show()
        pass
    def learning_curves(self):
        tinit = 0.005* np.random.rand(self.LABS, self.N)
        m, n = self.xt.shape
        sample = np.array([3, 6, 9, 12, 15, 18, 21, 24, 27, 30])*1000
        Jt, Je = np.array([]), np.array([])
        
        for m in sample:
            my_t = soft.optimizeThetas(tinit, self.xt[0:m,:], self.gt[0:m,:], \
                numLabels=self.LABS, l=self.L, visual=False)
            
            Jt = np.append(Jt, soft.j(my_t, self.xt[0:m,:], self.gt[0:m,:], self.LABS, self.L))
            Je = np.append(Je, soft.j(my_t, self.xe, self.ge, self.LABS, self.L))

        # plot (m, Jtr) and (m, Jcv)
        line1 = plt.plot(sample, Jt)
        line2 = plt.plot(sample, Je)
        
        plt.setp(line1, linewidth=2.0, label='training', color='b', solid_joinstyle='round')
        plt.setp(line2, linewidth=2.0, label='training', color='r', solid_joinstyle='round')
        plt.xlabel('Number of Examples')
        plt.ylabel('Cost / Error')
        plt.show()
        pass