def test_invwnn_kpoints_group():
"""Test InvWNN with group of k same points provided : all request should return
point of the dataset."""
result = True
for i in range(25):
n = random.randint(1, 20)
m = random.randint(1, 20)
k = random.randint(1, 20)
sigma = random.uniform(0.1, 10)
model = WeightedNNInverseModel(n, m, sigma, k)
xgroup = set()
for i in range(random.randint(1, 20)):
x = np.random.rand(n)
y = np.random.rand(m)
xgroup.add(tuple(x))
for i in range(k):
model.add_xy(x, y)
for i in range(10):
y = np.random.rand(m)
xp = model.infer_x(y)[0]
check = min([tools.dist(xp, x) for x in xgroup]) < 1e-10
if not check:
print('Error:', n, m, y, xe, xp)
result = result and check
return result
def test_invwnn_kpoints_group():
"""Test InvWNN with group of k same points provided : all request should return
point of the dataset."""
result = True
for i in range(25):
n = random.randint(1, 20)
m = random.randint(1, 20)
k = random.randint(1, 20)
sigma = random.uniform(0.1, 10)
model = WeightedNNInverseModel(n, m, sigma, k)
xgroup = set()
for i in range(random.randint(1, 20)):
x = np.random.rand(n)
y = np.random.rand(m)
xgroup.add(tuple(x))
for i in range(k):
model.add_xy(x, y)
for i in range(10):
y = np.random.rand(m)
xp = model.infer_x(y)[0]
check = min([tools.dist(xp, x) for x in xgroup]) < 1e-10
if not check:
print('Error:', n, m, y, xe, xp)
result = result and check
return result
def test_invwnn_samepoint():
"""Test InvWNN with one point provided multiple times."""
result = True
for i in range(25):
n = random.randint(1, 20)
m = random.randint(1, 20)
k = random.randint(1, 20)
sigma = random.uniform(0.1, 10)
model = WeightedNNInverseModel(n, m, sigma, k)
x = np.random.rand(n)
y = np.random.rand(m)
for i in range(random.randint(1, 20)):
model.add_xy(x, y)
for i in range(10):
y = np.random.rand(m)
xe = x
xp = model.infer_x(y)[0]
check = np.allclose(xe, xp, rtol=1e-10, atol=1e-10)
if not check:
print('Error:', n, m, y, xe, xp)
result = result and check
return result
def test_invwnn_samepoint():
"""Test InvWNN with one point provided multiple times."""
result = True
for i in range(25):
n = random.randint(1, 20)
m = random.randint(1, 20)
k = random.randint(1, 20)
sigma = random.uniform(0.1, 10)
model = WeightedNNInverseModel(n, m, sigma, k)
x = np.random.rand(n)
y = np.random.rand(m)
for i in range(random.randint(1, 20)):
model.add_xy(x, y)
for i in range(10):
y = np.random.rand(m)
xe = x
xp = model.infer_x(y)[0]
check = np.allclose(xe, xp, rtol = 1e-10, atol = 1e-10)
if not check:
print('Error:', n, m, y, xe, xp)
result = result and check
return result
