def test_apply(self):
x = np.array([12] * 100 + [20] * 100 + [15] * 100).astype(float)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
f = mfblp_filter(w=5, s=s, a=.1, b=1.)
m = f.apply(x)
self.assertTrue((np.abs(m - x) < 0.1).all())
def test_apply(self) :
x = np.array([12] * 100 + [20] * 100 + [15] * 100).astype(float)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
f = mfblp_filter(w=5, s=s, a=.1, b=1.)
m = f.apply(x)
self.assertTrue((np.abs(m - x) < 0.1).all())
def test_apply_restrict(self):
x = np.array([12] * 100 + [20] * 100 + [15] * 100).astype(float)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
f = mfblp_filter(w=5, s=s, a=.1, b=1.)
m = f.apply_restrict(x)
self.assertTrue((np.abs(m - x) > 0.1).nonzero()[0].shape[0] < 3)
def test_apply_restrict(self) :
x = np.array([12] * 100 + [20] * 100 + [15] * 100).astype(float)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
f = mfblp_filter(w=5, s=s, a=.1, b=1.)
m = f.apply_restrict(x)
self.assertTrue((np.abs(m - x) > 0.1).nonzero()[0].shape[0] < 3)
def test_search_w7(self):
x = np.array([12] * 100 + [19] * 100 + [15] * 100).astype(float)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
starts = np.ones(x.shape)
stepsize = 0.1
steps = 300
f = mfblp_filter(w=7, s=s, a=.1, b=10.)
opt_ms = f.search(x, starts, stepsize, steps)
self.assertTrue((np.abs(opt_ms - x) < 0.1).all())
def test_apply_noisy_gaussian(self):
x = np.array([12] * 100 + [20] * 100 + [15] * 100).astype(float)
s = np.array([10, 12, 15, 17, 20]).astype(float)
np.random.seed(42)
#x = x + np.random.normal(0, .1, size=x.shape)
f = mfblp_filter(w=9, s=s, a=.1, b=10.)
m = f.apply(x)
self.assertTrue((np.abs(m - x) > 0.5).nonzero()[0].shape[0] < 5)
def test_apply_noisy_uniform(self):
x = np.array([12] * 100 + [20] * 100 + [15] * 100).astype(float)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
np.random.seed(42)
x = x + np.random.uniform(low=-0.5, high=0.5)
f = mfblp_filter(w=7, s=s, a=.1, b=.1)
m = f.apply(x)
self.assertTrue((np.abs(m - x) < 0.1).all())
def test_search_w7(self) :
x = np.array([12] * 100 + [19] * 100 + [15] * 100).astype(float)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
starts = np.ones(x.shape)
stepsize = 0.1
steps = 300
f = mfblp_filter(w=7, s=s, a=.1, b=10.)
opt_ms = f.search(x, starts, stepsize, steps)
self.assertTrue((np.abs(opt_ms - x) < 0.1).all())
def test_apply_noisy_gaussian(self) :
x = np.array([12] * 100 + [20] * 100 + [15] * 100).astype(float)
s = np.array([10, 12, 15, 17, 20]).astype(float)
np.random.seed(42)
#x = x + np.random.normal(0, .1, size=x.shape)
f = mfblp_filter(w=9, s=s, a=.1, b=10.)
m = f.apply(x)
self.assertTrue((np.abs(m - x) > 0.5).nonzero()[0].shape[0] < 5)
def test_apply_noisy_uniform(self) :
x = np.array([12] * 100 + [20] * 100 + [15] * 100).astype(float)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
np.random.seed(42)
x = x + np.random.uniform(low=-0.5, high=0.5)
f = mfblp_filter(w=7, s=s, a=.1, b=.1)
m = f.apply(x)
self.assertTrue((np.abs(m - x) < 0.1).all())
def test_search_noisy_gaussian(self):
x = np.array([12] * 100 + [19] * 100 + [15] * 100).astype(float)
np.random.seed(42)
x = x + np.random.normal(0, .5, size=x.shape)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
starts = np.ones(x.shape)
stepsize = 0.1
steps = 300
f = mfblp_filter(w=15, s=s, a=.5, b=.5)
opt_ms = f.search(x, starts, stepsize, steps)
self.assertTrue((np.abs(opt_ms - x) < 3.).all())
def test_search_noisy_uniform(self):
x = np.array([12] * 100 + [19] * 100 + [15] * 100).astype(float)
np.random.seed(42)
x = x + np.random.uniform(low=-0.5, high=0.5)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
starts = np.ones(x.shape)
stepsize = 0.1
steps = 300
f = mfblp_filter(w=11, s=s, a=.1, b=10.)
opt_ms = f.search(x, starts, stepsize, steps)
self.assertTrue((np.abs(opt_ms - x) < 2.).all())
def test_get_likelihood(self):
x = np.array([0.9, 0.91, 0.9, 2.05, 1.9, 3.1, 2.91])
m1 = np.array([1.1, 1.1, 1.1, 2.1, 2.1, 3.1, 3.1])
m2 = np.array([2.1, 2.1, 1.3, 1.1, 5.2, 1.1, 3.4])
f = mfblp_filter(w=5, s=np.array([1., 2., 3.]), a=1., b=10.)
l1 = f.get_likelihood(x, m1)
l2 = f.get_likelihood(x, m2)
self.assertTrue(l1.shape == x.shape)
self.assertTrue((l1 < l2).all())
self.assertTrue(l1.sum() < l2.sum())
def test_search_noisy_gaussian(self) :
x = np.array([12] * 100 + [19] * 100 + [15] * 100).astype(float)
np.random.seed(42)
x = x + np.random.normal(0, .5, size=x.shape)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
starts = np.ones(x.shape)
stepsize = 0.1
steps = 300
f = mfblp_filter(w=15, s=s, a=.5, b=.5)
opt_ms = f.search(x, starts, stepsize, steps)
self.assertTrue((np.abs(opt_ms - x) < 3.).all())
def test_search_noisy_uniform(self) :
x = np.array([12] * 100 + [19] * 100 + [15] * 100).astype(float)
np.random.seed(42)
x = x + np.random.uniform(low=-0.5, high=0.5)
s = np.array([9.5, 10, 12, 15, 16, 19, 20, 21]).astype(float)
starts = np.ones(x.shape)
stepsize = 0.1
steps = 300
f = mfblp_filter(w=11, s=s, a=.1, b=10.)
opt_ms = f.search(x, starts, stepsize, steps)
self.assertTrue((np.abs(opt_ms - x) < 2.).all())
def test_get_likelihood(self) :
x = np.array([0.9, 0.91, 0.9, 2.05, 1.9, 3.1, 2.91])
m1 = np.array([1.1, 1.1, 1.1, 2.1, 2.1, 3.1, 3.1])
m2 = np.array([2.1, 2.1, 1.3, 1.1, 5.2, 1.1, 3.4])
f = mfblp_filter(w=5, s=np.array([1., 2., 3.]), a=1., b=10.)
l1 = f.get_likelihood(x, m1)
l2 = f.get_likelihood(x, m2)
self.assertTrue(l1.shape == x.shape)
self.assertTrue((l1 < l2).all())
self.assertTrue(l1.sum() < l2.sum())