Periodic signal and sinusoidal timeseries

echotorch/datasets/PeriodicSignalDataset.py

@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+#
+
+# Imports
+import torch
+from torch.utils.data.dataset import Dataset
+import numpy as np
+
+
+# Periodic signal timeseries
+class PeriodicSignalDataset(Dataset):
+    """
+    Create simple periodic signal timeseries
+    """
+
+    # Constructor
+    def __init__(self, sample_len, period, n_samples, start=0):
+        """
+        Constructor
+        :param sample_len: Sample's length
+        :param period:
+        """
+        # Properties
+        self.sample_len = sample_len
+        self.n_samples = n_samples
+        self.period = period
+        self.start = start
+
+        # Period length
+        if type(period) is list:
+            self.period_length = len(period)
+        elif type(period) is np.array or type(period) is torch.FloatTensor:
+            self.period_length = period.shape[0]
+        # end if
+
+        # 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
+            period_tensor = torch.FloatTensor(self.period)
+            sample = period_tensor.repeat(int(self.sample_len // self.period_length) + 1)
+
+            # Start
+            if type(self.start) is list:
+                start = self.start[i]
+            else:
+                start = self.start
+            # end if
+
+            # Append
+            samples.append(sample[start:start+self.sample_len].unsqueeze(-1))
+        # end for
+
+        return samples
+    # end _generate
+
+# end PeriodicSignalDataset

echotorch/datasets/SinusoidalTimeseries.py

@@ -18,7 +18,7 @@ class SinusoidalTimeseries(Dataset):
     """
 
     # Constructor
-    def __init__(self, sample_len, n_samples, w, a=1, seed=None):
+    def __init__(self, sample_len, n_samples, w, a=1, g=None, seed=None):
         """
         Constructor
         :param sample_len: Length of the time-series in time steps.
@@ -32,6 +32,7 @@ def __init__(self, sample_len, n_samples, w, a=1, seed=None):
         self.n_samples = n_samples
         self.w = w
         self.a = a
+        self.g = g
 
         # Seed
         if seed is not None:
@@ -107,12 +108,19 @@ def _generate(self):
             # Tensor
             sample = torch.zeros(self.sample_len, 1)
 
-            # Init
-            init_g = self.random_initial_points()
-
-            for t in range(0, self.sample_len):
-                sample[t, 0] = self.a * math.sin(self.w * t + init_g)
-            # end for
+            # Random start
+            if self.g is None:
+                # Init
+                init_g = self.random_initial_points()
+
+                for t in range(0, self.sample_len):
+                    sample[t, 0] = self.a * math.sin(self.w * t + init_g)
+                # end for
+            else:
+                for t in range(0, self.sample_len):
+                    sample[t, 0] = self.a * math.sin(self.w * t + self.g)
+                # end for
+            # end if
 
             # Append
             samples.append(sample)

echotorch/datasets/__init__.py

@@ -11,8 +11,9 @@
 from .NARMADataset import NARMADataset
 from .RosslerAttractor import RosslerAttractor
 from .SinusoidalTimeseries import SinusoidalTimeseries
+from .PeriodicSignalDataset import PeriodicSignalDataset
 
 __all__ = [
    'DatasetComposer', 'HenonAttractor', 'LogisticMapDataset', 'LorenzAttractor', 'MackeyGlassDataset',
-   'MemTestDataset', 'NARMADataset', 'RosslerAttractor', 'SinusoidalTimeseries'
+   'MemTestDataset', 'NARMADataset', 'RosslerAttractor', 'SinusoidalTimeseries', 'PeriodicSignalDataset'
 ]