import matplotlib.pyplot as plt import numpy as np from concept_formation.examples.examples_utils import lowess from concept_formation.predict import incremental_prediction from concept_formation.cobweb3 import Cobweb3Tree from concept_formation.dummy import DummyTree from concept_formation.datasets import load_iris num_runs = 30 num_examples = 30 irises = load_iris() naive_data = incremental_prediction(DummyTree(), irises, run_length=num_examples, runs=num_runs, attr="class") cobweb_data = incremental_prediction(Cobweb3Tree(), irises, run_length=num_examples, runs=num_runs, attr="class") naive_data.sort() cobweb_data.sort() cobweb_x, cobweb_y = [], [] naive_x, naive_y = [], [] for x,y in cobweb_data: cobweb_x.append(x) cobweb_y.append(y) for x,y in naive_data:
from __future__ import division import matplotlib.pyplot as plt import numpy as np from concept_formation.examples.examples_utils import lowess from concept_formation.predict import incremental_prediction from concept_formation.cobweb import CobwebTree from concept_formation.dummy import DummyTree from concept_formation.datasets import load_mushroom num_runs = 30 num_examples = 30 mushrooms = load_mushroom() naive_data = incremental_prediction(DummyTree(), mushrooms, run_length=num_examples, runs=num_runs, attr="classification") cobweb_data = incremental_prediction(CobwebTree(), mushrooms, run_length=num_examples, runs=num_runs, attr="classification") naive_data.sort() cobweb_data.sort() cobweb_x, cobweb_y = [], [] naive_x, naive_y = [], [] for x,y in cobweb_data: cobweb_x.append(x) cobweb_y.append(y) for x,y in naive_data:
from concept_formation.predict import incremental_prediction from concept_formation.trestle import TrestleTree from concept_formation.dummy import DummyTree from concept_formation.datasets import load_rb_s_07 from concept_formation.datasets import load_rb_s_07_human_predictions from concept_formation.structure_mapper import ObjectVariablizer num_runs = 30 num_examples = 30 towers = load_rb_s_07() variablizer = ObjectVariablizer() towers = [variablizer.transform(t) for t in towers] naive_data = incremental_prediction(DummyTree(), towers, run_length=num_examples, runs=num_runs, attr="success") cobweb_data = incremental_prediction(TrestleTree(), towers, run_length=num_examples, runs=num_runs, attr="success") human_data = [] key = None human_predictions = load_rb_s_07_human_predictions() for line in human_predictions: line = line.rstrip().split(",") if key is None: key = {v:i for i,v in enumerate(line)} continue x = int(line[key['order']])-1 y = (1 - abs(int(line[key['correctness']]) -