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#!/usr/bin/env python
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
# Load the data
csv="../data/polynomial.csv"
data=pd.read_csv(csv)
x=np.array(data["x"])
y=np.array(data["y"])
# Define the weight
w1=w2=w3=10
# Define our model
def h(x):
return(w1+w2*x+w3*(x**2))
# Define all partial derivative
def dh1():
return(1/len(x)*np.sum(h(x)-y))
def dh2():
return(1/len(x)*np.sum((h(x)-y)*x))
def dh3():
return(1/len(x)*np.sum((h(x)-y)*(x**2)))
# Perform the gradient decent
fig, ax = plt.subplots()
frame=0 # Current frame (plot animation)
alpha=0.005 # Proportion of the gradient to take into account
accuracy=0.000001 # Accuracy of the decent
done=False
def decent(i):
global w1,w2,w3,x,y,frame
while True:
w1_old=w1
w1_new=w1-alpha*dh1()
w2_old=w2
w2_new=w2-alpha*dh2()
w3_old=w3
w3_new=w3-alpha*dh3()
w1=w1_new
w2=w2_new
w3=w3_new
if abs(w1_new-w1_old) <= accuracy and abs(w2_new-w2_old) <= accuracy and abs(w2_new-w2_old) <= accuracy:
done=True
frame+=1
if frame >=1000:
frame=0
ax.clear()
ax.set_xlim([0, 7])
ax.set_ylim([0, 5])
ax.plot(x,y,"ro")
ax.plot(x,h(x))
break
def IsDone():
global done
i = 0
while not done:
i += 1
yield i
anim=FuncAnimation(fig,decent,frames=IsDone,repeat=False)
anim.save('polynomial.gif',dpi=80,writer="imagemagick")
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