exercise1 gradientDescentMulti(), shape of theta is wrong about ml-coursera-python-assignments HOT 4 CLOSED

<ipython-input-43-9bcabdbf6f18> in computeCostMulti(X, y, theta)
     33     # ======================= YOUR CODE HERE ===========================
     34     for i in range(m):
---> 35         cost += np.square(np.dot(X[i], theta) - y[i])
     36         J = cost * (1/(2*m))
     37     # ==================================================================

ValueError: shapes (3,) and (2,) not aligned: 3 (dim 0) != 2 (dim 0)

That concatenation of a colum to X or X_norm breaks everything. computeCost won't work now.

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Very weird. I found solutions online and noticed that the gradient descent code is exactly the same for univariate and multivariate. So I don't really understand the point of the exercise. Anyways, isn't the univariate question actually multivariate when you consider theta_0 and theta_1 as 2 features? I know theta_0 is just the y-intercept basis thing or whatever it is that gets a value of 1 assigned to it. I thought there would be more of a difference in the formula and code.

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@nyck33 just wondering if you ran into this issue, If you did, how did you solve it
#36 (comment)

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solved.

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