# Import required libraries:
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from sklearn.metrics import r2_score

# Generating dataset:
# Y = A*sin(B(X + C)) + D
# A = Amplitude
# Period = 2*pi/B
# Period = Length of One Cycle
# C = Phase Shift (In Radian)
# D = Vertical Shift

X = np.linspace(0,1,100)             #(Start,End,Points)

# Here…
# A = 1
# B= 2*pi
# B = 2*pi/Period
# Period = 1
# C = 0
# D = 0

Y = 1*np.sin(2*np.pi*X)


# Adding some Noise :
Noise = 0.4*np.random.normal(size=100)
Y_data = Y + Noise
plt.scatter(X,Y_data,c="r")

# Calculate the value:
def calc_sine(x,a,b,c,d):
  return a * np.sin(b* ( x + np.radians(c))) + d
  
# Finding optimal parameters :
popt,pcov = curve_fit(calc_sine,X,Y_data)

# Plot the main data :
plt.scatter(X,Y_data)# Plot the best fit curve :
plt.plot(X,calc_sine(X,*popt),c="r")
plt.show()

# Check the accuracy :
Accuracy =r2_score(Y_data,calc_sine(X,*popt))
print (Accuracy)

# Function to calculate the value :
def calc_line(X,m,b):
  return b + X*m
  
# It returns optimized parametes for our function :
# popt stores optimal parameters
# pcov stores the covarience between each parameters.
popt,pcov = curve_fit(calc_line,X,Y_data)

# Plot the main data :
plt.scatter(X,Y_data)

# Plot the best fit line :
plt.plot(X,calc_line(X,*popt),c="r")
plt.show()

# Check the accuracy of model :
Accuracy =r2_score(Y_data,calc_line(X,*popt))
print ("Accuracy of Linear Model : ",Accuracy)

# Import required libraries:
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from sklearn.metrics import r2_score

# Generating dataset:
# Y = A*sin(B(X + C)) + D
# A = Amplitude
# Period = 2*pi/B
# Period = Length of One Cycle
# C = Phase Shift (In Radian)
# D = Vertical Shift

X = np.linspace(0,1,100)             #(Start,End,Points)

# Here…
# A = 1
# B= 2*pi
# B = 2*pi/Period
# Period = 1
# C = 0
# D = 0

Y = 1*np.sin(2*np.pi*X)


# Adding some Noise :
Noise = 0.4*np.random.normal(size=100)
Y_data = Y + Noise
plt.scatter(X,Y_data,c="r")

# Calculate the value:
def calc_sine(x,a,b,c,d):
  return a * np.sin(b* ( x + np.radians(c))) + d
  
# Finding optimal parameters :
popt,pcov = curve_fit(calc_sine,X,Y_data)

# Plot the main data :
plt.scatter(X,Y_data)# Plot the best fit curve :
plt.plot(X,calc_sine(X,*popt),c="r")
plt.show()

# Check the accuracy :
Accuracy =r2_score(Y_data,calc_sine(X,*popt))
print (Accuracy)

# Function to calculate the value :
def calc_line(X,m,b):
  return b + X*m
  
# It returns optimized parametes for our function :
# popt stores optimal parameters
# pcov stores the covarience between each parameters.
popt,pcov = curve_fit(calc_line,X,Y_data)

# Plot the main data :
plt.scatter(X,Y_data)

# Plot the best fit line :
plt.plot(X,calc_line(X,*popt),c="r")
plt.show()

# Check the accuracy of model :
Accuracy =r2_score(Y_data,calc_line(X,*popt))
print ("Accuracy of Linear Model : ",Accuracy)

# Import required libraries: # Generating dataset: # Y = A*sin(B(X + C)) + D # A = Amplitude # Period = 2*pi/B # Period = Length of One Cycle # C = Phase Shift (In Radian) # D = Vertical Shift #(Start,End,Points) # Here… # A = 1 # B= 2*pi # B = 2*pi/Period # Period = 1 # C = 0 # D = 0 # Adding some Noise : # Calculate the value: # Finding optimal parameters : # Plot the main data : # Plot the best fit curve : # Check the accuracy : # Function to calculate the value : # It returns optimized parametes for our function : # popt stores optimal parameters # pcov stores the covarience between each parameters. # Plot the main data : # Plot the best fit line : # Check the accuracy of model :