All my weights for gradient descent become 0 on feature expansion - matlab

Question

I have 2 features which I expand to contain all possible combinations of the two features under order 6. When I do MATLAB's fminunc, it returns a weight vector where all elements are 0.
The dataset is here
clear all;
clc;
data = load("P2-data1.txt");
m = length(data);
para = 0; % regularization parameter
%% Augment Feature
y = data(:,3);
new_data = newfeature(data(:,1), data(:,2), 3);
[~, n] = size(new_data);
betas1 = zeros(n,1); % initial weights
options = optimset('GradObj', 'on', 'MaxIter', 400);
[beta_new, cost] = fminunc(#(t)(regucostfunction(t, new_data, y, para)), betas1, options);
fprintf('Cost at theta found by fminunc: %f\n', cost);
fprintf('theta: \n');
fprintf(' %f \n', beta_new); % get all 0 here
% Compute accuracy on our training set
p_new = predict(beta_new, new_data);
fprintf('Train Accuracy after feature augmentation: %f\n', mean(double(p_new == y)) * 100);
fprintf('\n');
%% the functions are defined below
function g = sigmoid(z) % running properly
g = zeros(size(z));
g=ones(size(z))./(ones(size(z))+exp(-z));
end
function [J,grad] = regucostfunction(theta,x,y,para) % CalculateCost(x1,betas1,y);
m = length(y); % number of training examples
J = 0;
grad = zeros(size(theta));
hyp = sigmoid(x*theta);
err = (hyp - y)';
grad = (1/m)*(err)*x;
sum = 0;
for k = 2:length(theta)
sum = sum+theta(k)^2;
end
J = (1/m)*((-y' * log(hyp) - (1 - y)' * log(1 - hyp)) + para*(sum) );
end
function p = predict(theta, X)
m = size(X, 1); % Number of training examples
p = zeros(m, 1);
index = find(sigmoid(theta'*X') >= 0.5);
p(index,1) = 1;
end
function out = newfeature(X1, X2, degree)
out = ones(size(X1(:,1)));
for i = 1:degree
for j = 0:i
out(:, end+1) = (X1.^(i-j)).*(X2.^j);
end
end
end
data contains 2 columns of rows followed by a third column of 0/1 values.
The functions used are: newfeature returns the expanded features and regucostfunction computes the cost. When I did the same approach with the default features, it worked and I think the problem here has to do with some coding issue.