Reference¶
MELD density estimation¶
- class meld.meld.MELD(beta=60, offset=0, order=1, filter='heat', solver='chebyshev', chebyshev_order=50, lap_type='combinatorial', sample_normalize=True, anisotropy=1, n_landmark=None, **kwargs)[source]¶
MELD operator for filtering signals over a graph.
- Parameters
beta (int, optional, Default: 60) – Amount of smoothing to apply. Default value of 60 determined through analysis of simulated data using Splatter.
offset (float, optional, Default: 0) – Amount to shift the MELD filter in the eigenvalue spectrum. Recommend using an eigenvalue from the graph based on the spectral distribution. Should be in interval [0,1]
order (int, optional, Default: 1) – Falloff and smoothness of the filter. High order leads to square-like filters.
filter (str, optional, Default: 'heat') – Filter type to use. Should be in [‘heat’, ‘laplacian’]
solver (string, optional, Default: 'chebyshev') – Method to solve convex problem. ‘chebyshev’ uses a chebyshev polynomial approximation of the corresponding filter. ‘exact’ uses the eigenvalue solution to the problem
chebyshev_order (int, optional, Default: 50) – Order of chebyshev approximation to use.
lap_type (('combinatorial', 'normalized'), Default: 'combinatorial') – The kind of Laplacian to calculate
sample_normalize (boolean, optional, Default: True) – If True, the sample indicator vectors are column normalized to sum to 1
- property beta¶
Amount of smoothing to apply. Default value of 60 determined throughanalysis of simulated data using Splatter
- property chebyshev_order¶
Order of chebyshev approximation to use.
- property filter¶
Filter type to use. Should be in [‘heat’, ‘laplacian’]
- fit_transform(X, sample_labels, **kwargs)[source]¶
Builds the MELD filter over a graph built on data X and estimates density of each sample in sample_labels
- Parameters
X (array-like, shape=[n_samples, m_features]) – Data on which to build graph to perform data smoothing over.
sample_labels (array-like, shape=[n_samples, p_signals]) – 1- or 2-dimensional array of non-numerics indicating the sample origin for each cell.
kwargs (additional arguments for graphtools.Graph) –
- Returns
sample_densities – Density estimate for each sample over a graph built from X
- Return type
ndarray, shape=[n_samples, p_signals]
- property lap_type¶
The kind of Laplacian to calculate
- property offset¶
Amount to shift the MELD filter in the eigenvalue spectrum.Recommend using an eigenvalue from the graph based on thespectral distribution. Should be in interval [0,1]
- property order¶
Falloff and smoothness of the filter.High order leads to square-like filters.
- property sample_densities¶
Density associated with each sample
- property solver¶
Method to solve convex problem.’chebyshev’ uses a chebyshev polynomial approximation of the correspondingfilter. ‘exact’ uses the eigenvalue solution to the problem
Vertex Frequency Clustering¶
- class meld.cluster.VertexFrequencyCluster(n_clusters=10, likelihood_bias=1, window_count=9, window_sizes=None, sparse=False, suppress=False, random_state=None, **kwargs)[source]¶
Bases:
BaseEstimator
- Performs Vertex Frequency clustering for data given a
raw experimental signal and enhanced experimental signal.
- Parameters
n_clusters (int, optional, default: 10) – The number of clusters to form.
likelihood_bias (float, optional, default: 1) – A normalization term that biases clustering towards the likelihood (higher values) or towards the spectrogram (lower values)
window_count (int, optional, default: 9) – Number of windows to use if window_sizes = None
window_sizes (None, optional, default: None) – ndarray of integer window sizes to supply to t
sparse (bool, optional, default: False) – Use sparse matrices. This is significantly slower, but will use less memory
suppress (bool, optional) – Suppress warnings
random_state (int or None, optional (default: None)) – Random seed for clustering
**kwargs – Description
- Raises
NotImplementedError – Window functions are not implemented
Examples
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance