rmcgibbo/mixtape — pull request #325 by rmcgibbo (2014-11-21 22:44:01+00:00)
Title: command line stuff

1. sorting the command line
2. printing multiple paragraphs for the description of each class, if that's what the docstring contains
3. more commands

```
$ msmb -h
usage: msmb [-h] ...

Construct models of high-dimensional time-series data.

optional arguments:
-h, --help show this help message and exit

commands:

AtomIndices Create index file for atoms or distance pairs.
AtomPairsFeaturizer Featurizer based on atom pair distances.
ContactFeaturizer Featurizer based on residue-residue distances.
DRIDFeaturizer Featurizer based on distribution of reciprocal interatomic distances (DRID).
DihedralFeaturizer Featurizer based on dihedral angles.
SuperposeFeaturizer Featurizer based on euclidian atom distances to reference structure.
KCenters K-Centers clustering.
KMeans K-Means clustering.
KMedoids K-Medoids clustering.
MiniBatchKMeans Mini-Batch K-Means clustering.
MiniBatchKMedoids Mini-Batch K-Medoids clustering.
PCA Principal component analysis (PCA).
tICA Time-structure Independent Component Analysis (tICA).
AlanineDipeptide Alanine dipeptide dataset.
DoubleWell Brownian dynamics on a 1D double well potential.
FsPeptide Fs peptide (implicit solvent) dataset.
MetEnkephalin Loader for the met-enkephalin dataset.
QuadWell Brownian dynamics on a 1D four well potential.
GaussianFusionHMM Reversible Gaussian Hidden Markov Model L1-Fusion Regularization.
BayesianMarkovStateModel
Bayesian Markov state model.
```

```
$ msmb PCA -h
usage: msmb PCA [-h] --inp INP [--out OUT] [--transformed TRANSFORMED]
[--n_components N_COMPONENTS] [--no-copy] [--no-whiten]

Principal component analysis (PCA). Linear dimensionality reduction using
Singular Value Decomposition of the data and keeping only the most significant
singular vectors to project the data to a lower dimensional space.

This implementation uses the scipy.linalg implementation of the singular value
decomposition. It only works for dense arrays and is not scalable to large
dimensional data.

The time complexity of this implementation is ``O(n ** 3)`` assuming n ~
n_samples ~ n_features.
```