def work(self):
self.worked = True
kwargs = dict(
weights=self.weights,
mus=self.mus,
sigmas=self.sigmas,
low=self.low,
high=self.high,
q=self.q,
)
samples = GMM1(rng=self.rng, size=(self.n_samples, ), **kwargs)
samples = np.sort(samples)
edges = samples[::self.samples_per_bin]
#print samples
pdf = np.exp(GMM1_lpdf(edges[:-1], **kwargs))
dx = edges[1:] - edges[:-1]
y = 1 / dx / len(dx)
if self.show:
plt.scatter(edges[:-1], y)
plt.plot(edges[:-1], pdf)
plt.show()
err = (pdf - y)**2
print np.max(err)
print np.mean(err)
print np.median(err)
if not self.show:
assert np.max(err) < .1
assert np.mean(err) < .01
assert np.median(err) < .01
def test_mat_output(self):
samples = GMM1([.9999, .0001], [0.0, 1.0], [0.000001, 0.000001],
rng=self.rng,
size=[40, 20])
assert samples.shape == (40, 20)
assert -.001 < np.mean(samples) < .001, np.mean(samples)
assert np.var(samples) < .0001, np.var(samples)
def test_weights(self):
samples = GMM1([.9999, .0001], [0.0, 1.0], [0.000001, 0.000001],
rng=self.rng,
size=[1000])
assert samples.shape == (1000, )
#import matplotlib.pyplot as plt
#plt.hist(samples)
#plt.show()
assert -.001 < np.mean(samples) < .001, np.mean(samples)
assert np.var(samples) < .0001, np.var(samples)
def test_mus_make_variance(self):
samples = GMM1([.5, .5], [0.0, 1.0], [0.000001, 0.000001],
rng=self.rng,
size=[1000])
print samples.shape
#import matplotlib.pyplot as plt
#plt.hist(samples)
#plt.show()
assert .45 < np.mean(samples) < .55, np.mean(samples)
assert .2 < np.var(samples) < .3, np.var(samples)
def work(self, **kwargs):
self.__dict__.update(kwargs)
del kwargs
self.worked = True
gkwargs = dict(
weights=self.weights,
mus=self.mus,
sigmas=self.sigmas,
low=self.low,
high=self.high,
q=self.q,
)
samples = GMM1(rng=self.rng, size=(self.n_samples, ), **
gkwargs) / self.q
print 'drew', len(samples), 'samples'
assert np.all(samples == samples.astype('int'))
min_max = int(samples.min()), int(samples.max())
counts = np.bincount(samples.astype('int') - min_max[0])
print counts
xcoords = np.arange(min_max[0], min_max[1] + 1) * self.q
prob = np.exp(GMM1_lpdf(xcoords, **gkwargs))
assert counts.sum() == self.n_samples
y = counts / float(self.n_samples)
if self.show:
import matplotlib.pyplot as plt
plt.scatter(xcoords, y, c='r', label='empirical')
plt.scatter(xcoords, prob, c='b', label='predicted')
plt.legend()
plt.title(str(self.show))
plt.show()
err = (prob - y)**2
print np.max(err)
print np.mean(err)
print np.median(err)
if self.show:
raise nose.SkipTest()
else:
assert np.max(err) < .1
assert np.mean(err) < .01
assert np.median(err) < .01
def work(self, **kwargs):
self.__dict__.update(kwargs)
del kwargs
self.worked = True
gkwargs = dict(
weights=self.weights,
mus=self.mus,
sigmas=self.sigmas,
low=self.low,
high=self.high,
q=self.q,
)
samples = old_div(
GMM1(rng=self.rng, size=(self.n_samples, ), **gkwargs), self.q)
print("drew", len(samples), "samples")
assert np.all(samples == samples.astype("int"))
min_max = int(samples.min()), int(samples.max())
counts = np.bincount(samples.astype("int") - min_max[0])
print(counts)
xcoords = np.arange(min_max[0], min_max[1] + 1) * self.q
prob = np.exp(GMM1_lpdf(xcoords, **gkwargs))
assert counts.sum() == self.n_samples
y = old_div(counts, float(self.n_samples))
if self.show:
plt.scatter(xcoords, y, c="r", label="empirical")
plt.scatter(xcoords, prob, c="b", label="predicted")
plt.legend()
plt.title(str(self.show))
plt.show()
err = (prob - y)**2
print(np.max(err))
print(np.mean(err))
print(np.median(err))
if self.show:
raise nose.SkipTest()
else:
assert np.max(err) < 0.1
assert np.mean(err) < 0.01
assert np.median(err) < 0.01
def test_sigma_is_used_correctly(self):
samples = GMM1([1], [0.0], [10.0], size=[1000], rng=self.rng)
assert 9 < np.std(samples) < 11
def test_mu_is_used_correctly(self):
assert np.allclose(10, GMM1([1], [10.0], [0.0000001], rng=self.rng))
