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'))