import numpy as np
import matplotlib.pyplot as plt
from dataNormalization import rescaleNormalization
# Starting codes for the HA2 of CS596
# Fill in the codes between "%PLACEHOLDER#start" and "PLACEHOLDER#end"
# Ground-truth Cashier
groundUnitPrice = np.array([20, 25, 8]) # for fish, chip, and ketchup, respectively
# step 1: initialize your guess on the unit prices of fish, chip and ketchup.
estimatedUnitPrice = np.array([10,10,10]) # initial unit prices.
#MAX_POSSIBLE_UNIT_PRICE = 50
#estimatedUnitPrice = np.random.randint(MAX_POSSIBLE_UNIT_PRICE, size=3) # choose random initial guesses
#PLACEHOLDER_1#start: set your own stopping conditions and learning rate
#condition 1: maximal iterations, stop.
MAX_ITERATION = 100000
#condition 2: if the difference between your prediction and the cashier's price is smaller than a threshold, stop.
MIN_DELTA = 0.01
# learning rate
ALPHA = .001#1e-3
#PLACEHOLDER_1#end
# Y coordinates for plotting
deltaHistory = []
# step 2: iterative method
for i in range(0, MAX_ITERATION):
# order a meal (simulating training data)
randomMealPortions = np.random.randint(10, size=3)
# calculate the estimated price
expectedTotalPrice = np.sum(estimatedUnitPrice * randomMealPortions )
# calculate cashier/true price;
cashierPrice = np.sum(groundUnitPrice * randomMealPortions)
#%%%PLACEHOLDER_2#start
# calculate current error
iterError =
# append iterError to the history array
deltaHistory.append(abs(iterError))
#update unit prices
estimatedUnitPrice =
#%%%%PLACEHOLDER_2#end
#check stop conditions
if abs(delta) < MIN_DELTA:
break
print('iteration:{}, delta:{}'.format(i, abs(delta)))
# step 3: evaluation
error = np.mean(abs(estimatedUnitPrice - groundUnitPrice))
print('estimation error:{}'.format(error))
# visualize convergence curve: error v.s. iterations
plt.plot(range(0, len(deltaHistory)), deltaHistory)
plt.xlabel('iteration (cnt:{})'.format(len(deltaHistory)))
plt.ylabel('abs(delta)')
plt.title('Final:{} est err:{} actl :{}'.format([ '%.4f' % elem for elem in estimatedUnitPrice ], round(error, 4), round(delta, 4)))
plt.show()