Added repeated linear and quadratic

EIVPackage/EIVData/repeated_linear.py

+"""
+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)

EIVPackage/EIVData/repeated_quadratic.py

+"""
+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)

EIVPackage/EIVData/repeated_sampling.py

+"""
+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