pyemma.coordinates.transform.PCA

class pyemma.coordinates.transform.PCA(*args, **kwargs)

Principal component analysis.

__init__(dim=- 1, var_cutoff=0.95, mean=None, stride=1, skip=0)

Principal component analysis.

Given a sequence of multivariate data \(X_t\), computes the mean-free covariance matrix.

\[C = (X - \mu)^T (X - \mu)\]

and solves the eigenvalue problem

\[C r_i = \sigma_i r_i,\]

where \(r_i\) are the principal components and \(\sigma_i\) are their respective variances.

When used as a dimension reduction method, the input data is projected onto the dominant principal components.

Parameters

• dim (int, optional, default -1) – the number of dimensions (independent components) to project onto. A call to the map function reduces the d-dimensional input to only dim dimensions such that the data preserves the maximum possible autocorrelation amongst dim-dimensional linear projections. -1 means all numerically available dimensions will be used unless reduced by var_cutoff. Setting dim to a positive value is exclusive with var_cutoff.

• var_cutoff (float in the range [0,1], optional, default 0.95) – Determines the number of output dimensions by including dimensions until their cumulative kinetic variance exceeds the fraction subspace_variance. var_cutoff=1.0 means all numerically available dimensions (see epsilon) will be used, unless set by dim. Setting var_cutoff smaller than 1.0 is exclusive with dim

• mean (ndarray, optional, default None) – Optionally pass pre-calculated means to avoid their re-computation. The shape has to match the input dimension.

• skip (int, default 0) – skip the first n frames of each trajectory.

Methods

_Loggable__create_logger()
_SerializableMixIn__interpolate(state, klass)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__([dim, var_cutoff, mean, stride, skip])	Principal component analysis.
__init_subclass__(*args, **kwargs)	This method is called when a class is subclassed.
__iter__()
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__my_getstate__()
__my_setstate__(state)
__ne__(value, /)	Return self!=value.
__new__(cls, *args, **kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__setstate__(state)
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_check_estimated()
_chunk_finite(data)
_cleanup_logger(logger_id, logger_name)
_clear_in_memory()
_compute_default_cs(dim, itemsize[, logger])
_create_iterator([skip, chunk, stride, …])	Should be implemented by non-abstract subclasses.
_data_flow_chain()	Get a list of all elements in the data flow graph.
_diagonalize()
_estimate(iterable, **kw)
_get_classes_to_inspect()	gets classes self derives from which 1.
_get_interpolation_map(cls)
_get_param_names()	Get parameter names for the estimator
_get_private_field(cls, name[, default])
_get_serialize_fields(cls)
_get_state_of_serializeable_fields(klass, state)	:return a dictionary {k:v} for k in self.serialize_fields and v=getattr(self, k)
_get_traj_info(filename)
_get_version(cls[, require])
_get_version_for_class_from_state(state, klass)	retrieves the version of the current klass from the state mapping from old locations to new ones.
_init_covar(partial_fit, n_chunks)
_logger_is_active(level)	@param level: int log level (debug=10, info=20, warn=30, error=40, critical=50)
_map_to_memory([stride])	Maps results to memory.
_set_random_access_strategies()
_set_state_from_serializeable_fields_and_state(…)	set only fields from state, which are present in klass.__serialize_fields
_source_from_memory([data_producer])
_transform_array(X)	Projects the data onto the dominant principal components.
describe()	Get a descriptive string representation of this class.
dimension()	output dimension
estimate(X, **kwargs)	Estimates the model given the data X
fit(X[, y])	Estimates parameters - for compatibility with sklearn.
fit_transform(X[, y])	Fit to data, then transform it.
get_output([dimensions, stride, skip, chunk])	Maps all input data of this transformer and returns it as an array or list of arrays
get_params([deep])	Get parameters for this estimator.
iterator([stride, lag, chunk, …])	creates an iterator to stream over the (transformed) data.
load(file_name[, model_name])	Loads a previously saved PyEMMA object from disk.
n_chunks(chunksize[, stride, skip])	how many chunks an iterator of this sourcde will output, starting (eg.
n_frames_total([stride, skip])	Returns total number of frames.
number_of_trajectories([stride])	Returns the number of trajectories.
output_type()	By default transformers return single precision floats.
partial_fit(X)
save(file_name[, model_name, overwrite, …])	saves the current state of this object to given file and name.
set_params(**params)	Set the parameters of this estimator.
trajectory_length(itraj[, stride, skip])	Returns the length of trajectory of the requested index.
trajectory_lengths([stride, skip])	Returns the length of each trajectory.
transform(X)	Maps the input data through the transformer to correspondingly shaped output data array/list.
write_to_csv([filename, extension, …])	write all data to csv with numpy.savetxt
write_to_hdf5(filename[, group, …])	writes all data of this Iterable to a given HDF5 file.

Attributes

_DataSource__serialize_fields
_Estimator__serialize_fields
_FALLBACK_CHUNKSIZE
_InMemoryMixin__serialize_fields
_InMemoryMixin__serialize_version
_Loggable__ids
_Loggable__refs
_PCA__serialize_version
_SerializableMixIn__serialize_fields
_SerializableMixIn__serialize_modifications_map
_SerializableMixIn__serialize_version
__abstractmethods__
__dict__
__doc__
__module__
__weakref__	list of weak references to the object (if defined)
_abc_impl
_estimated
_loglevel_CRITICAL
_loglevel_DEBUG
_loglevel_ERROR
_loglevel_INFO
_loglevel_WARN
_save_data_producer
_serialize_version
chunksize	chunksize defines how much data is being processed at once.
cumvar
data_producer	The data producer for this data source object (can be another data source object).
default_chunksize	How much data will be processed at once, in case no chunksize has been provided.
eigenvalues
eigenvectors
feature_PC_correlation	Instantaneous correlation matrix between input features and PCs
filenames	list of file names the data is originally being read from.
in_memory	are results stored in memory?
is_random_accessible	Check if self._is_random_accessible is set to true and if all the random access strategies are implemented.
is_reader	Property telling if this data source is a reader or not.
logger	The logger for this class instance
mean
model	The model estimated by this Estimator
name	The name of this instance
ndim
ntraj
ra_itraj_cuboid	Implementation of random access with slicing that can be up to 3-dimensional, where the first dimension corresponds to the trajectory index, the second dimension corresponds to the frames and the third dimension corresponds to the dimensions of the frames.
ra_itraj_jagged	Behaves like ra_itraj_cuboid just that the trajectories are not truncated and returned as a list.
ra_itraj_linear	Implementation of random access that takes arguments as the default random access (i.e., up to three dimensions with trajs, frames and dims, respectively), but which considers the frame indexing to be contiguous.
ra_linear	Implementation of random access that takes a (maximal) two-dimensional slice where the first component corresponds to the frames and the second component corresponds to the dimensions.