def measure(individual):
func = numpy_phenotype(individual)
data = load_boston()
yhat = func(*data.data.T)
if np.isscalar(yhat):
yhat = np.ones_like(data.target) * yhat
fit = nrmse(data.target, yhat), len(individual)
return replace_nan(fit)
def measure(ind):
g = lambda x: x**2 - 1.1
points = np.linspace(-1, 1, 100, endpoint=True)
y = g(points)
f = gp.individual.numpy_phenotype(ind)
yhat = f(points)
if np.isscalar(yhat):
yhat = np.ones_like(y) * yhat
return nrmse(y, yhat), len(gp.individual.resolve_sc(ind))
def error(ind, *args):
g = lambda x: x**2 - 1.1
points = np.linspace(-1, 1, 100, endpoint=True)
y = g(points)
f = gp.individual.numpy_phenotype(ind)
yhat = f(points, *args)
if np.isscalar(yhat):
yhat = np.ones_like(y) * yhat
return nrmse(y, yhat)
def error(ind, *args):
g = lambda x: x**2 - 1.1
points = np.linspace(-1, 1, 100, endpoint=True).reshape(ind.arity, -1)
y = g(points)
f = phenotype(ind)
args[0].dot(points) + args[
1] # ??? without this there will be segmentation fault
yhat = f(points, *args).reshape(y.shape)
return nrmse(y, yhat)
