This assignment will be partially graded by an automated test script. When a file or variable name

is explicitly named in the assignment $($e$.$g$.$ hw$1\_$complete.py$,$ average$\_$age$),$ be sure to use the

correct name, so the test script can identify that you have done the task.

A$.$ Copy it into a file hw$1\_$complete.py and run it$.$ It should print out several names and tell how

long each name is$.$

B$.$ Use a list comprehension to create a list of lengths of the names. Here is an example list

comprehension. This is quite a powerful feature; search around the web for more complex

examples.

C$.$ Create a separate file, named $"$

person.py$".$ In that file, define a class "person" with fields "name",

"age", and "height." Define the method $($constructor$)$ that takes arguments 'name', 'age',

and 'height', and sets the object variables of the same names. Eg$.$

should create an object named "new$\_$person" with the name, age, and height indicated and

then print out "Joe is $34$ years old."

D$.$ Add a method $"($self$)"$ that returns a string of this form: "Bob is $17$ years old and $182$

$cm$ tall".

E$.$ Back in the main file hw$1\_$complete.py$,$ add a line to "import person". Then iterate through the

names and create a dictionary named "people" where the key is a name, and the value is a

person object.

F$.$ Use numpy.array$()$ to convert the list of ages into an array. Repeat for the list of heights.

G$.$ Use the numpy.mean$()$ function to find the average age of the people in your list and put that

average age in a variable named average$\_$age.

H$.$ Use matplotlib to create a scatter plot of ages $($on the $x$ axis$)$ and heights $($on the $y$ axis$).$ Add

gridlines, $x$ and $y$ axis labels, and a title. Export the plot as a png file.

I. There is already code in the template to load the 'iris' dataset and to make one plot of it$.$

Explore the data.

J$.$ Write a function named classify$\_$iris$()$ that implements a linear classifier for the iris dataset.

You'll have a $34$ array of weights and a $3-$element vector of biases. The function will take a $4-$

$D$ vector of features as its only argument and return an integer label $,$ or $2$