cognitive-discovery-system-v2

CI PyPI Python License: MIT Code style: ruff

CDS v2 is a scientific computing platform built on the scientific Python stack — NumPy, SciPy, pandas and matplotlib. The algorithms proven in the pure-Python cognitive-discovery-system (v1.x) form its foundation; v2 rebuilds them for speed and adds new domain modules on top.

Installation

pip install cognitive-discovery-system-v2

From source:

git clone https://github.com/Furox88/cognitive-discovery-system-v2.git
cd cognitive-discovery-system-v2
pip install -e .[dev]

Quick start

import numpy as np
import cds2

# Linear algebra
A = [[3.0, 1.0], [1.0, 2.0]]
b = [9.0, 8.0]
x = cds2.linalg.solve(A, b)

# Statistics
r = cds2.stats.independent_t_test([1, 2, 3, 4, 5], [3, 4, 5, 6, 7])

# Optimization
res = cds2.optimize.minimize(lambda v: (v[0] - 2) ** 2 + (v[1] + 1) ** 2, x0=[0.0, 0.0])
print(res.x)  # ~ [2.0, -1.0]

# Signals
freqs, psd = cds2.signals.power_spectrum(np.sin(np.linspace(0, 100, 1024)), fs=256.0)

# Graphs with PageRank
adj = cds2.graph.from_edges(4, [(0, 1), (0, 2), (1, 3), (2, 3)], directed=True)
scores = cds2.graph.pagerank(adj)

Modules

Module Built on Highlights
cds2.linalg NumPy solve, det, inv, pinv, eig/eigh, SVD, least squares, cholesky, cond
cds2.stats scipy.stats t-tests, ANOVA, non-parametrics, correlations, chi-square, effect sizes, normal dist helpers
cds2.optimize scipy.optimize minimize, roots (brentq/newton/system), linprog, least squares, curve fit
cds2.integrate scipy.integrate quad, 2-D/3-D integration, ODE solvers, trapezoid/simpson
cds2.interpolate scipy.interpolate linear/cubic/pchip, lagrange, griddata, regular grids
cds2.signals scipy.signal FFT, PSD/welch/spectrogram, Butterworth filters, peaks, envelope
cds2.montecarlo NumPy Generator pi estimate, MC integration/expectation, hit-or-miss (all seedable)
cds2.graph scipy.sparse.csgraph components, Dijkstra/Bellman-Ford/Floyd-Warshall, MST, topological order, PageRank
cds2.ml NumPy/SciPy LinearRegression, LogisticRegression, KMeans++, PCA, KNN, metrics, data generators
cds2.timeseries pandas moving average, EWM, differencing, seasonal decomposition, ACF/PACF, Ljung-Box
cds2.viz matplotlib series/histogram/scatter/heatmap/spectrum/regression/confusion plots
cds2.io pandas CSV/JSON read-write, optional Excel/Parquet bridges, DataFrame summaries
cds2.calculus NumPy derivative, complex-step gradient, jacobian, hessian
cds2.special scipy.special gamma, erf family, beta, Bessels, zeta
cds2.sparse scipy.sparse.linalg CG/GMRES/BiCGSTAB solvers, Lanczos eigenpairs, truncated SVD
cds2.distributions scipy.stats t, chi2, F, exponential, uniform, lognormal, poisson, binomial (pdf/cdf/ppf)
cds2.spectral scipy.sparse Laplacians, Fiedler vector, algebraic connectivity, spectral clustering

CLI

cds2 info
cds2 stats 1,2,3,4,5
cds2 integrate sin --a 0 --b 3.14159
cds2 linsolve --a "3,1;1,2" --b "9,8"
cds2 plot 1,3,2,5,4 --file out.png

Relationship to CDS v1.x

The original zero-dependency pure-Python line lives at Furox88/cognitive-discovery-system and remains available. v2 is an independent project that trades that constraint for the speed and breadth of the scientific Python ecosystem.

Runnable case studies live in examples/ - see the docs page for details.

Benchmarks

cds2 races the scientific stack head-to-head — and ships its own compiled C kernels where they help. Current scoreboard (full methodology in docs/benchmarks.md):

Race Baseline cds2/baseline
PageRank 400n (C kernel) NetworkX 0.18x
K-Means 4k×2 k=8 (C kernel) scikit-learn 0.72x
Linear regression 20k×10 scikit-learn 0.74x
Monte Carlo pi 2M hand-vectorized NumPy 0.77x
solve / eigh / rfft / welch / minimize NumPy & SciPy ~1.00x
describe 500k (adds quartiles) SciPy 1.10x

Wrapper APIs hold parity with raw NumPy/SciPy; the KMeans Lloyd loop and PageRank power iteration are from-scratch C extensions (cds2._fast_kmeans, cds2._fast_pagerank) that beat the specialist libraries. A pure-Python fallback wheel keeps compiler-less installs working.

python benchmarks/run_benchmarks.py            # full run
python benchmarks/run_benchmarks.py --quick    # smoke run

Development

pip install -e .[dev]
pytest            # run the test suite
ruff check .      # lint

License

MIT — see LICENSE.

Project links

Data verified by PyPI on Aug 22, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (4.0.0)

1 maintainer

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Avatar for furkannarkn from gravatar.com furkannarkn

Credits

Author: Furox88

GitHub Statistics

Data verified by PyPI on Aug 22, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

dev docs all

Tags

scientific-computing numpy scipy pandas matplotlib machine-learning signal-processing statistics optimization monte-carlo graph-theory pagerank time-series numerical-methods data-analysis

Classifiers

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4.0.0

1 file

3.3.0

22 files

3.2.1

22 files

3.2.0

22 files

This release

3.1.0 This release

22 files

3.0.0

22 files

2.6.0

22 files

2.5.1

22 files

2.5.0

22 files

2.4.0

22 files

2.3.0

22 files

2.2.0

22 files

2.1.0

22 files

2.0.0

2 files

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