Complete the compute$\_$cost below to:

Iterate over the training examples, and for each example, compute:

The prediction of the model for that example

The cost for that example

$f_{ub}(x^{(i)})=w{x}^{(i)}+b$

$cos{t}^{(0)}=(f_{ub}-y^{(0)}{)}^2$

Return the total cost over all examples

$J(w,b)=\frac{1}{2m}{\displaystyle \underset{i=0}{\overset{m-1}{}}}cos{t}^{(i)}$

Here, $m$ is the number of training examples and ${\displaystyle \underset{?}{\overset{?}{}}}$ is the summation operator

If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.GRADED FUNCTION: compute$\_$cost

def compute$\_$cost$($x$,$y$,$w$,$ b$)$:

$"*"$

Computes the cost function for linear regression,

Args:

x $($ndarray$)$: Shape $($m$,)$ Input to the model $($Population of cities$)$

y $($ndarray$)$: Shape $($m$,)$ Label $($Actual profits for the cities$)$

w$,$ b $($scalar$)$: Parameters of the model

Returns

total$\_$cost $($float$)$: The cost of using w$,$b as the parameters for linear regression

to fit the data points in $\}$x$\backslash$mathrm$\{$ and $\}$

$"$n$"$m $=$ x$.$shape $[0]$total$\_$cost $=0$for i in range$($m$)$:

f$\_$wb$=$w$*$x$[$i$]+$b

total$\_$cost $+=($f$\_$wb $-$ y$[$i$])**2$

total$\_$cost $=(1/(2$ total$\_$costGRADED FUNCTION: compute$\_$cost

def compute$\_$cost$($x$,$y$,$ w$,$ b$)$:

$"$m$"$

Computes the cost function for linear regression.

Args:

x $($ndarray$)$: Shape $($m$,)$ Input to the model $($Population of cities$)$

y $($ndarray$)$: Shape $($m$,)$ Label $($Actual profits for the cities$)$

w$,$ b $($scalar$)$: Parameters of the model

Returns

total$\_$cost $($float$)$: The cost of using w$,$b as the parameters for linear regression

to fit the data points in $\}$x$\backslash$mathrm$\{$ and y

m$*$nm $=$ x$.$shape$[0]$total$\_$cost $=0$for i in range$($m$)$:

f wb $=$ w$*$ x$[$i$]+$ b

total$\_$cost $+=($f$\_$wb $-$ y$[$i$])***2$

total cost $=(1/(2$ total$\_$costreturn total$\_$costreturn total$\_$costClick for hints

You can check if your implementation was correct by running the following test code:initial$\_$w $=2$

initial$\_$b $==$

cost $=$ compute$\_$cost$($x$\_$train, y$\_$train, initial$\_$w$,$ initial$\_$b$)$

print$($type$($cost$))$

print$($f$\text{'}$Cost at initial w: $\{$cost:$\mathrm{.}3$f$\}\text{'})$from public$\_$tests import $*$

compute$\_$cost$\_$test$($compute$\_$cost$)$Expected Output: