# How to use similarize method in ATX

Best Python code snippet using ATX

density.py

Source:density.py

`1"""2Measures for estimating the information density of a given sample.3"""4from typing import Callable, Union5import numpy as np6from scipy.spatial.distance import cosine, euclidean7from sklearn.metrics.pairwise import pairwise_distances8from modAL.utils.data import modALinput9def similarize_distance(distance_measure: Callable) -> Callable:10 """11 Takes a distance measure and converts it into a information_density measure.12 Args:13 distance_measure: The distance measure to be converted into information_density measure.14 Returns:15 The information_density measure obtained from the given distance measure.16 """17 def sim(*args, **kwargs):18 return 1/(1 + distance_measure(*args, **kwargs))19 return sim20cosine_similarity = similarize_distance(cosine)21euclidean_similarity = similarize_distance(euclidean)22def information_density(X: modALinput, metric: Union[str, Callable] = 'euclidean') -> np.ndarray:23 """24 Calculates the information density metric of the given data using the given metric.25 Args:26 X: The data for which the information density is to be calculated.27 metric: The metric to be used. Should take two 1d numpy.ndarrays for argument.28 Todo:29 Should work with all possible modALinput.30 Perhaps refactor the module to use some stuff from sklearn.metrics.pairwise31 Returns:32 The information density for each sample.33 """34 # inf_density = np.zeros(shape=(X.shape[0],))35 # for X_idx, X_inst in enumerate(X):36 # inf_density[X_idx] = sum(similarity_measure(X_inst, X_j) for X_j in X)37 #38 # return inf_density/X.shape[0]39 similarity_mtx = 1/(1+pairwise_distances(X, X, metric=metric))...`

information_density.py

Source:information_density.py

`1from modAL.density import similarize_distance, information_density2from sklearn.datasets import make_blobs3from scipy.spatial.distance import euclidean4X, y = make_blobs(n_features=2, n_samples=10, centers=3, random_state=0, cluster_std=0.7)5cosine_density = information_density(X)...`

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