def predict(self, train, test_row, num_neighbours=7, classify=True):
neigbours = get_neighbours(train,
test_row,
num_neighbours,
distance_metrics="block")
ouput = [row[-1] for row in neigbours]
if classify:
prediction = max(set(ouput), key=ouput.count)
else:
prediction = sum(ouput) / len(ouput)
return prediction
def predict(self, train, test_row, num_neighbours=7, classify=True):
"""
KNN Prediction Model, used for either Regression or
Classification , in respect to Test Point and
Dataset Type.
PARAMETERS
==========
train: ndarray
Array Representation of Collection
of Points, with their corresponding
x1,x2 and y features.
test_row: ndarray(dtype=int,ndim=1,axis=1)
Array representation of test point,
with its corresponding x1,x2 and y
features.
num_neighbours: int
Number of nearest neighbours, close
to the test point, with respect to
x1,x2 and y features.
classify: Boolean
Type of Mode, K Nearest Neighbour
Model wants to be applied, according
to Dataset and Application Field.
neighbours: list
List of n nearest neighbours, close
to the test point, with their
associated Point Array and distance
from the Test point.
ouput: list
List of Distances of n nearest
neighbours, calculated with respect
to the test point, using either
Block or Euclidean Metric.
key: int
Count of number of terms inside
ouput list.
RETURNS
=======
prediction: float/int
If used as a Classifier, gives
Class number as prediction. Else,
it will give the mean of Cluster
made by test point and its n
nearest neighbours.
"""
neigbours = get_neighbours(train,
test_row,
num_neighbours,
distance_metrics="block")
ouput = [row[-1] for row in neigbours]
if classify:
prediction = max(set(ouput), key=ouput.count)
else:
prediction = sum(ouput) / len(ouput)
return prediction
