Typical usage of the package
A typical usage of DADApy involves the initialisation of a Data object either with a set of coordinates or with a set of distances between points. After the initialisation a series of computations are performed by calling the class method relative to specific algorithm wanted. The results of the computations are typically available as attributes of the object.
import numpy as np import matplotlib.pyplot as plt from dadapy.data import Data # Generate a simple 3D gaussian dataset X = np.random.normal(0, 1, (1000, 3)) # initialise the "Data" class with a # set of coordinates data = Data(X) # compute distances up to the 100th # nearest neighbour data.compute_distances(maxk = 100) # compute the intrinsic dimension # using the 2NN estimator intrinsic_dim, intrinsic_dim_err = data.compute_id_2NN() # check the value of the intrinsic # dimension found print(data.intrinsic_dim) # compute the density of all points # using a simple kNN estimator log_den, log_den_err = data.compute_density_kNN(k = 15) # as an alternative, compute the density # using a more sophisticated estimator log_den, log_den_err = data.compute_density_PAk() plt.hist(data.log_den) # find the statistically significant peaks # of the density profile computed previously data.compute_clustering_ADP(Z = 1.5) print(data.N_clusters)