1 files changed, 76 insertions, 0 deletions
diff --git a/pyspike/distances.py b/pyspike/distances.py
index f4be625..52c6640 100644
--- a/pyspike/distances.py
+++ b/pyspike/distances.py
@@ -224,3 +224,79 @@ def spike_distance(spikes1, spikes2):
     # could be less than original length due to equal spike times
     return PieceWiseLinFunc(spike_events[:index+1], 
                             y_starts[:index], y_ends[:index])
+
+
+
+
+############################################################
+# multi_distance
+############################################################
+def multi_distance(spike_trains, pair_distance_func, indices=None):
+    """ Internal implementation detail, use isi_distance_multi or 
+    spike_distance_multi.
+
+    Computes the multi-variate distance for a set of spike-trains using the
+    pair_dist_func to compute pair-wise distances. That is it computes the 
+    average distance of all pairs of spike-trains:
+    S(t) = 2/((N(N-1)) sum_{<i,j>} S_{i,j}, 
+    where the sum goes over all pairs <i,j>.
+    Args:
+    - spike_trains: list of spike trains
+    - pair_distance_func: function computing the distance of two spike trains
+    - indices: list of indices defining which spike trains to use, 
+    if None all given spike trains are used (default=None)
+    Returns:
+    - The averaged multi-variate distance of all pairs
+    """
+    if indices==None:
+        indices = np.arange(len(spike_trains))
+    indices = np.array(indices)
+    # check validity of indices
+    assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \
+            "Invalid index list."
+    # generate a list of possible index pairs
+    pairs = [(i,j) for i in indices for j in indices[i+1:]]
+    # start with first pair
+    (i,j) = pairs[0]
+    average_dist = pair_distance_func(spike_trains[i], spike_trains[j])
+    for (i,j) in pairs[1:]:
+        current_dist = pair_distance_func(spike_trains[i], spike_trains[j])
+        average_dist.add(current_dist)      # add to the average
+    average_dist.mul_scalar(1.0/len(pairs)) # normalize
+    return average_dist
+
+
+############################################################
+# isi_distance_multi
+############################################################
+def isi_distance_multi(spike_trains, indices=None):
+    """ computes the multi-variate isi-distance for a set of spike-trains. That
+    is the average isi-distance of all pairs of spike-trains:
+    S(t) = 2/((N(N-1)) sum_{<i,j>} S_{i,j}, 
+    where the sum goes over all pairs <i,j>
+    Args:
+    - spike_trains: list of spike trains
+    - indices: list of indices defining which spike trains to use, 
+    if None all given spike trains are used (default=None)
+    Returns:
+    - A PieceWiseConstFunc representing the averaged isi distance S
+    """
+    return multi_distance(spike_trains, isi_distance, indices)
+
+
+############################################################
+# spike_distance_multi
+############################################################
+def spike_distance_multi(spike_trains, indices=None):
+    """ computes the multi-variate spike-distance for a set of spike-trains. 
+    That is the average spike-distance of all pairs of spike-trains:
+    S(t) = 2/((N(N-1)) sum_{<i,j>} S_{i,j}, 
+    where the sum goes over all pairs <i,j>
+    Args:
+    - spike_trains: list of spike trains
+    - indices: list of indices defining which spike trains to use, 
+    if None all given spike trains are used (default=None)
+    Returns:
+    - A PieceWiseLinFunc representing the averaged spike distance S
+    """
+    return multi_distance(spike_trains, spike_distance, indices)