all.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def hypothesis(x, θ):
    hypothesis = np.matmul(θ.T, x)
    return hypothesis
  
def loss_function(h_x, y):
    loss_error = 1 /2 *(h_x - y) *(h_x - y)
    return loss_error
  
def update_parameters(THETA, hypothesis, x, y, learning_rate):
	
	x = np.reshape(x, THETA.shape)
	Updated_THETA = THETA - learning_rate *(hypothesis - y) *x
	return Updated_THETA

df = pd.read_csv("output.csv")
X = df['X']
y = df['y']

X = np.array([[i] for i in X])
xzeros = np.ones([X.shape[0], 1], dtype=X.dtype)
X = np.concatenate((xzeros, X), axis=1)

#THETA nın ilk değerleri sıfır olmalı. Daha sonra bunları güncelleyeceğiz
THETA = np.zeros([2, 1], dtype=np.float32)
#Öğrenme hızı
learning_rate = 0.01

for x, y in zip(X, y):
	#Bir tahmin yap
	hypothesis_x = hypothesis(x, THETA)
	#Hatanı ölç
	loss = loss_function(hypothesis_x, y)
	#Parametreleri güncelle
	THETA = update_parameters(THETA, hypothesis_x, x, y, learning_rate)

print(THETA) #Output [[44.56747853], [18.34766775]]