Please help with python code only using numpy package

Nearest Neighbor Classification Introduction Machine learning is an area of computer science whose aim is to create programs which improve their performance with experience. There are many applications for this, including: face recognition, recommendation systems, defect detection, robot navigation, and game playing. For this assignment, you will implement a simple machine learning algorithm called Nearest Neighbor which learns by remembering training examples. It then classifies test examples by choosing the class of the closest training example. The notion of closeness differs depending on applications. You will need to use the Nearest Neighbor algorithm to learn and classify types of Iris plants based on their sepal and petal length and width.

The learning will be done by remembering training examples stored in a comma-separated file. The training examples include different measurements which collectively are called features or attributes, and a class label for different instances. These are: 1. sepal length in cm 2. sepal width in cm 3. petal length in cm 4. petal width in cm 5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica To see how well the program learned, you will then load a file containing testing examples, which will include the same type of information, but for different instances. For each test instance, you will apply the Nearest Neighbor algorithm to classify the instance. This algorithm works by choosing a class label of the closest training example, where closest means shortest distance. The distance is computed using the following

formula: (, ) = ( ) 2 +( ) 2 + ( ) 2 + ( ) 2

where , are two instances (i.e. a training or a testing example), , are their sepal lengths, , are their sepal widths, , are their petal lengths, and , are their petal widths. After you finish classifying each testing instance, you will then need to compare it to the true label that is specified for each example and compute the accuracy. Accuracy is measured as the number of correctly classified instances divided by the number of total testing instances