Curve Fitting

AIM: Write a Python program to calculate Curve fitting by least – square approximations.

Source Code:

# This is naive approach, there are shortcut methods for doing it!
# Least square method
# Fitting y = ax^b to given n data points
import numpy as np

# Reading value of n
n = int(input("How many data points? "))

# Creating numpy array x & y to store n data points
x = np.zeros(n)
y = np.zeros(n)

# Reading data
print("Enter data:")
for i in range(n):
  x[i] = float(input("x["+str(i)+"]= "))
  y[i] = float(input("y["+str(i)+"]= "))
 
# Finding required sum for least square methods
sumX,sumX2,sumY,sumXY = 0,0,0,0
for i in range(n):
  sumX = sumX + np.log(x[i])
  sumX2 = sumX2 +np.log(x[i])*np.log(x[i])
  sumY = sumY + np.log(y[i])
  sumXY = sumXY + np.log(x[i])*np.log(y[i])

# Finding coefficients A and B
b = (n*sumXY-sumX*sumY)/(n*sumX2-sumX*sumX)
A = (sumY - b*sumX)/n

# Obtaining a and b
a = np.exp(A)

# Displaying coefficients a, b & equation
print("Coefficients are:")
print("a: ", a)
print("b: ", b)

Output:

How many data points? 4
Enter data:
x[0]= 61
y[0]= 350
x[1]= 26
y[1]= 400
x[2]= 7
y[2]= 500
x[3]= 2.6
y[3]= 600

Coefficients are:
a: 701.9919475945616
b: -0.1708554336187208