Create a dataset from a lambda function

echotorch/datasets/LambdaDataset.py

@@ -0,0 +1,87 @@
+# -*- coding: utf-8 -*-
+#
+
+# Imports
+import torch
+from torch.utils.data.dataset import Dataset
+import numpy as np
+
+
+# Lambda dataset
+class LambdaDataset(Dataset):
+    """
+    Create simple periodic signal timeseries
+    """
+
+    # Constructor
+    def __init__(self, sample_len, n_samples, func, start=0, dtype=torch.float32):
+        """
+        Constructor
+        :param sample_len: Sample's length
+        :param period:
+        """
+        # Properties
+        self.sample_len = sample_len
+        self.n_samples = n_samples
+        self.func = func
+        self.start = start
+        self.dtype = dtype
+
+        # Generate data set
+        self.outputs = self._generate()
+    # end __init__
+
+    #############################################
+    # OVERRIDE
+    #############################################
+
+    # Length
+    def __len__(self):
+        """
+        Length
+        :return:
+        """
+        return self.n_samples
+    # end __len__
+
+    # Get item
+    def __getitem__(self, idx):
+        """
+        Get item
+        :param idx:
+        :return:
+        """
+        return self.outputs[idx]
+    # end __getitem__
+
+    ##############################################
+    # PRIVATE
+    ##############################################
+
+    # Generate
+    def _generate(self):
+        """
+        Generate dataset
+        :return:
+        """
+        # List of samples
+        samples = list()
+
+        # For each sample
+        for i in range(self.n_samples):
+            # Tensor
+            sample = torch.zeros(self.sample_len, 1, dtype=self.dtype)
+
+            # Timestep
+            for t in range(self.sample_len):
+                sample[t, 0] = self.func(t + self.start)
+            # end for
+
+            # Append
+            samples.append(sample)
+        # end for
+
+        return samples
+    # end _generate
+
+# end LambdaDataset