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Changed loss to use enum

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This commit is contained in:
Andrew Kane
2021-10-16 18:50:06 -07:00
parent f11e49a272
commit 40accfa870
6 changed files with 57 additions and 35 deletions
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README.md
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@@ -80,41 +80,41 @@ Set parameters - default values below
```rust
libmf::Model::params()
.loss(0) // loss function
.factors(8) // number of latent factors
.threads(12) // number of threads used
.bins(25) // number of bins
.iterations(20) // number of iterations
.lambda_p1(0.0) // coefficient of L1-norm regularization on P
.lambda_p2(0.1) // coefficient of L2-norm regularization on P
.lambda_q1(0.0) // coefficient of L1-norm regularization on Q
.lambda_q2(0.1) // coefficient of L2-norm regularization on Q
.learning_rate(0.1) // learning rate
.alpha(0.1) // importance of negative entries
.c(0.0001) // desired value of negative entries
.nmf(false) // perform non-negative MF (NMF)
.quiet(false); // no outputs to stdout
.loss(libmf::Loss::RealL2) // loss function
.factors(8) // number of latent factors
.threads(12) // number of threads used
.bins(25) // number of bins
.iterations(20) // number of iterations
.lambda_p1(0.0) // coefficient of L1-norm regularization on P
.lambda_p2(0.1) // coefficient of L2-norm regularization on P
.lambda_q1(0.0) // coefficient of L1-norm regularization on Q
.lambda_q2(0.1) // coefficient of L2-norm regularization on Q
.learning_rate(0.1) // learning rate
.alpha(0.1) // importance of negative entries
.c(0.0001) // desired value of negative entries
.nmf(false) // perform non-negative MF (NMF)
.quiet(false); // no outputs to stdout
```
### Loss Functions
For real-valued matrix factorization
- 0 - squared error (L2-norm)
- 1 - absolute error (L1-norm)
- 2 - generalized KL-divergence
- `Loss::RealL2` - squared error (L2-norm)
- `Loss::RealL1` - absolute error (L1-norm)
- `Loss::RealKL` - generalized KL-divergence
For binary matrix factorization
- 5 - logarithmic error
- 6 - squared hinge loss
- 7 - hinge loss
- `Loss::BinaryLog` - logarithmic error
- `Loss::BinaryL2` - squared hinge loss
- `Loss::BinaryL1` - hinge loss
For one-class matrix factorization
- 10 - row-oriented pair-wise logarithmic loss
- 11 - column-oriented pair-wise logarithmic loss
- 12 - squared error (L2-norm)
- `Loss::OneClassRow` - row-oriented pair-wise logarithmic loss
- `Loss::OneClassCol` - column-oriented pair-wise logarithmic loss
- `Loss::OneClassL2` - squared error (L2-norm)
## Metrics