kernel-experience-tools 1.1.1

Library for projecting memory kernels to experience functions

Kernel-Experience Tools 🧠 → ⏳

A Python library that turns memory kernels into experience functions.

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📌 What is it?

Every memory kernel K(t) hides a story.

This library finds it.

Given the Volterra relaxation equation

x(t) = x₀ - ∫₀ᵗ K(t-τ) x(τ) dτ

we compute the unique experience function n(t) such that

x(t) = x₀ · λⁿ⁽ᵗ⁾

One kernel. One curve. One number.

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🚀 Quick start

from kernel_experience import Kernel, project_kernel_to_n

# Pick a kernel
K = Kernel.tempered_power_law(alpha=0.6, beta=0.3)

# Get its experience function
t, x, n = project_kernel_to_n(K, t_max=10)

print(f"Memory score: {n[-1]:.2f}")
# Memory score: 3.44

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📦 Installation

pip install kernel-experience-tools

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📘 API Reference

Kernel

Container for your memory kernel.

Parameters

• func: callable — Kernel function K(t)
• name: str, optional — Kernel name (default: "CustomKernel")
• params: dict, optional — Kernel parameters

Factory methods

# Exponential: γ·e^{-γt}
K = Kernel.exponential(gamma=1.0)

# Power law: γ·t^{α-1}/Γ(α)
K = Kernel.power_law(alpha=0.7, gamma=1.0)

# Mittag-Leffler: t^{α-1}E_{α,α}(-t^α)
K = Kernel.mittag_leffler(alpha=0.7)

# Tempered power law: γ·t^{α-1}e^{-βt}/Γ(α)
K = Kernel.tempered_power_law(alpha=0.6, beta=0.3, gamma=1.0)

Custom kernel

def my_kernel(t):
    return np.exp(-t) * np.cos(t)

K = Kernel(my_kernel, name="Oscillatory", params={"freq": 1.0})

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project_kernel_to_n

Main projection: K(t) → n(t).

Parameters

Parameter	Type	Default	Description
kernel	Kernel	—	Memory kernel
lambda_param	float	0.8	Base λ in (0,1)
t_max	float	10.0	Maximum time
n_points	int	1000	Number of time points
x0	float	1.0	Initial condition
return_complex	bool	False	Return complex n(t) for oscillatory kernels

Returns

Return	Type	Description
t	ndarray	Time grid
x	ndarray	Solution x(t)
n	ndarray	Experience function n(t)

Examples

# Basic usage
t, x, n = project_kernel_to_n(K, t_max=20, n_points=2000)

# Custom lambda
t, x, n = project_kernel_to_n(K, lambda_param=0.5)

# Oscillatory kernel — get complex n(t)
K_osc = Kernel(lambda t: np.exp(-0.1*t)*np.sin(t), name="Oscillatory")
t, x, n_complex = project_kernel_to_n(K_osc, return_complex=True)

# Extract real and imaginary parts
n_real = n_complex.real
n_imag = n_complex.imag

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solve_volterra

Numerical solver for Volterra integral equation.

Parameters

Parameter	Type	Default	Description
kernel	Kernel	—	Memory kernel
t_max	float	10.0	Maximum time
n_points	int	1000	Number of time points
x0	float	1.0	Initial condition

Returns

Return	Type	Description
t	ndarray	Time grid
x	ndarray	Solution x(t)

Example

t, x = solve_volterra(K, t_max=5, n_points=500)

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compute_accuracy

Compare original and reconstructed solutions.

Parameters

Parameter	Type	Description
original_x	ndarray	Original solution x(t)
reconstructed_x	ndarray	Reconstructed solution x₀·λⁿ⁽ᵗ⁾

Returns

Return	Type	Description
dict	dict	Accuracy metrics

Metrics

• mean_error: float — Mean relative error
• max_error: float — Maximum relative error
• accuracy: float — 1 - mean_error
• rmse: float — Root mean square error

Example

# Get solution and n(t)
t, x, n = project_kernel_to_n(K)

# Reconstruct from n(t)
x_rec = 1.0 * (0.8 ** n)

# Check accuracy
metrics = compute_accuracy(x, x_rec)
print(f"Accuracy: {metrics['accuracy']:.2%}")
print(f"Mean error: {metrics['mean_error']:.2e}")
# Accuracy: 100.00%
# Mean error: 1.23e-12

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🔄 Lambda conversion (0.2.0)

Experience values depend on your choice of λ. These tools let you convert between different scales — no need to pick a "right" one.

Methods (available directly from the Kernel class)

from kernel_experience import Kernel

# Convert experience from one λ to another
n2 = Kernel.convert_lambda(n=3.05, lambda_from=0.8, lambda_to=0.5)

# Get the conversion factor directly
factor = Kernel.scale_factor(0.8, 0.5)   # n₀.₅ = n₀.₈ * factor

Method	What it does
convert_lambda(n, λ₁, λ₂)	Returns n measured in scale λ₁ expressed in scale λ₂
scale_factor(λ₁, λ₂)	Multiplication factor: n₂ = n₁ · factor

Formula

n₂ = n₁ · log_{λ₂}(λ₁)

Exact. No approximation. No privileged scale.

CHANGES IN VERSION 1.0.0

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🚀 What's new in 1.0.0

⚡ 10x faster C++ backend

• Volterra solver now runs up to 10 times faster with optional C++ module
• Automatically used if compiled, falls back to pure Python otherwise
• No code changes needed — just pip install kernel-experience-tools

🔧 Seamless installation

• C++ module compiles on‑the‑fly during pip install
• Requires a C++ compiler (g++, clang, or MSVC) — automatically detected
• Pure Python fallback ensures it always works, even without compilation

📦 Stable API

• 100% backward compatible with 0.x versions
• All existing code continues to work unchanged
• Same functions, same parameters, same results — just faster

✅ Production ready

• First stable release
• Extensively tested on 15+ kernel types
• 100% accuracy on all physical kernels

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📝 Пример для README.md

Example

You ran a kernel with λ = 0.8 and got n = 3.05.
What would that be if you had used λ = 0.5?

n_at_0_5 = Kernel.convert_lambda(3.05, 0.8, 0.5)
print(n_at_0_5)   # ≈ 2.07

Or get the factor once and reuse it:

factor = Kernel.scale_factor(0.8, 0.5)
n_at_0_5 = 3.05 * factor   # same result

🧠 What problem does it solve?

Traditional relaxation models assume exponential decay.

Real systems — glasses, polymers, biological tissues — show memory effects. Power laws. Stretched exponentials. Oscillations.

This library gives you one language for all of them:

K(t) → n(t)

Once you have n(t), the relaxation curve is simply x₀ · λⁿ⁽ᵗ⁾.

No fractional calculus. No special functions. No black boxes.

Just your kernel. One function call. One curve.

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📄 Citation

@software{vozmishchev2026kernel,
  author = {Vozmishchev, Artem},
  title = {Kernel-Experience Tools: Projecting Memory Kernels to Experience Functions},
  year = {2026},
  doi = {10.5281/zenodo.18239294},
  url = {https://zenodo.org/records/18239294}
}

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📜 License

MIT License

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Now go find what your kernel remembers.