SCIPY MINIMIZE CODE

def linear_model(thetas, X): ''' Return the linear combination of thetas and features as defined above.

SCIPY MINIMIZE CODE

minimize_average_loss(l2, linear_model, one_hot_X, tips) from scipy.optimize import minimize from math import def f(c): return sqrt((sin(pi/2) + sin(0) + sin(c) - 2)2 + (cos(pi/2) + cos(0) + cos(c) - 1)2) print minimize(f, 3.14/2 + 3.14/7) The above code does try to minimize the function f, but for my task I need to minimize with respect to three variables. # This entry corresponds to the optimal theta estimated by the function. , x0=.) # Notice above that we extract the 'x' entry in the dictionary returned by `minimize`.

Your answer will be very similar to your answer to question 2 from lab 7. DO NOT hard-code the length of x0 # it should depend on the number of features in X. Replace the second '.' with an initial value for theta, # and remember that theta is now a vector. Yes, this is confusing # but optimization people like x to be the thing they are # optimizing. The first '.' should be replaced with the average loss evaluated on # the data X, y using the model and appropriate loss function. Parameters - loss_function: either the squared or absolute loss functions defined above model: the model (as defined in Question 1b) X: a 2D dataframe of numeric features (one-hot encoded) y: a 1D vector of tip amounts Returns - The estimate for the optimal theta vector that minimizes our loss ''' # Notes on the following function call which you need to finish: # 0. from scipy.optimize import minimize def l1(y, y_hat): return np.abs(y - y_hat) def l2(y, y_hat): return (y - y_hat)**2 def minimize_average_loss(loss_function, model, X, y): ''' Minimize the average loss calculated from using different theta vectors, and estimate the optimal theta for the model.

Scipy minimize