本文整理汇总了Python中nipy.testing.assert_almost_equal函数的典型用法代码示例。如果您正苦于以下问题:Python assert_almost_equal函数的具体用法?Python assert_almost_equal怎么用?Python assert_almost_equal使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了assert_almost_equal函数的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_PCAMask
def test_PCAMask():
# for 2 and 4D case
ntotal = data['nimages'] - 1
ncomp = 5
arr4d = data['fmridata']
mask3d = data['mask']
arr2d = arr4d.reshape((-1, data['nimages']))
mask1d = mask3d.reshape((-1))
for arr, mask in (arr4d, mask3d), (arr2d, mask1d):
p = pca(arr, -1, mask, ncomp=ncomp)
assert_equal(p['basis_vectors'].shape, (data['nimages'], ntotal))
assert_equal(p['basis_projections'].shape, mask.shape + (ncomp,))
assert_equal(p['pcnt_var'].shape, (ntotal,))
assert_almost_equal(p['pcnt_var'].sum(), 100.)
# Any reasonable datatype for mask
for dt in ([np.bool_] +
np.sctypes['int'] +
np.sctypes['uint'] +
np.sctypes['float']):
p = pca(arr4d, -1, mask3d.astype(dt), ncomp=ncomp)
assert_equal(p['basis_vectors'].shape, (data['nimages'], ntotal))
assert_equal(p['basis_projections'].shape, mask3d.shape + (ncomp,))
assert_equal(p['pcnt_var'].shape, (ntotal,))
assert_almost_equal(p['pcnt_var'].sum(), 100.)
# Mask data shape must match
assert_raises(ValueError, pca, arr4d, -1, mask1d)
示例2: test_apply_affine
def test_apply_affine():
XYZ = (100*(np.random.rand(10,11,12,3)-.5)).astype('int')
T = np.eye(4)
T[0:3,0:3] = np.random.rand(3,3)
T[0:3,3] = 100*(np.random.rand(3)-.5)
_XYZ = apply_affine(inverse_affine(T), apply_affine(T, XYZ))
assert_almost_equal(_XYZ, XYZ)
示例3: _test_similarity_measure
def _test_similarity_measure(simi, val):
I = Image(make_data_int16(), dummy_affine)
J = Image(I.data.copy(), dummy_affine)
regie = IconicRegistration(I, J)
regie.set_source_fov(spacing=[2,1,3])
regie.similarity = simi
assert_almost_equal(regie.eval(np.eye(4)), val)
示例4: test_search2
def test_search2():
# Test that the search region works.
search = rft.IntrinsicVolumes([3,4,5])
x = np.linspace(0.1,10,100)
stats = [rft.Gaussian(search=search)]
ostats = [rft.Gaussian()]
for dfn in range(5,10):
for dfd in [40,50,np.inf]:
stats.append(rft.FStat(dfn=dfn, dfd=dfd, search=search))
ostats.append(rft.FStat(dfn=dfn, dfd=dfd))
stats.append(rft.TStat(dfd=dfd, search=search))
ostats.append(rft.TStat(dfd=dfd))
stats.append(rft.ChiSquared(dfn=dfn, search=search))
ostats.append(rft.ChiSquared(dfn=dfn))
for i in range(len(stats)):
stat = stats[i]
ostat = ostats[i]
v1 = stat(x)
v2 = 0
for j in range(search.mu.shape[0]):
v2 += ostat.density(x, j) * search.mu[j]
assert_almost_equal(v1, v2)
示例5: test_evaluate_exact
def test_evaluate_exact():
# without mfx nor spatial relaxation
prng = np.random.RandomState(10)
data, XYZ, XYZvol, vardata, signal = make_data(n=20,
dim=np.array([20,20,20]), r=3, amplitude=5, noise=0,
jitter=0, prng=prng)
p = len(signal)
XYZvol *= 0
XYZvol[list(XYZ)] = np.arange(p)
P = os.multivariate_stat(data)
P.init_hidden_variables()
P.evaluate(nsimu=100, burnin=100, J=[XYZvol[5, 5, 5]],
compute_post_mean=True, verbose=verbose)
P.log_likelihood_values = P.compute_log_region_likelihood()
# Verify code consistency
Q = os.multivariate_stat(data, vardata*0, XYZ, std=0, sigma=5)
Q.init_hidden_variables()
Q.evaluate(nsimu=100, burnin=100, J = [XYZvol[5,5,5]],
compute_post_mean=True, update_spatial=False,
verbose=verbose)
Q.log_likelihood_values = Q.compute_log_region_likelihood()
yield assert_almost_equal(P.mean_m.mean(),
Q.mean_m.mean(),
int(np.log10(P.nsimu))-1)
yield assert_almost_equal(Q.log_likelihood_values.sum(),
P.log_likelihood_values.sum(), 0)
示例6: test_Ragreement
def test_Ragreement():
# This code would fit the two-way ANOVA model in R
# X = read.table('http://www-stat.stanford.edu/~jtaylo/courses/stats191/data/kidney.table', header=T)
# names(X)
# X$Duration = factor(X$Duration)
# X$Weight = factor(X$Weight)
# lm(Days~Duration*Weight, X)
# A = anova(lm(Days~Duration*Weight, X))
# rA = rpy.r('A')
rA = {
"Df": [1, 2, 2, 54],
"F value": [7.2147239263803673, 13.120973926380339, 1.8813266871165633, np.nan],
"Mean Sq": [209.06666666666663, 380.21666666666584, 54.51666666666663, 28.977777777777778],
"Pr(>F)": [0.0095871255601553771, 2.2687781292164585e-05, 0.16224035152442268, np.nan],
"Sum Sq": [209.06666666666663, 760.43333333333169, 109.03333333333326, 1564.8],
}
# rn = rpy.r('rownames(A)')
rn = ["Duration", "Weight", "Duration:Weight", "Residuals"]
pairs = [
(rn.index("Duration"), "Duration"),
(rn.index("Weight"), "Weight"),
(rn.index("Duration:Weight"), "Interaction"),
]
for i, j in pairs:
assert_almost_equal(F[j], rA["F value"][i])
assert_almost_equal(p[j], rA["Pr(>F)"][i])
assert_almost_equal(MS[j], rA["Mean Sq"][i])
assert_almost_equal(df[j], rA["Df"][i])
assert_almost_equal(SS[j], rA["Sum Sq"][i])
示例7: _test_similarity_measure
def _test_similarity_measure(simi, val):
I = AffineImage(make_data_int16(), dummy_affine, 'ijk')
J = AffineImage(I.get_data().copy(), dummy_affine, 'ijk')
R = HistogramRegistration(I, J)
R.subsample(spacing=[2,1,3])
R.similarity = simi
assert_almost_equal(R.eval(Affine()), val)
示例8: test_model_selection_exact
def test_model_selection_exact():
prng = np.random.RandomState(10)
data, XYZ, XYZvol, vardata, signal = make_data(n=30, dim=20, r=3,
amplitude=1, noise=0, jitter=0, prng=prng)
labels = (signal > 0).astype(int)
P1 = os.multivariate_stat(data, labels=labels)
P1.init_hidden_variables()
P1.evaluate(nsimu=100, burnin=10, verbose=verbose)
L1 = P1.compute_log_region_likelihood()
Prior1 = P1.compute_log_prior()
#v, m_mean, m_var = P1.v.copy(), P1.m_mean.copy(), P1.m_var.copy()
Post1 = P1.compute_log_posterior(nsimu=1e2, burnin=1e2, verbose=verbose)
M1 = L1 + Prior1[:-1] - Post1[:-1]
yield assert_almost_equal(M1.mean(),
P1.compute_marginal_likelihood().mean(), 0)
P0 = os.multivariate_stat(data, labels=labels)
P0.network *= 0
P0.init_hidden_variables()
P0.evaluate(nsimu=100, burnin=100, verbose=verbose)
L0 = P0.compute_log_region_likelihood()
Prior0 = P0.compute_log_prior()
Post0 = P0.compute_log_posterior(nsimu=1e2, burnin=1e2,
verbose=verbose)
M0 = L0 + Prior0[:-1] - Post0[:-1]
yield assert_almost_equal(M0.mean(),
P0.compute_marginal_likelihood().mean(), 0)
yield assert_true(M1[1] > M0[1])
yield assert_true(M1[0] < M0[0])
示例9: test_alias
def test_alias():
x = F.Term("x")
f = F.aliased_function("f", lambda x: 2 * x)
g = F.aliased_function("g", lambda x: np.sqrt(x))
ff = F.Formula([f(x), g(x) ** 2])
n = F.make_recarray([2, 4, 5], "x")
yield assert_almost_equal(ff.design(n)["f(x)"], n["x"] * 2)
yield assert_almost_equal(ff.design(n)["g(x)**2"], n["x"])
示例10: test_alias
def test_alias():
x = F.Term('x')
f = implemented_function('f', lambda x: 2*x)
g = implemented_function('g', lambda x: np.sqrt(x))
ff = F.Formula([f(x), g(x)**2])
n = F.make_recarray([2,4,5], 'x')
yield assert_almost_equal(ff.design(n)['f(x)'], n['x']*2)
yield assert_almost_equal(ff.design(n)['g(x)**2'], n['x'])
示例11: test_PCANoMask_nostandardize
def test_PCANoMask_nostandardize():
ntotal = data['nimages'] - 1
ncomp = 5
p = pca(data['fmridata'], -1, ncomp=ncomp, standardize=False)
assert_equal(p['basis_vectors'].shape, (data['nimages'], ntotal))
assert_equal(p['basis_projections'].shape, data['mask'].shape + (ncomp,))
assert_equal(p['pcnt_var'].shape, (ntotal,))
assert_almost_equal(p['pcnt_var'].sum(), 100.)
示例12: test_same_basis
def test_same_basis():
arr4d = data['fmridata']
shp = arr4d.shape
arr2d = arr4d.reshape((np.prod(shp[:3]), shp[3]))
res = pos1pca(arr2d, axis=-1)
p1b_0 = res['basis_vectors']
for i in range(3):
res_again = pos1pca(arr2d, axis=-1)
assert_almost_equal(res_again['basis_vectors'], p1b_0)
示例13: test_scaling
def test_scaling():
with InTemporaryDirectory():
for dtype_type in (np.uint8, np.uint16,
np.int16, np.int32,
np.float32):
newdata, data = uint8_to_dtype(dtype_type, 'img.nii')
assert_almost_equal(newdata, data)
newdata, data = float32_to_dtype(dtype_type, 'img.nii')
assert_almost_equal(newdata, data)
示例14: test_search3
def test_search3():
# In the Gaussian case, test that search and product give same results.
search = rft.IntrinsicVolumes([3, 4, 5, 7])
g1 = rft.Gaussian(search=search)
g2 = rft.Gaussian(product=search)
x = np.linspace(0.1, 10, 100)
y1 = g1(x)
y2 = g2(x)
assert_almost_equal(y1, y2)
示例15: test_mat2vec
def test_mat2vec():
mat = np.eye(4)
tmp = np.random.rand(3,3)
U, s, Vt = np.linalg.svd(tmp)
U /= np.linalg.det(U)
Vt /= np.linalg.det(Vt)
mat[0:3,0:3] = np.dot(np.dot(U, np.diag(s)), Vt)
T = Affine(mat)
assert_almost_equal(T.as_affine(), mat)