cognitive-discovery-system-v2

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.

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cognitive-discovery-system-v2 3.3.0

cognitive-discovery-system-v2

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