From e236fcd2fb15ee09100fa939fe261dc07a61ab2f Mon Sep 17 00:00:00 2001
From: Balthazar Casale <balthazar.casale@lis-lab.fr>
Date: Fri, 9 Jun 2023 18:10:56 +0200
Subject: [PATCH] Update 2 files
- /src/models/approx_based.py
- /README.md
---
README.md | 8 +++++---
src/models/approx_based.py | 10 ++++++++++
2 files changed, 15 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index 54de1ab..d5fb6ec 100644
--- a/README.md
+++ b/README.md
@@ -24,12 +24,13 @@ We give a typical use case in the following snipped of code :
```python
from types import save_dmstack, load_dmstack
from pipeline import *
+from samplers.mixed import RandomInduced
from models.criteria import PPT
from models.approx_based import DistToSep
from transformers.sep_approximation import FrankWolfe
states, infos = Pipeline([
- ('sample', InducedMeasure(k_params=[25]).states), # induced measure of parameter 25
+ ('sample', RandomInduced(k_params=[25]).states), # induced measure of parameter 25
('ppt only', select(PPT.is_respected, True)), # respecting the PPT criterion
('fw', add(FrankWolfe(1000).approximation, key = 'approx'), # compute the sep approx.
('sel ent', select(DistToSep(0.01, sep_key = 'fw__approx').predict, Label.ENT))
@@ -55,6 +56,7 @@ def sampler(n_states : int, dims : list[int]) -> DMStack, dict
```
the following samplers can be found in the library :
+- samplers.utils.FromSet
- samplers.pure.RandomHaar
- samplers.mixed.RandomInduced
- samplers.mixed.RandomBures
@@ -71,8 +73,8 @@ def transformer(states : DMStack, infos : dict) -> DMStack, dict
the following transformers can be found in the library :
- transformers.sep_approximations.FrankWolfe
-- transformers.real_representation.GellMann
-- transformer.real_representation.Measures
+- transformers.representations.GellMann
+- transformer.representations.Measures
### model
diff --git a/src/models/approx_based.py b/src/models/approx_based.py
index 8e98738..e82a4ab 100644
--- a/src/models/approx_based.py
+++ b/src/models/approx_based.py
@@ -7,6 +7,16 @@ from ..types import Label
import numpy as np
+class MlModel :
+ """
+ Use a machine learning model (sklearn) as model
+ """
+ def __init__(self, model) :
+ self.model = model
+
+ def predict(self, states, infos={}):
+ return self.model.predict(state), {}
+
class DistToSep:
"""
Distance from a separable approximation
--
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