def test_fast_cvm(n_samples=1000):
random = RandomState()
data1 = random.uniform(size=n_samples)
weights1 = random.uniform(size=n_samples)
mask = random.uniform(size=n_samples) > 0.5
data2 = data1[mask]
weights2 = weights1[mask]
a = cvm_2samp(data1, data2, weights1, weights2)
prepared_data1, prepared_weights1, F1 = prepare_distibution(data1, weights1)
b = _cvm_2samp_fast(prepared_data1, data2, prepared_weights1, weights2, F1=F1)
assert numpy.allclose(a, b)
def test_ks2samp_fast(size=1000):
y1 = RandomState().uniform(size=size)
y2 = y1[RandomState().uniform(size=size) > 0.5]
a = ks_2samp(y1, y2)[0]
prep_data, prep_weights, prep_F = prepare_distibution(y1, numpy.ones(len(y1)))
b = _ks_2samp_fast(prep_data, y2, prep_weights, numpy.ones(len(y2)), F1=prep_F)
c = _ks_2samp_fast(prep_data, y2, prep_weights, numpy.ones(len(y2)), F1=prep_F)
d = ks_2samp_weighted(y1, y2, numpy.ones(len(y1)) / 3, numpy.ones(len(y2)) / 4)
assert numpy.allclose(a, b, rtol=1e-2, atol=1e-3)
assert numpy.allclose(b, c)
assert numpy.allclose(b, d)
print('ks2samp is ok')
def test_fast_cvm(n_samples=1000):
random = RandomState()
data1 = random.uniform(size=n_samples)
weights1 = random.uniform(size=n_samples)
mask = random.uniform(size=n_samples) > 0.5
data2 = data1[mask]
weights2 = weights1[mask]
a = cvm_2samp(data1, data2, weights1, weights2)
prepared_data1, prepared_weights1, F1 = prepare_distibution(
data1, weights1)
b = _cvm_2samp_fast(prepared_data1,
data2,
prepared_weights1,
weights2,
F1=F1)
assert numpy.allclose(a, b)
def test_ks2samp_fast(size=1000):
y1 = RandomState().uniform(size=size)
y2 = y1[RandomState().uniform(size=size) > 0.5]
a = ks_2samp(y1, y2)[0]
prep_data, prep_weights, prep_F = prepare_distibution(
y1, numpy.ones(len(y1)))
b = _ks_2samp_fast(prep_data,
y2,
prep_weights,
numpy.ones(len(y2)),
F1=prep_F)
c = _ks_2samp_fast(prep_data,
y2,
prep_weights,
numpy.ones(len(y2)),
F1=prep_F)
d = ks_2samp_weighted(y1, y2,
numpy.ones(len(y1)) / 3,
numpy.ones(len(y2)) / 4)
assert numpy.allclose(a, b, rtol=1e-2, atol=1e-3)
assert numpy.allclose(b, c)
assert numpy.allclose(b, d)
print('ks2samp is ok')
