Faphouse Github Link 〈Fast BREAKDOWN〉

| Milestone | Target Release | Description | |-----------|----------------|-------------| | v1.3 | Q4 2026 | GPU‑accelerated VI via torch + support for mixed‑precision training. | | v1.4 | Q2 2027 | Integration with scikit‑learn pipelines (fit_transform, set_params). | | v2.0 | Q4 2027 | Full tensor‑factorization extension (FA for multi‑way data). | | v2.1 | 2028 | Interactive web UI (Streamlit/Dash) for non‑programmers. |

Contributors are encouraged to prioritize issues tagged good first issue or enhancement. faphouse github link

# Stable release from PyPI
pip install faphouse
# Or install the latest development version directly from GitHub
pip install git+https://github.com/faphouse/faphouse.git

import numpy as np
import faphouse as fp
# Simulated data: 500 samples, 30 observed variables
np.random.seed(42)
X = np.random.randn(500, 30)
# Fit a 5‑factor model using the default EM optimizer
model = fp.FactorAnalysis(n_factors=5, method='em')
model.fit(X)
# Retrieve latent scores and loadings
scores = model.transform(X)          # shape: (500, 5)
loadings = model.loadings_           # shape: (30, 5)
print("Explained variance per factor:", model.explained_variance_)

You should see a printed array of variance contributions and a convergence log in the console. | Milestone | Target Release | Description |

Faphouse is a website that hosts adult content, including videos and images. On the other hand, GitHub is a platform where developers can share and collaborate on code. If Faphouse has an open-source project or a repository on GitHub, it would typically be used for hosting, collaborating on, or showcasing their software projects. # Stable release from PyPI pip install faphouse

model = fp.FactorAnalysis(
    n_factors=8,
    method='vi',
    regularizer='l1',
    alpha=0.01,
    max_iter=1000,
    device='cuda'   # if a GPU is available
)
model.fit(X)
print("ELBO:", model.elbo_)

The elbo_ attribute stores the Evidence Lower Bound at each iteration, which can be plotted with model.plot_convergence().