From 00982faed6cd87f0ba7b301914037d8440ab11ab Mon Sep 17 00:00:00 2001
From: Joerg Martin <joerg.martin@ptb.de>
Date: Thu, 6 Jan 2022 15:30:45 +0100
Subject: [PATCH] Added repeated linear and quadratic
---
EIVPackage/EIVData/repeated_linear.py | 11 +++++++++++
EIVPackage/EIVData/repeated_quadratic.py | 11 +++++++++++
EIVPackage/EIVData/repeated_sampling.py | 17 +++++++++++++++++
3 files changed, 39 insertions(+)
create mode 100644 EIVPackage/EIVData/repeated_linear.py
create mode 100644 EIVPackage/EIVData/repeated_quadratic.py
diff --git a/EIVPackage/EIVData/repeated_linear.py b/EIVPackage/EIVData/repeated_linear.py
new file mode 100644
index 0000000..6e0bd53
--- /dev/null
+++ b/EIVPackage/EIVData/repeated_linear.py
@@ -0,0 +1,11 @@
+"""
+Repeated sampling from the linear dataset.
+"""
+from EIVData import linear
+
+from EIVData.repeated_sampling import repeated_sampling
+
+fixed_seed = 0
+
+load_data = repeated_sampling(dataclass=linear,
+ fixed_seed=fixed_seed)
diff --git a/EIVPackage/EIVData/repeated_quadratic.py b/EIVPackage/EIVData/repeated_quadratic.py
new file mode 100644
index 0000000..4c40982
--- /dev/null
+++ b/EIVPackage/EIVData/repeated_quadratic.py
@@ -0,0 +1,11 @@
+"""
+Repeated sampling from the quadratic dataset.
+"""
+from EIVData import quadratic
+
+from EIVData.repeated_sampling import repeated_sampling
+
+fixed_seed = 0
+
+load_data = repeated_sampling(dataclass=quadratic,
+ fixed_seed=fixed_seed)
diff --git a/EIVPackage/EIVData/repeated_sampling.py b/EIVPackage/EIVData/repeated_sampling.py
index 771577d..465a20e 100644
--- a/EIVPackage/EIVData/repeated_sampling.py
+++ b/EIVPackage/EIVData/repeated_sampling.py
@@ -1,3 +1,7 @@
+"""
+Contains the class `repeated_sampling` that can be used to generate
+datasets for repeated sampling from datasets with a ground truth.
+"""
import sys
import torch
@@ -6,6 +10,19 @@ from torch.utils.data import TensorDataset
from EIVGeneral.manipulate_tensors import add_noise
class repeated_sampling():
+ """
+ A class for repeated sampling from datasets with a known ground truth and
+ known input and output noise. The class `dataclass` should contain a
+ `load_data` routine that returns a ground truth and two positive floats
+ `x_noise_strength` and `y_noise_strength` that will be used as the standard
+ deviation of input and output noise.
+ :param dataclass: A module that contains a routine `load_data`, which
+ accepts the keyword `return_ground_truth` and returns the noisy and true
+ train and test datasets, and two positive floats `x_noise_strength` and
+ `y_noise_strength`.
+ :param fixed_seed: Integer. The seed to load the unnoisy ground truth,
+ defaults to 0.
+ """
def __init__(self, dataclass, fixed_seed=0):
self.dataclass = dataclass
self.fixed_seed = fixed_seed
--
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