Feyn

Feyn

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›Essentials

Getting Started

  • Quick start
  • Using Feyn
  • Installation
  • Transition to Feyn 3.0
  • What is a QLattice?
  • Community edition
  • Commercial use

Essentials

  • Auto Run
  • Visualise a model
  • Summary plot
  • Semantic types
  • Categorical features
  • Estimating priors
  • Model parameters
  • Predicting with a model
  • Saving and loading models
  • Filtering models
  • Seeding a QLattice
  • Privacy

Evaluate Regressors

  • Regression plot
  • Residuals plot

Evaluate Classifiers

  • ROC curve
  • Confusion matrix
  • Plot probability scores

Understand Your Models

  • Plot response
  • Plot response 2D
  • Model signal
  • Segmented loss
  • Interactive flow

Primitive Operations

  • Using the primitives
  • Updating priors
  • Sample models
  • Fitting models
  • Pruning models
  • Diverse models
  • Updating a QLattice
  • Validate data

Advanced

  • Converting a model to SymPy
  • Setting themes
  • Saving a graph as an image
  • Using the query language
  • Model complexity

Seeding a QLattice

by: Chris Cave
(Feyn version 3.0 or newer)

The QLattice is a method of sampling Models from a probability distribution so by it's nature the QLattice is a stochastic method. Between instances of QLattices, it is not guaranteed that they will give the same results.

The results are likely to be similar but not identical.

If want to be able to reproduce your results (for example, you are sharing them to a colleague or in a publication) then you can seed the QLattice by:

import feyn

ql = feyn.QLattice(random_seed=42)

This will ensure that you will receive exactly the same Models between identical training runs.

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