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']]) -
