https://github.com/ieee8023/sparse-coding
https://github.com/ieee8023/sparse-coding
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/ieee8023/sparse-coding
- Owner: ieee8023
- Created: 2019-06-15T17:25:15.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-06-18T15:48:36.000Z (over 6 years ago)
- Last Synced: 2025-04-06T22:51:13.507Z (9 months ago)
- Language: Jupyter Notebook
- Size: 186 KB
- Stars: 6
- Watchers: 0
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Comparison of sparse coding algorithms
Authors: Joseph Paul Cohen, Shawn Tan, Giancarlo Kerg, Yannick Pouliot, and Yoshua Bengio.
(Much of the code comes from other sources)
Here there are implementations of the following methods:
- Random
- Iterative Shrinkage-Thresholding Algorithm (ISTA)
- Iterative Shrinkage-Thresholding Algorithm (ISTA) Non-Negative
- Orthogonal Matching Pursuit
- Lasso Coordinate Descent
- Non-Negative Least Squares
- Bounded-Variable Least-Squares
- Differential Evolution
- Non-negative Matrix Factorization (NMF)
- Sparse Pseudo Inverse
The above plot was made with the following code:
```python
errorss = []
solvers_list = [solvers.DifferentialEvolution(strategy="best1bin",max_h=90),
solvers.Random(num_tries=10000, max_h=10),
solvers.ISTA(),
solvers.ISTA_NN(),
solvers.OrthogonalMatchingPursuit(),
solvers.ISTA_NN(0.001,50.0),
solvers.NMF(),
solvers.NMF(beta_loss="kullback-leibler", solver="mu"),
solvers.BVLS(max_h=90),
solvers.Random(num_tries=1000, max_h=90),
solvers.SparsePseudoInverse(),
solvers.LassoCoordinateDescent(alpha=0.01)
]
for solver in solvers_list:
errors = []
for i in range(30):
dataset = datasets.NoisySparseRandomDataset(n_in=5, n_out=30, sparsity=0.2, noise=i)
hp = solver.solve(dataset.get_D(),dataset.get_signals())
hp = np.maximum(hp,0)
l1 = np.abs((dataset.get_h() - hp)).mean()
l2 = (((dataset.get_h() - hp))**2).mean()
errors.append(l2)
errorss.append(errors)
plt.rcParams['figure.figsize'] = (15, 5)
linestyles = ['_', '-', '--', ':']
for lines in errorss:
plt.plot(lines,np.random.choice(linestyles))
plt.legend([str(e) for e in solvers_list])
plt.xlabel("Gaussian Noise");
plt.ylabel("Mean Squared Error");
```