def compare_dependence_heatmap():
e1 = Engine.from_pickle('resources/animals/animals.engine')
e2 = Engine.from_pickle('resources/animals/animals-lovecat.engine')
D1 = e1.dependence_probability_pairwise()
D2 = e2.dependence_probability_pairwise()
C1 = pu.plot_clustermap(D1)
ordering = C1.dendrogram_row.reordered_ind
fig, ax = plt.subplots(nrows=1, ncols=2)
pu.plot_heatmap(D1, xordering=ordering, yordering=ordering, ax=ax[0])
pu.plot_heatmap(D2, xordering=ordering, yordering=ordering, ax=ax[1])
def render_states_to_disk(filepath, prefix):
engine = Engine.from_pickle(filepath)
for i in range(engine.num_states()):
print '\r%d' % (i, )
savefile = '%s-%d' % (prefix, i)
state = engine.get_state(i)
ru.viz_state(state,
row_names=animal_names,
col_names=animal_features,
savefile=savefile)
def launch_analysis():
engine = Engine(animals.values.astype(float),
num_states=64,
cctypes=['categorical'] * len(animals.values[0]),
distargs=[{
'k': 2
}] * len(animals.values[0]),
rng=gu.gen_rng(7))
engine.transition(N=900)
with open('resources/animals/animals.engine', 'w') as f:
engine.to_pickle(f)
engine = Engine.from_pickle(open('resources/animals/animals.engine', 'r'))
D = engine.dependence_probability_pairwise()
pu.plot_clustermap(D)
def load_engine(dist, noise, timestamp):
print 'Loading %s %f' % (dist, noise)
return Engine.from_pickle(file(filename_engine(dist, noise, timestamp),'r'))