linearRegressionBasics

'''
Sample Linear Regression project which we provide data sample manually
Author- Edirisooriya D M B
Date- 27/09/2024
'''

#Importing libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#Import sample data you want to analyse here as features and labels

# features
x=input("Input your feature values separated by a comma: ")
x_train=list(map(float,x.split(',')))
x_train=np.array(x_train)
# labels
y=input("Input your label values separated by a comma: ")
y_train=list(map(float,y.split(',')))
y_train=np.array(y_train)

df=pd.DataFrame({'x':x_train,'y':y_train})
print(df)
print(x_train)
print(y_train)

#Defining a cost function
def cost_function(w,b,x,y):
  l=len(x)
  total=0
  for i in range(l):
    f_wb=w*x[i]+b
    cost=(f_wb-y[i]**2)
    total=total+cost
  total=total/(2*l)
  return total

#Define a function to compute the gradient
def compute_gradient(x,y,w,b):
  m=len(x)
  dj_dw=0
  dj_db=0
  for i in range(m):
    f_wb=w*x[i]+b
    dj_dw_i=(f_wb-y[i])*x[i]
    dj_db_i=(f_wb-y[i])
    dj_dw=dj_dw+dj_dw_i
    dj_db=dj_db+dj_db_i
  return dj_dw/m,dj_db/m

#Define a function to compute the gradient descent
def gradient_descent(x,y,w_in,b_in,alpha,num_iters,cost_function,compute_gradient):
  dj_dw=0
  dj_db=0
  w=w_in
  b=b_in
  for i in range(num_iters):
    dj_dw,dj_db=compute_gradient(x,y,w,b)
    w=w-alpha*dj_dw
    b=b-alpha*dj_db
  return w,b

m,c=gradient_descent(x_train,y_train,5,2,1.0e-2,100000,cost_function,compute_gradient)
print(m,c)
# Data visualization
x = np.linspace(0, 10, 100)
y = m * x + c
plt.plot(x, y,'r')
plt.scatter(x_train,y_train)
plt.xlabel('x')
plt.ylabel('y')
plt.show()