Thanks Fred and all. Appreciate the help.
On Fri Jan 10 2014 at 1:26:13 PM, Fred Mailhot <fred.mail...@gmail.com>
wrote:
There are a few implementations of DTW in Cython floating around...I think
mblondel has one. Maybe you could tweak one of these and see whether it
yields a useful speed-up?
https://github.com/SnippyHolloW/DTW_Cython
http://www.mblondel.org/journal/2009/08/31/dynamic-time-warping-theory/
https://github.com/mdeklerk/DTW/blob/master/_dtw.pyx
On 10 January 2014 13:05, Mark Regan <m...@thinkvein.com> wrote:
Am I correct to assume the only algorithm that will work with a custom
distance metric is "brute"? DWT with 1NN is performing pretty slow with
just 10,000 observations.
New to Python, perhaps I could write the distance metric function more
efficiently?
# Define function to compute dynamic time warp distance between
# two arrays containing multiple time series arrays (each array
# represents a different data attribute. eg Gmail 30DA)
def dtw_2d(u, v, num_ts=80):
"""
Function to compute Dynamic Time Warp Distance
between two arrays of shape m x n.
m: number of time series attributes
n: number of observations in each time series attribute
num_ts: Number of elements in each time series attribute
Due to sklearn constraints, the input array into KNearestNeighbour
is converted from a 3D arrary to a 2D array. This function
converts it back to a 3D array before computing DTW distances
"""
# Reshape u and v into 3D arrays
u_dim = np.shape(u)
v_dim = np.shape(v)
# Calculate num dimensions to add to u & v
u_obs = u_dim[0]/num_ts
v_obs = v_dim[0]/num_ts
# Reshape u & v
new_u = u.reshape(u_obs, num_ts)
new_v = v.reshape(v_obs, num_ts)
# Compute DTW distances between u & v
dtw_distance = []
for a, b in zip(new_u, new_v):
dtw_distance.append(mlpy.dtw.dtw_std(a, b))
# Return the average of all distances
# ToDo: Improve this aggregation metric
return np.average(dtw_distance)
On Fri Jan 10 2014 at 9:42:01 AM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
Fully agreed with Lars.
On Fri, Jan 10, 2014 at 02:44:40PM +0100, Lars Buitinck wrote:
> 2014/1/10 Robert Layton <robertlay...@gmail.com>:
> > I wonder if that check could be removed -- as long as the input is
> > fancy-indexable, the code should otherwise not have an issue (until it
hits
> > the distance metric, in which case you have that covered).
> -1. Since high-d data is usually a mistake and NumPy offers easy
> reshaping for the advanced use cases, I think we should leave the code
> as is. It fits the existing convention that an array has shape
> (n_samples, n_features) and raises a very clear exception. Passing
> higher-d data on would raise an exception deep down in the k-NN code,
> making debugging of easy mistakes harder.
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Laboratoire de Neuro-Imagerie Assistee par Ordinateur
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