scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
Returns
centroid
[ndarray] A k by N array of centroids found at the last iteration of k-means.
label
[ndarray] label[i] is the code or index of the centroid the i’th observation is closest to.
3.1.3 Vector Quantization / Kmeans
Clustering algorithms are useful in information theory, target detection, communications, compression,
and other areas. The vq module only supports vector quantization and the k-means algorithms. Development
of self-organizing maps (SOM) and other approaches is underway.
3.1.4 Hierarchical Clustering
The hierarchy module provides functions for hierarchical and agglomerative clustering. Its features include
generating hierarchical clusters from distance matrices, computing distance matrices from observation
vectors, calculating statistics on clusters, cutting linkages to generate flat clusters, and visualizing
clusters with dendrograms.
3.1.5 Distance Computation
The distance module provides functions for computing distances between pairs of vectors from a set of
observation vectors.
3.2 Constants (scipy.constants)
Physical and mathematical constants and units.
3.2.1 Mathematical constants
pi Pi
golden Golden ratio
152 Chapter 3. Reference