BINLESSINFO Compute information components using binless method.
[I_PART,I_CONT,I_COUNT,I_TOTAL] =
BINLESSINFO(X,COUNTS,CATEGORIES,M,OPTS) computes the various
components of information in the matrix of embedded data X. COUNTS
is a vector of the number of points in the data from which X is
derived (e.g., spike counts), and CATEGORIES is a vector of
category indices, as obtained from BINLESSOPEN. M is the number of
categories.
I_PART is the information conveyed by zero-distance data and
singletons. I_CONT is the continuous component of the information
which describes the separability of the embedded data. I_PART and
I_CONT sum to give the "timing" component of the information.
I_COUNT is the information conveyed by the number of points in the
data. I_TOTAL is the sum of all of the components. While I_CONT is
a scalar, I_PART, I_COUNT, and I_TOTAL are structures of type
ESTIMATE.
OPTS.min_embed_dim: The minimal embedding dimension for
episodic data. The default is 1. (Related option
OPTS.max_embed_dim is used by BINLESSEMBED.)
OPTS.cont_min_embed_dim: The minimal embedding dimension for
continuous data. The default is 0. (Related option
OPTS.cont_max_embed_dim is used by BINLESSEMBED.)
OPTS.stratification_strategy: The strategy for stratifying data
by the number of points.
OPTS.stratification_strategy=0 puts all data in a single
stratum.
OPTS.stratification_strategy=1 stratifies data by the number
of points. For continuous data in which the number of
samples on each trial is the same (as is typical), this
is equivalent to OPTS.stratification_strategy=0. For
episodic data, each spike count gets its own stratum.
OPTS.stratification_strategy=2 is similar to option 1
except that all data with more than
OPTS.embed_dim_max-OPTS.embed_dim_min points go into a
single stratum.
The default value is 2 for episodic data and 0 for
continuous data.
OPTS.singleton_strategy: The strategy for handling
singletons.
OPTS.singleton_strategy=0 means that singletons are
considered uninformative and are ignored.
OPTS.singleton_strategy=1 means that singletons are
considered maximally informative and are included.
The default value is 0.
[I_PART,I_CONT,I_COUNT,I_TOTAL] =
BINLESSINFO(X,COUNTS,CATEGORIES,M) uses the default options and
parameters.
[I_PART,I_CONT,I_COUNT,I_TOTAL,OPTS_USED] =
BINLESSINFO(X,COUNTS,CATEGORIES,M) or
[I_PART,I_CONT,I_COUNT,I_TOTAL,OPTS_USED] =
BINLESSINFO(X,COUNTS,CATEGORIES,M,OPTS) additionally return the
options used.
See also BINLESSOPEN, BINLESSEMBED, BINLESSWARP.
This function calls: