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