Unlock 30% off on Manual Testing Annual Plans this Holiday Season.

Copied to Clipboard!

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)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.