def test_owa(): def almost(a, b, prec=0.00001): return np.abs(a - b) < prec X = np.array([1.0, 1.0, 1.0]) w = [0.5, 0.3, 0.2] owa = fl.owa(w) r = owa(X) assert 1.0 == r owa = fl.owa(0.5, 0.3, 0.2) r2 = owa(X) assert r == r2 with pytest.raises(ValueError) as v: owa = fl.owa(0.5, 0.3, 0.2) owa(np.array([0, 1, 0.5, 0.2, 0.3])) assert "len(X) != len(v)" in str(v.value) with pytest.raises(ValueError) as v: owa = fl.owa(0.5, 0.3, 0.2, 0.4) owa(np.array([0, 1, 0.5])) assert "len(X) != len(v)" in str(v.value)
def test_owa(): def almost(a, b, prec=0.00001): return np.abs(a - b) < prec X = np.array([1.0, 1.0, 1.0]) w = [0.5, 0.3, 0.2] owa = fl.owa(w) r = owa(X) assert 1.0 == r owa = fl.owa(0.5, 0.3, 0.2) r2 = owa(X) assert r == r2 with pytest.raises(ValueError) as v: owa = fl.owa(0.5, 0.3, 0.2) owa(np.array([0, 1, 0.5, 0.2, 0.3])) assert "len(X) != len(v)" in str(v.value) with pytest.raises(ValueError) as v: owa = fl.owa(0.5, 0.3, 0.2, 0.4) owa(np.array([0, 1, 0.5])) assert "len(X) != len(v)" in str(v.value)
def test_owa_matrix(): X = np.array([[1.0, 1.0, 1.0], [0.5, 0.5, 0.5]]) w = [0.5, 0.3, 0.2] owa = fl.owa(w) r = owa(X) assert 1.0 == r[0] assert 0.5 == r[1]
def test_owa_matrix(): X = np.array([[1.0, 1.0, 1.0], [0.5, 0.5, 0.5]]) w = [0.5, 0.3, 0.2] owa = fl.owa(w) r = owa(X) assert 1.0 == r[0] assert 0.5 == r[1]
def build_owa_operator(andness, m): beta = andness / (1.0 - andness) v = np.array(range(m)) + 1.0 w = ((v / m)**beta) - (((v - 1.0) / m)**beta) return fl.owa(w)
Trans. Fuzzy Syst., vol. 19, no. 2, pp. 241-252, Apr. 2011. [3] R. Senge, and E. Hüllermeier, "Pattern trees for regression and fuzzy systems modeling," in Proc. IEEE Int. Conf. on Fuzzy Syst., 2010. """ import numpy as np import heapq from sklearn.metrics import mean_squared_error from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.utils.validation import check_array import fylearn.fuzzylogic as fl # aggregation operators to use OPERATORS = (fl.min, fl.einstein_i, fl.lukasiewicz_i, fl.prod, fl.owa([0.2, 0.8]), fl.owa([0.4, 0.6]), fl.mean, fl.owa([0.6, 0.4]), fl.owa([0.8, 0.2]), fl.algebraic_sum, fl.lukasiewicz_u, fl.einstein_u, fl.max) def _tree_iterator(root): Q = [root] while Q: tree = Q.pop(0) if isinstance(tree, Inner): Q.extend(tree.branches_) yield tree def _tree_leaves(root):
def build_owa_operator(andness, m): beta = andness / (1.0 - andness) v = np.array(range(m)) + 1.0 w = ((v / m) ** beta) - (((v - 1.0) / m) ** beta) return fl.owa(w)
""" import numpy as np import heapq from sklearn.metrics import mean_squared_error from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.utils.validation import check_array import fylearn.fuzzylogic as fl # aggregation operators to use OPERATORS = ( fl.min, fl.einstein_i, fl.lukasiewicz_i, fl.prod, fl.owa([0.2, 0.8]), fl.owa([0.4, 0.6]), fl.mean, fl.owa([0.6, 0.4]), fl.owa([0.8, 0.2]), fl.algebraic_sum, fl.lukasiewicz_u, fl.einstein_u, fl.max ) def _tree_iterator(root): Q = [ root ] while Q: tree = Q.pop(0) if isinstance(tree, Inner):