Ich habe Probleme, eine mehrdimensionale Funktion zu vektorisieren.
Betrachten Sie das folgende Beispiel:numpy vectorize mehrdimensionale Funktion
def _cost(u):
return u[0] - u[1]
cost = np.vectorize(_cost)
>>> x = np.random.normal(0, 1,(10, 2))
>>> cost(x)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/lucapuggini/MyApps/scientific_python_3_5/lib/python3.5/site-packages/numpy/lib/function_base.py", line 2218, in __call__
return self._vectorize_call(func=func, args=vargs)
File "/Users/lucapuggini/MyApps/scientific_python_3_5/lib/python3.5/site-packages/numpy/lib/function_base.py", line 2281, in _vectorize_call
ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
File "/Users/lucapuggini/MyApps/scientific_python_3_5/lib/python3.5/site-packages/numpy/lib/function_base.py", line 2243, in _get_ufunc_and_otypes
outputs = func(*inputs)
TypeError: _cost() missing 1 required positional argument: 'v'
Hintergrundinformationen: ich das aufgetretene Problem beim Versuch, den folgenden Code (Particle Swarm Optimization Algorithm) auf multivariate Daten zu verallgemeinern:
import numpy as np
import matplotlib.pyplot as plt
def pso(cost, sim, space_dimension, n_particles, left_lim, right_lim, f1=1, f2=1, verbose=False):
best_scores = np.array([np.inf]*n_particles)
best_positions = np.zeros(shape=(n_particles, space_dimension))
particles = np.random.uniform(left_lim, right_lim, (n_particles, space_dimension))
velocities = np.zeros(shape=(n_particles, space_dimension))
for i in range(sim):
particles = particles + velocities
print(particles)
scores = cost(particles).ravel()
better_positions = np.argwhere(scores < best_scores).ravel()
best_scores[better_positions] = scores[better_positions]
best_positions[better_positions, :] = particles[better_positions, :]
g = best_positions[np.argmin(best_scores), :]
u1 = np.random.uniform(0, f1, (n_particles, 1))
u2 = np.random.uniform(0, f2, (n_particles, 1))
velocities = velocities + u1 * (best_positions - particles) + u2 * (g - particles)
if verbose and i % 50 == 0:
print('it=', i, ' score=', cost(g))
x = np.linspace(-5, 20, 1000)
y = cost(x)
plt.plot(x, y)
plt.plot(particles, cost(particles), 'o')
plt.vlines(g, y.min()-2, y.max())
plt.show()
return g, cost(g)
def test_pso_1_dim():
def _cost(x):
if 0 < x < 15:
return np.sin(x)*x
else:
return 15 + np.min([np.abs(x-0), np.abs(x-15)])
cost = np.vectorize(_cost)
sim = 100
space_dimension = 1
n_particles = 5
left_lim, right_lim = 0, 15
f1, f2 = 1, 1
x, cost_x = pso(cost, sim, space_dimension, n_particles,
left_lim, right_lim, f1, f2, verbose=False)
x0 = 11.0841839
assert np.abs(x - x0) < 0.01
return
Bitte geben Sie mir, wenn Vektorisierung ist in diesem Fall keine gute Idee.