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import csv
import sys
DATASET = "data.csv"
THETAS_FILE = "thetas.csv"
def normalize(data):
min_val = min(data)
max_val = max(data)
return [(x - min_val) / (max_val - min_val) for x in data], min_val, max_val
def load_data():
km = []
price = []
with open(DATASET) as f:
reader = csv.reader(f)
next(reader)
for row in reader:
km.append(float(row[0]))
price.append(float(row[1]))
return km, price
def estimate_price(mileage, theta0, theta1):
return theta0 + theta1 * mileage
# DV: dependant variable, IV: independant variable
def train_once(learning_rate, DV, IV, theta0, theta1):
tmp0 = (
learning_rate
* (1.0 / len(DV))
* sum(estimate_price(x, theta0, theta1) - y for x, y in zip(DV, IV))
)
tmp1 = (
learning_rate
* (1.0 / len(DV))
* sum((estimate_price(x, theta0, theta1) - y) * x for x, y in zip(DV, IV))
)
return tmp0, tmp1
def denormalize_thetas(t0, t1, km_min, km_max, price_min, price_max):
price_range = price_max - price_min
km_range = km_max - km_min
real_t1 = t1 * price_range / km_range
real_t0 = t0 * price_range + price_min - real_t1 * km_min
return real_t0, real_t1
def train(learning_rate, iterations):
kms, prices = load_data()
kms_norm, km_min, km_max = normalize(kms)
prices_norm, price_min, price_max = normalize(prices)
t0 = 0.0
t1 = 0.0
for _ in range(iterations):
grad0, grad1 = train_once(learning_rate, prices_norm, kms_norm, t0, t1)
t0 -= grad0
t1 -= grad1
return denormalize_thetas(t0, t1, km_min, km_max, price_min, price_max)
def save_thetas(theta0, theta1):
with open(THETAS_FILE, "w") as f:
f.write(f"{theta0}\n{theta1}\n")
learning_rate = float(sys.argv[1])
i = int(sys.argv[2])
t0, t1 = train(learning_rate, i)
save_thetas(t0, t1)
print(f"θ0={t0}, θ1={t1} saved to {THETAS_FILE}")
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