Sparse-Optimization-Toolbox 0.0.1

Package for Sparse optimization

An sparse opyimization toolbox contains test data generation and network reasoning

Test Data Generation

Import

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

Relationship between parameters: $$W_d*z_{Network} = x_{TargetGene}+\mu$$ $$\mu \sim N(normalLoc, normalScale)$$