This code implements a Particle-Mesh inspired t-SNE.
The Particle-Mesh algorithm (PM) is well used in physics to solve the N-body problem. It is based on the ability of FFTs to transform convolution products in simple products. The main idea is to compute a potential, and get back the forces by deriving it.
Python 3.6.9 was used during the development of this code.
Check the 'requirements' file to install dependencies.
- Annoy 1.16.3
- Cython 0.29.21
- Numpy 1.19.2
- Sklearn 0.23.2
- Scipy 1.5.2
>> python setup.py build_ext --inplaceIf matplotlib is installed, you can try...
>> python test_PM.py... in order to test the implementation on the 70 000 instances of MNIST.
import PM_tSNE
tsne = PM_tSNE.PM_tSNE(n_iter=750, coeff=8.0, grid_meth='NGP')
# Load the 70 000 instances of MNIST (already prepared for t-SNE: standardized + reduced to 50 features with PCA)
X = np.load('./MNIST_data.npy', allow_pickle=True)
Embedding = tsne.fit_transform(X)