from matplotlib.colors import ListedColormap

def plot$\_$decision$\_$regions$($X$,$ y$,$ classifier, resolution$=0\mathrm{.}02)$:

# setup marker generator and color map

markers $=(\text{'}$s$\text{'},\text{'}$o$\text{'},\text{'}$P$\text{'},\text{'}^\text{'},\text{'}$v$\text{'})$

colors $=(\text{'}$red$\text{'},$ 'blue', 'lightgreen', 'gray', 'cyan'$)$

cmap $=$ ListedColormap$($colors$[$:len$($np$.$unique$($y$))])$

# plot the decision surface

x$1\_$min, x$1\_$max $=$ X$[$:$,0].$min$()-1,$ X$[$:$,0].$max$()+1$

x$2\_$min, x$2\_$max $=$ X$[$:$,1].$min$()-1,$ X$[$:$,1].$max$()+1$

xx$1,$ xx$2=$ np$.$meshgrid$($np$.$arange$($x$1\_$min, x$1\_$max, resolution$),$

np$.$arange$($x$2\_$min, x$2\_$max, resolution$))$

Z $=$ classifier.predict$($np$.$array$([$xx$1.$ravel$(),$ xx$2.$ravel$()]).$T$)$

Z $=$ Z$.$reshape$($xx$1.$shape$)$

plt$.$contourf$($xx$1,$ xx$2,$ Z$,$ alpha$=0\mathrm{.}3,$ cmap$=$cmap$)$

plt$.$xlim$($xx$1.$min$(),$ xx$1.$max$())$

plt$.$ylim$($xx$2.$min$(),$ xx$2.$max$())$

# plot class examples

for idx, cl in enumerate$($np$.$unique$($y$))$:

plt$.$scatter$($x$=$X$[$y $==$ cl$,0],$

y$=$X$[$y $==$ cl$,1],$

alpha$=0\mathrm{.}8,$

c$=$colors$[$idx$],$

marker$=$markers$[$idx$],$

label$=$cl$,$

edgecolor$=$'black'$)$

plot$\_$decision$\_$regions$($X$,$ y$,$ classifier$=$ppn$)$

plt$.$xlabel$(\text{'}$sepal length $[$cm$]\text{'})$

plt$.$ylabel$(\text{'}$petal length $[$cm$]\text{'})$

plt$.$legend$($loc$=$'upper left'$)$

# plt$.$savefig$(\text{'}$images$/02\_08.$png$\text{'},$ dpi$=300)$

plt$.$show$()$

Using the above, give code that plots the decision regions for the first $10$ epochs. Use learning rate $=0\mathrm{.}01$ and random seed $=1$ when applicable.