I was trying to understand the working of the function fast_knn. So, I tried to execute it line by line in order to understand the working. Here it is:
from scipy.spatial import KDTree
def shepards(distances, power=2):
return to_percentage(1/np.power(distances, power))
def to_percentage(vec):
return vec/np.sum(vec)
data_temp = np.arange(25).reshape((5, 5)).astype(np.float)
data_temp[0][2] = np.nan
k=4
eps=0
p=2
distance_upper_bound=np.inf
leafsize=10
idw_fn=shepards
init_impute_fn=mean
nan_xy = np.argwhere(np.isnan(data_temp))
data_temp_c = init_impute_fn(data_temp)
kdtree = KDTree(data_temp_c, leafsize=leafsize)
for x_i, y_i in nan_xy:
distances, indices = kdtree.query(data_temp_c[x_i], k=k+1, eps=eps,
p=p, distance_upper_bound=distance_upper_bound)
# Will always return itself in the first index. Delete it.
distances, indices = distances[1:], indices[1:]
# Add small constant to distances to avoid division by 0
distances += 1e-3
weights = idw_fn(distances)
# Assign missing value the weighted average of `k` nearest neighbours
data_temp[x_i][y_i] = np.dot(weights, [data_temp_c[ind][y_i] for ind in indices])
data_temp
The master branch (from where the function's content have been used) uses shepards for weights. Whereas the v0.0.8 (pip install) uses weights = distances/np.sum(distances)
I was trying to understand the working of the function
fast_knn
. So, I tried to execute it line by line in order to understand the working. Here it is:This outputs:
whereas the function has a different output. The code :
and the output