scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
• method=’ward’ uses the Ward variance minimization algorithm. The new entry d(u, v) is computed
as follows,
�
|v| + |s|
d(u, v) = d(v, s)
T
2 |v| + |t|
+ d(v, t)
T
2 + |v|
d(s, t)2
T
where u is the newly joined cluster consisting of clusters s and t, v is an unused cluster in the forest,
T = |v| + |s| + |t|, and | ∗ | is the cardinality of its argument. This is also known as the incremental
algorithm.
Warning: When the minimum distance pair in the forest is chosen, there may be two or more pairs with the
same minimum distance. This implementation may chose a different minimum than the MATLAB(TM)
version.
Returns
Parameters
• Z
• y
[ndarray] A condensed or redundant distance matrix. A condensed distance matrix
is a flat array containing the upper triangular of the distance matrix. This is the
form that pdist returns. Alternatively, a collection of m observation vectors in n
dimensions may be passed as an m by n array.
• method
[string] The linkage algorithm to use. See the Linkage Methods section below
for full descriptions.
• metric
[string] The distance metric to use. See the distance.pdist function for a list
of valid distance metrics.
[ndarray] The hierarchical clustering encoded as a linkage matrix.
maxRstat(Z, R, i)
Returns the maximum statistic for each non-singleton cluster and its descendents.
Arguments
Returns
• Z
• R
• i
[ndarray] The hierarchical clustering encoded as a matrix. See linkage for more
information.
[ndarray] The inconsistency matrix.
[int] The column of R to use as the statistic.
• MR : ndarray Calculates the maximum statistic for the i’th column of the inconsistency
matrix R for each non-singleton cluster node. MR[j] is the maximum over R[Q(j)-n,
i] where Q(j) the set of all node ids corresponding to nodes below and including j.
3.1. Clustering package (scipy.cluster) 143