How to find bias for perceptron algorithm?
Rather than viewing your data as 

X = [[0.8, 0.1], [0.7, 0.2], [0.9, 0.3], [0.3, 0.8], [0.1, 0.7], [0.1, 0.9]]
Y = [-1, -1, -1, 1, 1, 1]


You could have treat the problem as 

X = [[1, 0.8, 0.1], [1, 0.7, 0.2], [1, 0.9, 0.3], [1, 0.3, 0.8], [1, 0.1, 0.7], [1, 0.1, 0.9]]
Y = [-1, -1, -1, 1, 1, 1]


That is to append a $1$ in every single entry of $X$. The weight corresponding to $1$ would be the bias.

That is $Y=Ax+b$ can be written as $Y=[A, e]\begin{bmatrix}x \\b\end{bmatrix}$ where $e$ is the all one vector.