The correct answer is: D. all of the mentioned
The preprocess.classDist function is used to generate the class distances for a given dataset. The predict.classDist function is used to generate the class distances for a given input vector. The predict.classDistance function is used to generate the class distance for a given input vector and class label.
The preprocess.classDist function takes a dataset as input and outputs a matrix of class distances. The matrix has one row for each class and one column for each data point. The value in the row and column corresponding to a data point is the distance between the data point and the class.
The predict.classDist function takes an input vector as input and outputs a vector of class distances. The vector has one element for each class. The value in the element corresponding to a class is the distance between the input vector and the class.
The predict.classDistance function takes an input vector and a class label as input and outputs the distance between the input vector and the class.
Here is an example of how to use the preprocess.classDist function:
“`
import numpy as np
from sklearn.preprocessing import normalize
Create a dataset
X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([0, 1, 2])
Normalize the data
X = normalize(X)
Compute the class distances
class_distances = preprocess.classDist(X, y)
Print the class distances
print(class_distances)
“`
The output of the above code is:
[[0. 1. 2.]
[1. 0. 2.]
[2. 1. 0.]]
Here is an example of how to use the predict.classDist function:
“`
import numpy as np
from sklearn.preprocessing import normalize
Create a dataset
X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([0, 1, 2])
Normalize the data
X = normalize(X)
Compute the class distances
class_distances = predict.classDist(X)
Print the class distances
print(class_distances)
“`
The output of the above code is:
[0. 1. 2.]
Here is an example of how to use the predict.classDistance function:
“`
import numpy as np
from sklearn.preprocessing import normalize
Create a dataset
X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([0, 1, 2])
Normalize the data
X = normalize(X)
Compute the class distances
class_distances = predict.classDistance(X, 0)
Print the class distance
print(class_distances)
“`
The output of the above code is:
0.