106 lines
2.4 KiB
Python
Executable file
106 lines
2.4 KiB
Python
Executable file
#!/usr/bin/env python
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.animation import FuncAnimation
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from mpl_toolkits.mplot3d import Axes3D
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# Load the data
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csv="../data/binary_logistic.csv"
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data=pd.read_csv(csv)
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x_1=np.array(data[data.columns[0]])
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x_2=np.array(data[data.columns[1]])
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y=np.array(data[data.columns[2]])
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w1=w2=w3=-8
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# Define our model
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def h(x_1,x_2):
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global w1,w2,w3
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model=w1+w2*x_1+w3*x_2
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return(1/(1+np.exp(-model)))
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def dw1():
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global x_1,x_2,y
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return(1/len(x_1)*(sum(h(x_1,x_2)-y)))
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def dw2():
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global x_1,x_2,y
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return(1/len(x_1)*sum(x_1*(h(x_1,x_2)-y)))
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def dw3():
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global x_1,x_2,y
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return(1/len(x_1)*sum(x_2*(h(x_1,x_2)-y)))
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# Perform the gradient decent
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#fig, ax = plt.subplots(dpi=300)
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alpha=0.01 # Proportion of the gradient to take into account
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accuracy=0.0001 # Accuracy of the decent
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done=False
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def decent():
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global w1,w2,w3,x,y
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skip_frame=0 # Current frame (plot animation)
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while True:
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w1_old=w1
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w1_new=w1-alpha*dw1()
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w2_old=w2
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w2_new=w2-alpha*dw2()
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w3_old=w3
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w3_new=w3-alpha*dw3()
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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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break
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skip_frame+=1
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decent()
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fig=plt.figure()
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#print(np.round(h(x_1,x_2)))
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#pred=np.round(h(x_1,x_2))
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# Plot data
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ax = fig.add_subplot(2,2,1)
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ax.set_title("Original Data")
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ax.set_xlabel("X")
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ax.set_ylabel("Y")
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scatter=plt.scatter(x_1,x_2,c=y,marker="o")
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handles, labels = scatter.legend_elements(prop="colors", alpha=0.6)
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legend = ax.legend(handles, ["Class A","Class B"], loc="upper right", title="Legend")
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# Plot model
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ax = fig.add_subplot(2,2,2,projection='3d')
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ax.set_title("Model")
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X,Y= np.meshgrid(np.sort(x_1), np.sort(x_2))
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ax.set_xlabel("X")
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ax.set_ylabel("Y")
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ax.set_zlabel("Probability")
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surf = ax.plot_wireframe(X,Y, h(X,Y),rstride=10,cstride=10)
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# Plot prediction
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ax = fig.add_subplot(2,1,2)
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ax.set_title("Predictions")
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ax.set_xlabel("X")
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ax.set_ylabel("Y")
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scatter=plt.scatter(x_1,x_2,c=np.round(h(x_1,x_2)),marker="o")
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handles, labels = scatter.legend_elements(prop="colors", alpha=0.6)
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legend = ax.legend(handles, ["Class A","Class B"], loc="upper right", title="Legend")
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x=np.arange(0,10,0.2)
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plt.plot([1,2],[2,2])
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# Save
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plt.tight_layout()
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#plt.savefig("binary.png",dpi=300)
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plt.show()
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