fill in 'change' and 'atmp' that would make this function work. import numpy as np # X- a matrix of features, n*p # y- single array/vector # theta-...
Question

# fill in 'change' and 'atmp' that would make this function work. import numpy as np

# X     - a matrix of features, n*p

# y     - single array/vector

# theta   - single array/vector

# alpha   - scalar

# iterations - scarlar

def gradientDescent(X, y, theta, alpha, numIterations):

'''

# This function returns a tuple (theta, Cost array)

'''

m = len(y)

arrCost =[];

transposedX = np.transpose(X) # transpose X into a vector -> XColCount X m matrix

for interation in range(0, numIterations):

################PLACEHOLDER4 #start##########################

#: codes to update theta, i.e., the parameters to estimate.

# Replace the following variables if needed

change =

theta = np.subtract(theta, change) # or theta = theta - alpha * gradient

################PLACEHOLDER4 #end##########################

################PLACEHOLDER5 #start##########################

# calculate the current cost with the new theta;

atmp =

arrCost.append(atmp)

print(atmp)

################PLACEHOLDER5 #start##########################

return theta, arrCost

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