Sinusoidal timeseries dataset

echotorch/datasets/SinusoidalTimeseries.py

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+# -*- coding: utf-8 -*-
+#
+
+# Imports
+import torch
+from torch.utils.data.dataset import Dataset
+import math
+from random import shuffle
+import numpy as np
+
+
+# Sinusoidal Timeseries
+class SinusoidalTimeseries(Dataset):
+    """
+    The Rössler attractor is the attractor for the Rössler system, a system of three non-linear ordinary differential
+    equations originally studied by Otto Rössler. These differential equations define a continuous-time dynamical
+    system that exhibits chaotic dynamics associated with the fractal properties of the attractor.
+    """
+
+    # Constructor
+    def __init__(self, sample_len, n_samples, w, a=1, seed=None):
+        """
+        Constructor
+        :param sample_len: Length of the time-series in time steps.
+        :param n_samples: Number of samples to generate.
+        :param a:
+        :param b:
+        :param c:
+        """
+        # Properties
+        self.sample_len = sample_len
+        self.n_samples = n_samples
+        self.w = w
+        self.a = a
+
+        # Seed
+        if seed is not None:
+            np.random.seed(seed)
+        # 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__
+
+    ##############################################
+    # PUBLIC
+    ##############################################
+
+    # Regenerate
+    def regenerate(self):
+        """
+        Regenerate
+        :return:
+        """
+        # Generate data set
+        self.outputs = self._generate()
+    # end regenerate
+
+    ##############################################
+    # PRIVATE
+    ##############################################
+
+    # Random initial points
+    def random_initial_points(self):
+        """
+        Random initial points
+        :return:
+        """
+        # Set
+        return np.random.random() * (math.pi * 2.0)
+    # end random_initial_points
+
+    # 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)
+
+            # 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
+
+            # Append
+            samples.append(sample)
+        # end for
+
+        return samples
+    # end _generate
+
+# end SinusoidalTimeseries