def test_distributions_normal_probability(): d = NormalDistribution(5, 2) e = NormalDistribution(5., 2.) assert_almost_equal(d.probability(5), 0.19947114) assert_equal(d.probability(5), e.probability(5)) assert_equal(d.probability(5), d.probability(5.)) assert_almost_equal(d.probability(0), 0.0087641502) assert_equal(d.probability(0), e.probability(0.))
def test_distributions_normal_probability(): d = NormalDistribution(5, 2) e = NormalDistribution(5., 2.) assert_almost_equal(d.probability(5), 0.19947114) assert_equal(d.probability(5), e.probability(5)) assert_equal(d.probability(5), d.probability(5.)) assert_almost_equal(d.probability(0), 0.0087641502) assert_equal(d.probability(0), e.probability(0.))
def plot_HalfNorm():
X = np.random.sample(size=(100000, 1))
dn = NormalDistribution(0, 1)
dhn1 = HalfNormalDistribution(0.5)
dhn2 = HalfNormalDistribution.from_samples(X)
dhn3 = HalfNormalDistribution(1)
x = np.arange(-5, 5, 0.1)
fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(x, dn.probability(x), label='Normal')
ax.plot(x, set_y(x, dhn1), label='HalfNorm, s=0.5')
ax.plot(x, set_y(x, dhn2), label='HalfNorm, s=rand')
ax.plot(x, set_y(x, dhn3), label='HalfNorm, s=1')
ax.set_ylabel('Probability', fontsize=10)
ax.legend(fontsize=10)
plt.savefig('/scratch/chd5n/test.png', bbox_inches='tight')
print('plot written to', '/scratch/chd5n/test.png')
def test_distributions_normal_underflow_probability(): d = NormalDistribution(5, 1e-10) assert_almost_equal(d.probability(1e100), 0.0)
def test_distributions_normal_nan_probability(): d = NormalDistribution(5, 2) assert_equal(d.probability(nan), 1) assert_array_almost_equal(d.probability([nan, 5]), [1, 0.199471])
def test_distributions_normal_underflow_probability(): d = NormalDistribution(5, 1e-10) assert_almost_equal(d.probability(1e100), 0.0)
def test_distributions_normal_nan_probability(): d = NormalDistribution(5, 2) assert_equal(d.probability(nan), 1) assert_array_almost_equal(d.probability([nan, 5]), [1, 0.199471])
