def test_lwr_linear():
"""Test LWLR on random linear models of dimensions from 1 to 20.
It should return exact results, give of take floating point imprecisions."""
result = True
for i in range(20):
n = random.randint(1, 20)
m = random.randint(1, 5)
f = random_linear(n, m)
model = LWLRForwardModel(n, m, 1.0)
for i in range(2*n):
x = np.random.rand(n)
y = f(x)
model.add_xy(x, y)
for i in range(10):
x = np.random.rand(n).ravel()
y = f(x)
yp = model.predict_y(x)
#print 'y ', y
#print 'yp', yp
if not np.allclose(y, yp, rtol = 1e-10, atol = 1e-10):
print(n, m)
result = result and np.allclose(y, yp, rtol = 1e-10, atol = 1e-10)
return result
def test_lwr1D_linear():
"""Simplest test possible (well, not quite, but close)."""
result = True
f = lambda x : 2.0*x
model = LWLRForwardModel(1, 1, k = 3, sigma = 0.1)
for i in range(10):
x = np.random.rand(1)
y = f(x)
#print x, y
model.add_xy(x, y)
for i in range(10):
x = np.random.rand(1).ravel()
y = f(x)
yp = model.predict_y(x)
#print 'y ', y
#print 'yp', yp
result = result and np.allclose(y, yp, rtol = 1e-5, atol = 1e-5)
return result