An sparse opyimization toolbox contains test data generation and network reasoning
Test Data Generation
Import
from sparsetools import matCreater
Data generation
matCreater.matCreater(tfLen=10, sampleNums=200, geneNums=2000, normalLoc=0, normalScale=0.1)
| Parameter | Type | Explanation |
|---|---|---|
| tfLen | int | The numbers of transcribe factors |
| sampleNums | int | The numbers of transcribe samples |
| geneNums | int | The numbers of target genes |
| normalLoc | float: recommond use 0 | Mean value of Gaussian noise |
| normalScale | float | Variance of Gaussian noise |
Return:
| Parameter | Type | Shapes | Explanation |
|---|---|---|---|
| W_d | np.array | (tfLen, sampleNums) | Over complete dictionary |
| zNetwork | np.array | (geneNums, tfLen) | Sparse matrix |
| xTargetGene | np.array | (geneNums,sampleNums) | Target |
Network reasoning
from sparsetools import Optimization
Optimization.voting(expre, tf_names, gene_names)
| Parameter | Type | Explanation |
|---|---|---|
| expre | np.array | The expresion matrix of genes |
| tf_names | np.array | Names of tf |
| gene_names | np.array | Name of all genes(including TF) |
Return:
The moderation network result matrix of the voting algorithm, while the results of the independent algorithm are named with the algorithm name and stored locally.
1 maintainer
Release files for Sparse-Optimization-Toolbox 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| Sparse-Optimization-Toolbox-0.1.0.tar.gz | 2.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| Sparse_Optimization_Toolbox-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.7 kB
Release files / Sparse-Optimization-Toolbox-0.1.0.tar.gz
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