algorithm - Verifying a Perceptron Learning Example -

i trying understand perceptron learning algorithm via example presented professor. here understanding. can 1 check if understanding correct?

lets have inputs

x1 x2 result(y)

1 3 +1

-1 -2 -1

1 -1 1

-2 1 -1

now use below algorithm weights

w0=0

1)y1(w0x1)<=0

hence w1=w0+y1*x1=[1,3]

2)y2(w1,x2)<=0

hence w2=w1+y2*x2=[3,-1]

3)y3(w2,x2)>=0

hence no iteration

4)y4(w2,x4)<=0

hence w3=w2+y4*x4=[5,-2]

hence weights

x1 x2 result(y) weights

1 3 +1 [1,3]

-1 -2 -1 [3,-1]

1 -1 1 [3,-1]

-2 1 -1 [5,2]

is understanding right?or making mistake weights selection /or mistake while making iteration .

it looks did correct, there number of comments:

you state that, initially, w0 = 0. not make sense, later add vectors of dimension 2. i'm guessing meant w0 = [0, 0].
fyi:
1. a more general perceptron learning algorithm not add/subtract misclassified instances, rather scaled version multiplied 0 < α ≤ 1. algorithm above uses α = 1.
2. it's common artificially prepend perceptron inputs, constant 1 term. hence, if original inputs vectors of dimension 2, you'd work on vectors dimension 3, first item of each vector 1.

Earony