timedelay added on fork

1 files changed, 56 insertions, 0 deletions

diff --git a/src/python/gudhi/point_cloud/timedelay.py b/src/python/gudhi/point_cloud/timedelay.py
new file mode 100644
index 00000000..5c7ba542
--- /dev/null
+++ b/src/python/gudhi/point_cloud/timedelay.py
@@ -0,0 +1,56 @@
+import numpy as np
+
+class TimeDelayEmbedding:
+    """Point cloud transformation class.
+
+    Embeds time-series data in the R^d according to Takens' Embedding Theorem
+    and obtains the coordinates of each point.
+
+    Parameters
+    ----------
+    dim : int, optional (default=3)
+        `d` of R^d to be embedded.
+
+    delay : int, optional (default=1)
+        Time-Delay embedding.
+
+    skip : int, optional (default=1)
+        How often to skip embedded points.
+
+    """
+    def __init__(self, dim=3, delay=1, skip=1):
+        self._dim = dim
+        self._delay = delay
+        self._skip = skip
+
+    def __call__(self, *args, **kwargs):
+        return self.transform(*args, **kwargs)
+
+    def _transform(self, ts):
+        """Guts of transform method."""
+        return ts[
+            np.add.outer(
+                np.arange(0, len(ts)-self._delay*(self._dim-1), self._skip),
+                np.arange(0, self._dim*self._delay, self._delay))
+        ]
+
+    def transform(self, ts):
+        """Transform method.
+
+        Parameters
+        ----------
+        ts : list[float] or list[list[float]]
+            A single or multiple time-series data.
+
+        Returns
+        -------
+        point clouds : list[list[float, float, float]] or list[list[list[float, float, float]]]
+            Makes point cloud every a single time-series data.
+        """
+        ndts = np.array(ts)
+        if ndts.ndim == 1:
+            # for single.
+            return self._transform(ndts).tolist()
+        else:
+            # for multiple.
+            return np.apply_along_axis(self._transform, 1, ndts).tolist()