From 9cc9e1cf3cd9ea42908324d410ef68fa12e8e832 Mon Sep 17 00:00:00 2001
From: mtakenouchi <m.takenouchi@jp.fujitsu.com>
Date: Fri, 14 Feb 2020 11:08:50 +0900
Subject: Update timedelay.py

---
 src/python/gudhi/point_cloud/timedelay.py | 66 ++++++++++++++++++++++---------
 1 file changed, 48 insertions(+), 18 deletions(-)

(limited to 'src/python/gudhi')

diff --git a/src/python/gudhi/point_cloud/timedelay.py b/src/python/gudhi/point_cloud/timedelay.py
index f283916d..d899da67 100644
--- a/src/python/gudhi/point_cloud/timedelay.py
+++ b/src/python/gudhi/point_cloud/timedelay.py
@@ -10,30 +10,55 @@ 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.
-
+        Given delay=3 and skip=2, an point cloud which is obtained by embedding 
+        a single time-series data into R^3 is as follows.
+        
+    .. code-block:: none
+    
+    time-series = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+    point clouds = [[1, 4, 7], 
+                    [3, 6, 9]]
+                   
     """
     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 __call__(self, ts):
+        """Transform method for single time-series data.
+        Parameters
+        ----------
+        ts : list[float]
+            A single time-series data.
+        Returns
+        -------
+        point clouds : list[list[float, float, float]]
+            Makes point cloud every a single time-series data.
+        Raises
+        -------
+        TypeError
+            If the parameter's type does not match the desired type.
+        """
+        ndts = np.array(ts)
+        if ndts.ndim == 1:
+            return self._transform(ndts)
+        else:
+            raise TypeError("Expects 1-dimensional array.")
 
+    def fit(self, ts, y=None):
+        return self
+    
     def _transform(self, ts):
         """Guts of transform method."""
         return ts[
@@ -43,22 +68,27 @@ class TimeDelayEmbedding:
         ]
 
     def transform(self, ts):
-        """Transform method.
-
+        """Transform method for multiple time-series data.
         Parameters
         ----------
-        ts : list[float] or list[list[float]]
-            A single or multiple time-series data.
-
+        ts : list[list[float]]
+            Multiple time-series data.
+        Attributes
+        ----------
+        ndts : 
+             The ndts means that all time series need to have exactly 
+             the same size. 
         Returns
         -------
-        point clouds : list[list[float, float, float]] or list[list[list[float, float, float]]]
+        point clouds : list[list[list[float, float, float]]]
             Makes point cloud every a single time-series data.
+        Raises
+        -------
+        TypeError
+            If the parameter's type does not match the desired type.
         """
         ndts = np.array(ts)
-        if ndts.ndim == 1:
-            # for single.
-            return self._transform(ndts).tolist()
+        if ndts.ndim == 2:
+            return np.apply_along_axis(self._transform, 1, ndts)
         else:
-            # for multiple.
-            return np.apply_along_axis(self._transform, 1, ndts).tolist()
+            raise TypeError("Expects 2-dimensional array.")
-- 
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