Consider the following set of data points:

input desired

$x_1,x_2,$ label

$1,0,1$

$0,1,1$

$-1,0,0$

$-1,1,0$

$0,-1,0$

As the above table shows, the data points are categorized $($labeled$)$ into two groups specified by the labels $"1"$ and $"0".$

$($A$)$ Use the perceptron learning rule to train a single$-$neuron perceptron on the data points given above. Initialize the weights to $(w0=-0\mathrm{.}7,w1=-0\mathrm{.}2,w2=0\mathrm{.}95),$ assume the learning rate is $1,$ and use the hard$-$limiter activation function $($If output $1$; otherwise output $0),$ i$.$e$.$:

$(v)=\{\begin{array}{ccc}1,& if& v0\\ 0,& if& v<mn\; id="EditorElement\_8029126">0</mn>\end{array}$

Report the weights after one iteration w$0=w1=$

$w2=$

Report the weights after two iterations $w0=w1=$

$$ w$2=.$ NOTE: Report them in $x.$ format.