Consider the following set of data points:

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$.$:

$(v)=\{\begin{array}{ccc}1,& if& v0\\ 0,& if& v<mn\; id="EditorElement\_372590">0</mn>\end{array}$

Report the weights after one iteration w $0=$

eights after one iteration $w0=$

$w2=$

Report the weights after two iterations w $0=$

$w1=$