In [80]:

    

import numpy as np
import cPickle as pickle
from pydtw import dtw2d
from joblib import Parallel, delayed


    

In [81]:

    

def pickle_this(data, filename='distance_matrix.p'):
    pickle.dump(data, open(filename, 'wb'))


    

In [82]:

    

def inner_loop(sample, i, n):
    row = np.zeros((n,))
    for j in xrange(i, n):
        row[j] = dtw2d(sample[i], sample[j])[-1,-1]
    return row


    

In [83]:

    

sample = pickle.load( open( 'sample_pitches.p', 'rb' ) )


    

In [99]:

    

n = 4
idxs = np.random.choice(len(sample), n)
subset = sample[idxs]


    

In [95]:

    

distance_matrix = np.asmatrix(Parallel(n_jobs=6)(delayed(inner_loop)(subset, i, n) for i in range(n)))
print distance_matrix


    

    




[[    0.          1766.58088787  1668.80347053  1720.59559259]
 [    0.             0.          1429.22276921   991.13049528]
 [    0.             0.             0.          1393.70271966]
 [    0.             0.             0.             0.        ]]