#coords = scale_to_interval(coords, max=100)
print 'writing coordinates to csv'
csv_writer = csv.writer(open(output_file, 'wb'), delimiter=',')
for r in xrange(len(coords)):
csv_writer.writerow(coords[r].astype('|S12')) # format with 10dp accuracy (but no '-e' format stuff)
if __name__ == '__main__':
codes_file = 'test/test_codes.csv'
coords_file = 'out/test_coords.csv'
codes = numpy.genfromtxt(codes_file, dtype=numpy.float32, delimiter=',')
codes = codes[:,1:]
for perplexity in xrange(20):
try:
tsne = TSNE(perplexity=perplexity, desired_dims=2)
tsne.initialize_with_codes(codes)
tsne.fit(iterations=500)
tsne.save_coords_to_file(coords_file)
density_plot_file = 'out/test_map_%i.png'%perplexity
labels_file = 'test/test_labels.csv'
render = Render(coords_file, labels_file)
render.plot_density(density_plot_file)
except:
print 'failed to compute with perplexity %i'%perplexity
desired_dims = cfg.tsne.desired_dims
target_iterations = cfg.tsne.initial_fit_iterations
# FIXME2 slightly braindead way to work out epochs expected (just run through it)
total_epochs = 0
if target_perplex >= (len(codes) / 2):
total_epochs = 1
print len(codes)
perplexity = len(codes) / 2
while perplexity > target_perplex:
perplexity = perplexity / 2
total_epochs = total_epochs + 1
iterations_per = target_iterations / total_epochs
print "will run for %i total epochs at %i iterations each " % (total_epochs, iterations_per)
# initialize
tsne = TSNE(desired_dims=desired_dims)
tsne.initialize_with_codes(codes)
# fit with reducing perplexity, ending up at target complexity
perplexity = len(codes) / 2
while perplexity > target_perplex:
print "training with perplexity", perplexity
tsne.perplexity = perplexity
tsne.fit(iterations_per)
perplexity = perplexity / 2
print "done training"
tsne.save_coords_to_file(coords_file)