KO
|
EN
gitlite — search
Search
#python
#java
#python3
#arduino
#golang
#machine-learning
#rust
#html
#flask
#javascript
#seismology
#nodejs
structure_index
★ 18
Open GitHub ↗
Structure Index code repository
Download README (.md)
Explore Similar Repositories
ob-samples
:
Show how to use OceanBase, providing the sample projects for OceanBase.
wikipedia-search
:
Allows users to quickly search highlighted items on Wikipedia. Inspired by the "search Wikipedia" function on the kindle mobile app.
laravel-tools
:
A tools for all laravel developers.
Idle-Action-Farm
:
A prototype of hypercasual game
GeekSynergy-Machine-Test
:
No description available.
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
structure_index
?
Download (.md)
# Structure Index Welcome! This repository hosts the implementation for the Structure Index (SI), a graph-based topological metric able to quantify the amount of structure present at the distribution of a given feature over a point cloud in an arbitrary D-dimensional space. See the [publication](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011768) for specific details and follow [this notebook](https://colab.research.google.com/github/PridaLab/structure_index/blob/main/demos/structure_index_demo.ipynb) for a step by step demo. ## The method Identifying the structure (or lack thereof) of the distribution of a given feature over a point cloud is a general research question. In the neuroscience field, this problem arises while investigating representations over neural manifolds (e.g., spatial coding), in the analysis of neurophysiological signals (e.g., auditory coding) or in anatomical image segmentation. The SI is defined from the overlapping distribution of data points sharing similar feature values in a given neighborhood. It can be applied to both scalar and vectorial features permitting quantification of the relative contribution of related variables. The following image illustrates the concepts behind this method:  **A**, Feature gradient distribution in a 2D-ellipsoid data cloud. Each point in the data cloud is assigned to a group associated with a feature bin value (bin-group). **B**, **C**, Next, the overlapping matrix between bin-groups is computed. **D**, The overlapping matrix represents a connection graph between bin-groups, where structure (overlapping, clustering, etc..) can be quantified using the SI from 0 (random, equivalent to full overlapping) to 1 (maximal separation, equivalent to zero overlapping between bins). **E**, The case of a randomly distributed feature in a 2D data cloud. ## How to use it [This notebook](https://colab.research.google.com/github/PridaLab/structure_index/blob/main/demos/structure_index_demo.ipynb) illustrates one example of how to use the Structure Index to quantify structure. Follow and execute each block to generate syntethic data and to compute the structure present on it. Moreover it includes a section to use the built-in function of the repo 'draw_graph' to visualize the resulting weighted directed graph. In sort, the way to compute the structure index is as follows: ``` SI, bin_label, overlap_mat, shuf_SI = compute_structure_index(data, label) ``` ### Parameters data: numpy 2d array of shape [n_samples,n_dimensions] Array containing the signal label: numpy 2d array of shape [n_samples,n_features] Array containing the labels of the data. It can either be a column vector (scalar feature) or a 2D array (vectorial feature) ### Optional parameters n_bins: integer (default: 10) number of bin-groups the label will be divided into (they will become nodes on the graph). For vectorial features, if one wants different number of bins for each entry then specify n_bins as a list (i.e. [10,20,5]). Note that it will be ignored if 'discrete_label' is set to True. dims: list of integers or None (default: None) list of integers containing the dimensions of data along which the structure index will be computed. Provide None to compute it along all dimensions of data. distance_metric: str (default: 'euclidean') Type of distance used to compute the closest n_neighbors. See 'distance_options' for currently supported distances. n_neighbors: int (default: 15) Number of neighbors used to compute the overlapping between bin-groups. This parameter controls the tradeoff between local and global structure. discrete_label: boolean (default: False) If the label is discrete, then one bin-group will be created for each discrete value it takes. Note that if set to True, 'n_bins' parameter will be ignored. num_shuffles: int (default: 100) Number of shuffles to be computed. Note it must fall within the interval [0, np.inf). verbose: boolean (default: False) Boolean controling whether or not to print internal process.. ### Returns: SI: float structure index bin_label: tuple Tuple containing: [0] Array indicating the bin-group to which each data point has been assigned. [1] Array indicating feature limits of each bin-group. Size is [number_bin_groups, n_features, 3] where the last dimension contains [bin_st, bin_center, bin_en] overlap_mat: numpy 2d array of shape [n_bins, n_bins] Array containing the overlapping between each pair of bin-groups. shuf_SI: numpy 1d array of shape [num_shuffles,] Array containing the structure index computed for each shuffling iteration.
