How to make a list a certain size (k)-CodePudding

I am creating a function that finds a k-nearest neighbors prediction.

def knn_predict(data, x_new, k):
    """ (tuple, number, int) -> number

    data is a tuple.
    
    data[0] are the x coordinates and 
    data[1] are the y coordinates.
    
    k is a positive nearest neighbor parameter.
    
    Returns k-nearest neighbor estimate using nearest
    neighbor parameter k at x_new.
    
    Assumes i) there are no duplicated values in data[0],
    ii) data[0] is sorted in ascending order, and
    iii) x_new falls between min(x) and max(x).
    
    >>> knn_predict(([0, 5, 10, 15], [1, 7, -5, 11]), 2, 2)
    4.0
    
    >>> knn_predict(([0, 5, 10, 15], [1, 7, -5, 11]), 2, 3)
    1.0

    >>> knn_predict(([0, 5, 10, 15], [1, 7, -5, 11]), 8, 2)
    1.0

    >>> knn_predict(([0, 5, 10, 15], [1, 7, -5, 11]), 8, 3)
    4.333333333333333
    """
    
  
    #use find_index and the x_new value for k loops to find N\/k(x_new) (list of indexes)
    #incorporate k value!!!
    nk = [find_index(data[0], x_new) for k in range(k)] #here
    #use N\/k(x_new) indexes to find correlated y values
    yvals = [data[1][val] for val in nk]
    #use correlated y values summed together divided by k to find y new
    ynew = sum(yvals) / k
    return ynew

important line:

nk = [find_index(data[0], x_new) for k in range(k)] #here

The line with #here at the end is supposed to use this function:

def find_index(x, x_new):
    """ (list, number) -> int
    
    Returns the smallest index i such that x[i] <= x_new 
    and x[i 1] >= x_new.
    
    Assumes i) there are no duplicated values in x,
    ii) x is sorted in ascending order, and
    iii) x_new falls between min(x) and max(x).
    
    >>> find_index([1, 5, 7, 9], 1)
    0

    >>> find_index([1, 5, 7, 9], 2)
    0

    >>> find_index([1, 5, 7, 9], 6)
    1

    >>> find_index([1, 5, 7, 9], 7)
    1

    >>> find_index([1, 5, 7, 9], 8)
    2

    >>> find_index([1, 5, 7, 9], 9)
    2
    """
   
    for i, element in enumerate(x):
        
        if x_new <= x[i 1] and element <= x_new:
            return i

and return the indexes. k is the number of indexes it will find. How can I correctly fix the line so that it finds k number of index (the list should be k long)

CodePudding user response：

This solves your problem in three lines of code. I don't think find_index is useful at all. Notice that this code doesn't care if the entries are in order or not, or even whether the value is between min(x) and max(x).

def knn_predict(data, x_new, k):
    """ (tuple, number, int) -> number

    data is a tuple.
    
    data[0] are the x coordinates and 
    data[1] are the y coordinates.
    
    k is a positive nearest neighbor parameter.
    
    Returns k-nearest neighbor estimate using nearest
    neighbor parameter k at x_new.
    """
    
    # Find the deltas from our target to the x values.
    
    deltas = [(abs(t-x_new),y) for t,y in zip(*data)]

    # Sort the values by the distance.

    deltas.sort()

    # Return the sum of the Ys.

    return sum( d[1] for d in deltas[:k] ) / k

print( knn_predict(([0, 5, 10, 15], [1, 7, -5, 11]), 2, 2) )
print( knn_predict(([0, 5, 10, 15], [1, 7, -5, 11]), 2, 3) )
print( knn_predict(([0, 5, 10, 15], [1, 7, -5, 11]), 8, 2) )
print( knn_predict(([0, 5, 10, 15], [1, 7, -5, 11]), 8, 3) )

Output:

4.0
1.0
1.0
4.333333333333333