def work(self, **kwargs):
self.__dict__.update(kwargs)
self.worked = True
samples = LGMM1(rng=self.rng,
size=(self.n_samples, ),
**self.LGMM1_kwargs)
samples = np.sort(samples)
edges = samples[::self.samples_per_bin]
centers = .5 * edges[:-1] + .5 * edges[1:]
print edges
pdf = np.exp(LGMM1_lpdf(centers, **self.LGMM1_kwargs))
dx = edges[1:] - edges[:-1]
y = 1 / dx / len(dx)
if self.show:
plt.scatter(centers, y)
plt.plot(centers, 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 work(self, **kwargs):
self.__dict__.update(kwargs)
self.worked = True
samples = old_div(
LGMM1(rng=self.rng, size=(self.n_samples, ), **self.kwargs),
self.q)
# -- we've divided the LGMM1 by self.q to get ints here
assert np.all(samples == samples.astype("int"))
min_max = int(samples.min()), int(samples.max())
print("SAMPLES RANGE", min_max)
counts = np.bincount(samples.astype("int") - min_max[0])
# print samples
# print counts
xcoords = np.arange(min_max[0], min_max[1] + 0.5) * self.q
prob = np.exp(LGMM1_lpdf(xcoords, **self.kwargs))
print(xcoords)
print(prob)
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.show()
# -- calculate errors on the low end, don't take a mean
# over all the range spanned by a few outliers.
err = ((prob - y)**2)[:20]
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 work(self, **kwargs):
self.__dict__.update(kwargs)
self.worked = True
samples = LGMM1(rng=self.rng, size=(self.n_samples, ), **
self.kwargs) / self.q
# -- we've divided the LGMM1 by self.q to get ints here
assert np.all(samples == samples.astype('int'))
min_max = int(samples.min()), int(samples.max())
print 'SAMPLES RANGE', min_max
counts = np.bincount(samples.astype('int') - min_max[0])
#print samples
#print counts
xcoords = np.arange(min_max[0], min_max[1] + 0.5) * self.q
prob = np.exp(LGMM1_lpdf(xcoords, **self.kwargs))
print xcoords
print prob
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.show()
# -- calculate errors on the low end, don't take a mean
# over all the range spanned by a few outliers.
err = ((prob - y)**2)[:20]
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