Covariate kernel selection

The kernel to apply to the covariates can be changed (Gaussian or Delta).

Bugfix

Fixed covariate correction.

Added support for covariates

In addition, default version of HSIC Lasso is now the block version. Vanilla, non-block, HSIC Lasso can be used by setting block size B to 0.

Fix ndarray input function

The ndarray input function was not properly working.

1. The feature name index was stored as integer (this should be string).
2. We could not specify feature name.

Fixed Gaussian kernel width in multi-variate regressions.

Fixed Gaussian kernel width in multi-variate regressions.

Bugfixes

• Fixed problems when dealing with singular matrices.

• Fixed ndarrays input not having featnames.

Improved performance

pyHSIC lasso speed up by ~30% by changing float64 to float32. Memory requirements halved.

Normalization at the whole dataset

• Normalization is performed over the whole dataset, instead of on batches. This should result in a more robust normalization, and hence, better results.

Support multi-variate output and parallel processing

• Support parallel processing
• Multi-variate output support

Added Hierarchical Clustering

• Added Hierarchical Clustering
  • Pull request #8
• Remove error when B is not a divisor of # features
  • Pull request #13
• accept discrete x
  • Pull request #14