L2 Regularized Non-negative logistic regression

nnlr

import scipy as sp
from scipy import optimize as opt
def nnlr(X, y, C):
    """
    Non-negative Logistic Regression with L2 regularizer
    """
    def lr_cost(X, y, theta, C):
        m = len(y)
        return (1./m) * (sp.dot(-y, sp.log(sigmoid(sp.dot(X, theta)))) \
              - sp.dot((1-y), sp.log(1 - sigmoid(sp.dot(X, theta)))) \
              + 2 * C * sp.dot(theta, theta))
    
    def lr_grad(X, y, theta, C):
        m = len(y)
        return (1./m) * (sp.dot(X.T, sigmoid(sp.dot(X, theta)) - y) \
                + C * theta)
    
    def sigmoid(z):
        return 1 / (1 + sp.exp(-z))
    
    N = X.shape[1]
    J = lambda theta: lr_cost(X, y, theta, C)
    J_grad = lambda theta: lr_grad(X, y, theta, C)
    theta0 = 0.02 * sp.ones(N)
    x, nfeval, rc = opt.fmin_tnc(J,theta0, fprime=J_grad, bounds=[(0,None)]*N,
                                 disp=0)
    return x

Got error when tried X = (400x19),y =(400,1), C =0,1