The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def _init_coefs(X, method='corrcoef'):
if method == 'corrcoef':
return np.corrcoef(X, rowvar=False), 1.0
elif method == 'cov':
init_cov = np.cov(X, rowvar=False)
return init_cov, np.max(np.abs(np.triu(init_cov)))
elif method == 'spearman':
return spearman_correlation(X, rowvar=False), 1.0
elif method == 'kendalltau':
return kendalltau_correlation(X, rowvar=False), 1.0
elif callable(method):
return method(X)
else:
raise ValueError(
("initialize_method must be 'corrcoef' or 'cov', "
"passed \'{}\' .".format(method))
) Example 2
def test_tril_triu_ndim3():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.array([
[[1, 1], [1, 1]],
[[1, 1], [1, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_tril_desired = np.array([
[[1, 0], [1, 1]],
[[1, 0], [1, 0]],
[[1, 0], [0, 0]],
], dtype=dtype)
a_triu_desired = np.array([
[[1, 1], [0, 1]],
[[1, 1], [0, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_triu_observed = np.triu(a)
a_tril_observed = np.tril(a)
yield assert_array_equal, a_triu_observed, a_triu_desired
yield assert_array_equal, a_tril_observed, a_tril_desired
yield assert_equal, a_triu_observed.dtype, a.dtype
yield assert_equal, a_tril_observed.dtype, a.dtype Example 3
def test_tril_triu_dtype():
# Issue 4916
# tril and triu should return the same dtype as input
for c in np.typecodes['All']:
if c == 'V':
continue
arr = np.zeros((3, 3), dtype=c)
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
# check special cases
arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'],
['2004-01-01T12:00', '2003-01-03T13:45']],
dtype='datetime64')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
arr = np.zeros((3,3), dtype='f4,f4')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype) Example 4
def potential_numba_array(cluster):
d = distances_numba_array(cluster)
# Original: dtri = np.triu(d)
# np.triu is not supported; so write my own loop to clear the
# lower triangle
for i in range(d.shape[0]):
for j in range(d.shape[1]):
if i > j:
d[i, j] = 0
# Original: lj_numba_array(d[d > 1e-6]).sum()
# d[d > 1e-6] is not supported due to the indexing with boolean
# array. Replace with custom loop.
energy = 0.0
for v in d.flat:
if v > 1e-6:
energy += lj_numba_array(v)
return energy Example 5
def pairwise_expansion(x, func, reflexive=True):
"""Computes func(xi, xj) over all possible indices i and j, where func is an arbitrary function
if reflexive == False, only pairs with i != j are considered
"""
x_height, x_width = x.shape
if reflexive:
k = 0
else:
k = 1
mask = numpy.triu(numpy.ones((x_width, x_width)), k) > 0.5
# mask = mask.reshape((1,x_width,x_width))
y1 = x.reshape(x_height, x_width, 1)
y2 = x.reshape(x_height, 1, x_width)
yexp = func(y1, y2)
# print "yexp.shape=", yexp.shape
# print "mask.shape=", mask.shape
out = yexp[:, mask]
# print "out.shape=", out.shape
# yexp.reshape((x_height, N*N))
return out Example 6
def products_2(x, func, k=0):
"""Computes func(xi, xj) over all possible indices i and j constrained to j >= i+k.
func is an arbitrary function, and k >= 0 is an integer
"""
x_height, x_width = x.shape
mask = numpy.triu(numpy.ones((x_width, x_width)), k) > 0.5
z1 = x.reshape(x_height, x_width, 1)
z2 = x.reshape(x_height, 1, x_width)
yexp = func(z1, z2) # twice computation, but performance gain due to lack of loops
out = yexp[:, mask]
return out Example 7
def tangent_space(covmats, Cref):
"""Project a set of covariance matrices in the tangent space according to the given reference point Cref
:param covmats: Covariance matrices set, Ntrials X Nchannels X Nchannels
:param Cref: The reference covariance matrix
:returns: the Tangent space , a matrix of Ntrials X (Nchannels*(Nchannels+1)/2)
"""
Nt, Ne, Ne = covmats.shape
Cm12 = invsqrtm(Cref)
idx = numpy.triu_indices_from(Cref)
T = numpy.empty((Nt, Ne * (Ne + 1) / 2))
coeffs = (
numpy.sqrt(2) *
numpy.triu(
numpy.ones(
(Ne,
Ne)),
1) +
numpy.eye(Ne))[idx]
for index in range(Nt):
tmp = numpy.dot(numpy.dot(Cm12, covmats[index, :, :]), Cm12)
tmp = logm(tmp)
T[index, :] = numpy.multiply(coeffs, tmp[idx])
return T Example 8
def untangent_space(T, Cref):
"""Project a set of Tangent space vectors in the manifold according to the given reference point Cref
:param T: the Tangent space , a matrix of Ntrials X (Nchannels*(Nchannels+1)/2)
:param Cref: The reference covariance matrix
:returns: A set of Covariance matrix, Ntrials X Nchannels X Nchannels
"""
Nt, Nd = T.shape
Ne = int((numpy.sqrt(1 + 8 * Nd) - 1) / 2)
C12 = sqrtm(Cref)
idx = numpy.triu_indices_from(Cref)
covmats = numpy.empty((Nt, Ne, Ne))
covmats[:, idx[0], idx[1]] = T
for i in range(Nt):
covmats[i] = numpy.diag(numpy.diag(covmats[i])) + numpy.triu(
covmats[i], 1) / numpy.sqrt(2) + numpy.triu(covmats[i], 1).T / numpy.sqrt(2)
covmats[i] = expm(covmats[i])
covmats[i] = numpy.dot(numpy.dot(C12, covmats[i]), C12)
return covmats Example 9
def test_tril_triu_ndim3():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.array([
[[1, 1], [1, 1]],
[[1, 1], [1, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_tril_desired = np.array([
[[1, 0], [1, 1]],
[[1, 0], [1, 0]],
[[1, 0], [0, 0]],
], dtype=dtype)
a_triu_desired = np.array([
[[1, 1], [0, 1]],
[[1, 1], [0, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_triu_observed = np.triu(a)
a_tril_observed = np.tril(a)
yield assert_array_equal, a_triu_observed, a_triu_desired
yield assert_array_equal, a_tril_observed, a_tril_desired
yield assert_equal, a_triu_observed.dtype, a.dtype
yield assert_equal, a_tril_observed.dtype, a.dtype Example 10
def test_tril_triu_dtype():
# Issue 4916
# tril and triu should return the same dtype as input
for c in np.typecodes['All']:
if c == 'V':
continue
arr = np.zeros((3, 3), dtype=c)
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
# check special cases
arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'],
['2004-01-01T12:00', '2003-01-03T13:45']],
dtype='datetime64')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
arr = np.zeros((3,3), dtype='f4,f4')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype) Example 11
def has_approx_support(m, m_hat, prob=0.01):
"""Returns 1 if model selection error is less than or equal to prob rate,
0 else.
NOTE: why does np.nonzero/np.flatnonzero create so much problems?
"""
m_nz = np.flatnonzero(np.triu(m, 1))
m_hat_nz = np.flatnonzero(np.triu(m_hat, 1))
upper_diagonal_mask = np.flatnonzero(np.triu(np.ones(m.shape), 1))
not_m_nz = np.setdiff1d(upper_diagonal_mask, m_nz)
intersection = np.in1d(m_hat_nz, m_nz) # true positives
not_intersection = np.in1d(m_hat_nz, not_m_nz) # false positives
true_positive_rate = 0.0
if len(m_nz):
true_positive_rate = 1. * np.sum(intersection) / len(m_nz)
true_negative_rate = 1. - true_positive_rate
false_positive_rate = 0.0
if len(not_m_nz):
false_positive_rate = 1. * np.sum(not_intersection) / len(not_m_nz)
return int(np.less_equal(true_negative_rate + false_positive_rate, prob)) Example 12
def read_mongodb_matrix(tickers, matrix_name):
mis = MatrixItem.objects(i__in = tickers,
j__in = tickers,
matrix_name = matrix_name)
n = len(tickers)
available_tickers = set([mi.i for mi in mis])
np.random.seed(n)
a = np.absolute(np.random.normal(0, 0.001, [n, n]))
a_triu = np.triu(a, k=0)
a_tril = np.tril(a, k=0)
a_diag = np.diag(np.diag(a))
a_sym_triu = a_triu + a_triu.T - a_diag
matrix = pd.DataFrame(a_sym_triu,
index = tickers,
columns = tickers)
for mi in mis:
if abs(mi.v) > 10:
mi.v = 0.001
matrix.set_value(mi.i, mi.j, mi.v)
matrix.set_value(mi.j, mi.i, mi.v)
matrix = matrix.round(6)
return matrix Example 13
def test_preserve_trace_ground_state(self, dm):
dm.hadamard(2)
assert np.allclose(dm.trace(), 1)
dm.hadamard(4)
assert np.allclose(dm.trace(), 1)
dm.hadamard(0)
assert np.allclose(dm.trace(), 1)
# @pytest.mark.skip
# def test_squares_to_one(self, dm_random):
# dm = dm_random
# a0 = dm.to_array()
# dm.hadamard(4)
# dm.hadamard(4)
# # dm.hadamard(2)
# # dm.hadamard(2)
# # dm.hadamard(0)
# # dm.hadamard(0)
# a1 = dm.to_array()
# assert np.allclose(np.triu(a0), np.triu(a1)) Example 14
def test_tril_triu_ndim3():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.array([
[[1, 1], [1, 1]],
[[1, 1], [1, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_tril_desired = np.array([
[[1, 0], [1, 1]],
[[1, 0], [1, 0]],
[[1, 0], [0, 0]],
], dtype=dtype)
a_triu_desired = np.array([
[[1, 1], [0, 1]],
[[1, 1], [0, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_triu_observed = np.triu(a)
a_tril_observed = np.tril(a)
yield assert_array_equal, a_triu_observed, a_triu_desired
yield assert_array_equal, a_tril_observed, a_tril_desired
yield assert_equal, a_triu_observed.dtype, a.dtype
yield assert_equal, a_tril_observed.dtype, a.dtype Example 15
def test_tril_triu_dtype():
# Issue 4916
# tril and triu should return the same dtype as input
for c in np.typecodes['All']:
if c == 'V':
continue
arr = np.zeros((3, 3), dtype=c)
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
# check special cases
arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'],
['2004-01-01T12:00', '2003-01-03T13:45']],
dtype='datetime64')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
arr = np.zeros((3,3), dtype='f4,f4')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype) Example 16
def make_symmetric_lower(mat):
'''
Copies the matrix entries below the main diagonal to the upper triangle
half of the matrix. Leaves the diagonal unchanged. Returns a `NumPy` matrix
object.
**mat** : `numpy.matrix`
A lower diagonal matrix.
returns : `numpy.matrix`
The lower triangle matrix.
'''
# extract lower triangle from matrix (including diagonal)
tmp_mat = np.tril(mat)
# if the matrix given wasn't a lower triangle matrix, raise an error
if (mat != tmp_mat).all():
raise Exception('Matrix to symmetrize is not a lower diagonal matrix.')
# add its transpose to itself, zeroing the diagonal to avoid doubling
tmp_mat += np.triu(tmp_mat.transpose(), 1)
return np.asmatrix(tmp_mat) Example 17
def test_tril_triu_ndim3():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.array([
[[1, 1], [1, 1]],
[[1, 1], [1, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_tril_desired = np.array([
[[1, 0], [1, 1]],
[[1, 0], [1, 0]],
[[1, 0], [0, 0]],
], dtype=dtype)
a_triu_desired = np.array([
[[1, 1], [0, 1]],
[[1, 1], [0, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_triu_observed = np.triu(a)
a_tril_observed = np.tril(a)
yield assert_array_equal, a_triu_observed, a_triu_desired
yield assert_array_equal, a_tril_observed, a_tril_desired
yield assert_equal, a_triu_observed.dtype, a.dtype
yield assert_equal, a_tril_observed.dtype, a.dtype Example 18
def test_tril_triu_dtype():
# Issue 4916
# tril and triu should return the same dtype as input
for c in np.typecodes['All']:
if c == 'V':
continue
arr = np.zeros((3, 3), dtype=c)
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
# check special cases
arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'],
['2004-01-01T12:00', '2003-01-03T13:45']],
dtype='datetime64')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
arr = np.zeros((3,3), dtype='f4,f4')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype) Example 19
def _init_topics_assignement(self):
dim = (self.J, self.J, 2)
alpha_0 = self.alpha_0
# Poisson way
#z = np.array( [poisson(alpha_0, size=dim) for dim in data_dims] )
# Random way
K = self.K_init
z = np.random.randint(0, K, (dim))
if self.likelihood._symmetric:
z[:, :, 0] = np.triu(z[:, :, 0]) + np.triu(z[:, :, 0], 1).T
z[:, :, 1] = np.triu(z[:, :, 1]) + np.triu(z[:, :, 1], 1).T
# LDA way
# improve local optima ?
#theta_j = dirichlet([1, gmma])
#todo ?
return z Example 20
def get_data_prop(self):
prop = super(frontendNetwork, self).get_data_prop()
if self.is_symmetric():
nnz = np.triu(self.data).sum()
else:
nnz = self.data.sum()
_nnz = self.data.sum(axis=1)
d = {'instances': self.data.shape[1],
'nnz': nnz,
'nnz_mean': _nnz.mean(),
'nnz_var': _nnz.var(),
'density': self.density(),
'diameter': self.diameter(),
'clustering_coef': self.clustering_coefficient(),
'modularity': self.modularity(),
'communities': self.clusters_len(),
'features': self.get_nfeat(),
'directed': not self.is_symmetric()
}
prop.update(d)
return prop Example 21
def setUp(self):
self.nwalkers = 100
self.ndim = 5
self.ntemp = 20
self.N = 1000
self.mean = np.zeros(self.ndim)
self.cov = 0.5 - np.random.rand(self.ndim ** 2) \
.reshape((self.ndim, self.ndim))
self.cov = np.triu(self.cov)
self.cov += self.cov.T - np.diag(self.cov.diagonal())
self.cov = np.dot(self.cov, self.cov)
self.icov = np.linalg.inv(self.cov)
self.p0 = [0.1 * np.random.randn(self.ndim)
for i in range(self.nwalkers)]
self.truth = np.random.multivariate_normal(self.mean, self.cov, 100000) Example 22
def test_tril_triu_ndim3():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.array([
[[1, 1], [1, 1]],
[[1, 1], [1, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_tril_desired = np.array([
[[1, 0], [1, 1]],
[[1, 0], [1, 0]],
[[1, 0], [0, 0]],
], dtype=dtype)
a_triu_desired = np.array([
[[1, 1], [0, 1]],
[[1, 1], [0, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_triu_observed = np.triu(a)
a_tril_observed = np.tril(a)
yield assert_array_equal, a_triu_observed, a_triu_desired
yield assert_array_equal, a_tril_observed, a_tril_desired
yield assert_equal, a_triu_observed.dtype, a.dtype
yield assert_equal, a_tril_observed.dtype, a.dtype Example 23
def test_tril_triu_dtype():
# Issue 4916
# tril and triu should return the same dtype as input
for c in np.typecodes['All']:
if c == 'V':
continue
arr = np.zeros((3, 3), dtype=c)
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
# check special cases
arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'],
['2004-01-01T12:00', '2003-01-03T13:45']],
dtype='datetime64')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
arr = np.zeros((3,3), dtype='f4,f4')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype) Example 24
def verify_solve_grad(self, m, n, A_structure, lower, rng):
# ensure diagonal elements of A relatively large to avoid numerical
# precision issues
A_val = (rng.normal(size=(m, m)) * 0.5 +
numpy.eye(m)).astype(config.floatX)
if A_structure == 'lower_triangular':
A_val = numpy.tril(A_val)
elif A_structure == 'upper_triangular':
A_val = numpy.triu(A_val)
if n is None:
b_val = rng.normal(size=m).astype(config.floatX)
else:
b_val = rng.normal(size=(m, n)).astype(config.floatX)
eps = None
if config.floatX == "float64":
eps = 2e-8
solve_op = Solve(A_structure=A_structure, lower=lower)
utt.verify_grad(solve_op, [A_val, b_val], 3, rng, eps=eps) Example 25
def test_as_spin_response(self):
response = self.response_factory()
num_samples = 100
num_variables = 200
samples = np.triu(np.ones((num_samples, num_variables))) * 2 - 1
energies = np.zeros((num_samples,))
response.add_samples_from_array(samples, energies)
dimod_response = response.as_spin_response()
for s, t in zip(response, dimod_response):
self.assertEqual(s, t)
dimod_response = response.as_spin_response(data_copy=True)
for (__, dat), (__, dat0) in zip(response.samples(data=True),
dimod_response.samples(data=True)):
self.assertNotEqual(id(dat), id(dat0)) Example 26
def test_as_binary_response(self):
response = self.response_factory()
num_samples = 100
num_variables = 200
samples = np.triu(np.ones((num_samples, num_variables)))
energies = np.zeros((num_samples,))
response.add_samples_from_array(samples, energies)
dimod_response = response.as_binary_response()
for s, t in zip(response, dimod_response):
self.assertEqual(s, t)
dimod_response = response.as_binary_response(data_copy=True)
for (__, dat), (__, dat0) in zip(response.samples(data=True),
dimod_response.samples(data=True)):
self.assertNotEqual(id(dat), id(dat0)) Example 27
def tangent_space(covmats, Cref):
"""Project a set of covariance matrices in the tangent space according to the given reference point Cref
:param covmats: Covariance matrices set, Ntrials X Nchannels X Nchannels
:param Cref: The reference covariance matrix
:returns: the Tangent space , a matrix of Ntrials X (Nchannels*(Nchannels+1)/2)
"""
Nt, Ne, Ne = covmats.shape
Cm12 = invsqrtm(Cref)
idx = numpy.triu_indices_from(Cref)
T = numpy.empty((Nt, Ne * (Ne + 1) / 2))
coeffs = (
numpy.sqrt(2) *
numpy.triu(
numpy.ones(
(Ne,
Ne)),
1) +
numpy.eye(Ne))[idx]
for index in range(Nt):
tmp = numpy.dot(numpy.dot(Cm12, covmats[index, :, :]), Cm12)
tmp = logm(tmp)
T[index, :] = numpy.multiply(coeffs, tmp[idx])
return T Example 28
def untangent_space(T, Cref):
"""Project a set of Tangent space vectors in the manifold according to the given reference point Cref
:param T: the Tangent space , a matrix of Ntrials X (Nchannels*(Nchannels+1)/2)
:param Cref: The reference covariance matrix
:returns: A set of Covariance matrix, Ntrials X Nchannels X Nchannels
"""
Nt, Nd = T.shape
Ne = int((numpy.sqrt(1 + 8 * Nd) - 1) / 2)
C12 = sqrtm(Cref)
idx = numpy.triu_indices_from(Cref)
covmats = numpy.empty((Nt, Ne, Ne))
covmats[:, idx[0], idx[1]] = T
for i in range(Nt):
covmats[i] = numpy.diag(numpy.diag(covmats[i])) + numpy.triu(
covmats[i], 1) / numpy.sqrt(2) + numpy.triu(covmats[i], 1).T / numpy.sqrt(2)
covmats[i] = expm(covmats[i])
covmats[i] = numpy.dot(numpy.dot(C12, covmats[i]), C12)
return covmats Example 29
def test_tril_triu_ndim3():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.array([
[[1, 1], [1, 1]],
[[1, 1], [1, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_tril_desired = np.array([
[[1, 0], [1, 1]],
[[1, 0], [1, 0]],
[[1, 0], [0, 0]],
], dtype=dtype)
a_triu_desired = np.array([
[[1, 1], [0, 1]],
[[1, 1], [0, 0]],
[[1, 1], [0, 0]],
], dtype=dtype)
a_triu_observed = np.triu(a)
a_tril_observed = np.tril(a)
yield assert_array_equal, a_triu_observed, a_triu_desired
yield assert_array_equal, a_tril_observed, a_tril_desired
yield assert_equal, a_triu_observed.dtype, a.dtype
yield assert_equal, a_tril_observed.dtype, a.dtype Example 30
def test_tril_triu_dtype():
# Issue 4916
# tril and triu should return the same dtype as input
for c in np.typecodes['All']:
if c == 'V':
continue
arr = np.zeros((3, 3), dtype=c)
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
# check special cases
arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'],
['2004-01-01T12:00', '2003-01-03T13:45']],
dtype='datetime64')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype)
arr = np.zeros((3,3), dtype='f4,f4')
assert_equal(np.triu(arr).dtype, arr.dtype)
assert_equal(np.tril(arr).dtype, arr.dtype) Example 31
def sqrtvc(m):
mup=m
mdown=mup.transpose()
mdown.setdiag(0)
mtogether=mup+mdown
sums_sq=np.sqrt(mtogether.sum(axis=1))
D_sq = sps.spdiags(1.0/sums_sq.flatten(), [0], mtogether.get_shape()[0], mtogether.get_shape()[1], format='csr')
return sps.triu(D_sq.dot(mtogether.dot(D_sq))) Example 32
def hichip_add_diagonal(m):
mup=m
mdown=mup.transpose()
mdown.setdiag(0)
mtogether=mup+mdown
sums=mtogether.sum(axis=1)
max_sum=np.max(sums)
to_add=1.0*max_sum-1.0*sums
to_add_values=[]
for i in range(m.shape[0]):
to_add_values.append(to_add[i,0])
mtogether.setdiag(np.array(to_add_values))
D = sps.spdiags(1.0/sums.flatten(), [0], mtogether.get_shape()[0], mtogether.get_shape()[1], format='csr')
return sps.triu(D.dot(mtogether)) Example 33
def coverage_norm(m):
mup=m
mdown=mup.transpose()
mdown.setdiag(0)
mtogether=mup+mdown
sums=mtogether.sum(axis=1)
D = sps.spdiags(1.0/sums.flatten(), [0], mtogether.get_shape()[0], mtogether.get_shape()[1], format='csr')
return sps.triu(D.dot(mtogether.dot(D)))
#assumes matrix is upper triangular Example 34
def array_2_coverageVector(m):
assert np.allclose(m, np.triu(m))
m_sym=m+m.T-m.diagonal()
return m_sym.sum(axis=0) Example 35
def subsample_to_depth_array_upperTri(m,seq_depth):
m=np.triu(m)
subsampled_data=np.zeros(m.shape)
depthm=m.sum()
assert seq_depth<=depthm
subsampling_prob=seq_depth/depthm
for i in range(m.shape[0]):
for j in range(m.shape[1]):
if j<=i:
continue
n=m[i,j]
subsampled_data[i,j]=np.random.binomial(n,subsampling_prob,1)[0]
return subsampled_data Example 36
def binarize_top(m,q):
threshold=mquantiles(np.triu(m).flatten(),q)
new_m=copy.deepcopy(m)
new_m[new_m<threshold]=0
new_m[new_m>=threshold]=1
return get_sqrtvc(new_m) Example 37
def compute_discount(gamma, maxlen):
c = numpy.ones((maxlen,)) * gamma
c[0] = 1.
c = c.cumprod()
C = numpy.triu(numpy.repeat(c[None, :], repeats=maxlen, axis=0))
C /= c[:, None]
return C Example 38
def get_attn_subsequent_mask(seq):
assert seq.dim() == 2
attn_shape = (seq.size(0), seq.size(1), seq.size(1))
subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
subsequent_mask = torch.from_numpy(subsequent_mask)
if seq.is_cuda:
subsequent_mask = subsequent_mask.cuda()
return subsequent_mask Example 39
def test_tril_triu_ndim2():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.ones((2, 2), dtype=dtype)
b = np.tril(a)
c = np.triu(a)
yield assert_array_equal, b, [[1, 0], [1, 1]]
yield assert_array_equal, c, b.T
# should return the same dtype as the original array
yield assert_equal, b.dtype, a.dtype
yield assert_equal, c.dtype, a.dtype Example 40
def test_tril_triu_with_inf():
# Issue 4859
arr = np.array([[1, 1, np.inf],
[1, 1, 1],
[np.inf, 1, 1]])
out_tril = np.array([[1, 0, 0],
[1, 1, 0],
[np.inf, 1, 1]])
out_triu = out_tril.T
assert_array_equal(np.triu(arr), out_triu)
assert_array_equal(np.tril(arr), out_tril) Example 41
def test_mask_indices():
# simple test without offset
iu = mask_indices(3, np.triu)
a = np.arange(9).reshape(3, 3)
yield (assert_array_equal, a[iu], array([0, 1, 2, 4, 5, 8]))
# Now with an offset
iu1 = mask_indices(3, np.triu, 1)
yield (assert_array_equal, a[iu1], array([1, 2, 5])) Example 42
def test_dynamic_programming_logic(self):
# Test for the dynamic programming part
# This test is directly taken from Cormen page 376.
arrays = [np.random.random((30, 35)),
np.random.random((35, 15)),
np.random.random((15, 5)),
np.random.random((5, 10)),
np.random.random((10, 20)),
np.random.random((20, 25))]
m_expected = np.array([[0., 15750., 7875., 9375., 11875., 15125.],
[0., 0., 2625., 4375., 7125., 10500.],
[0., 0., 0., 750., 2500., 5375.],
[0., 0., 0., 0., 1000., 3500.],
[0., 0., 0., 0., 0., 5000.],
[0., 0., 0., 0., 0., 0.]])
s_expected = np.array([[0, 1, 1, 3, 3, 3],
[0, 0, 2, 3, 3, 3],
[0, 0, 0, 3, 3, 3],
[0, 0, 0, 0, 4, 5],
[0, 0, 0, 0, 0, 5],
[0, 0, 0, 0, 0, 0]], dtype=np.int)
s_expected -= 1 # Cormen uses 1-based index, python does not.
s, m = _multi_dot_matrix_chain_order(arrays, return_costs=True)
# Only the upper triangular part (without the diagonal) is interesting.
assert_almost_equal(np.triu(s[:-1, 1:]),
np.triu(s_expected[:-1, 1:]))
assert_almost_equal(np.triu(m), np.triu(m_expected)) Example 43
def potential_numpy(cluster):
d = distances_numpy(cluster)
dtri = np.triu(d)
energy = lj_numpy(dtri[dtri > 1e-6]).sum()
return energy
#### END: numpy Example 44
def extract_test_vals(query, target, query_field, target_field, test_df, is_test_df_sym):
""" Extract values that has query in the columns and target in the rows.
Args:
query (string)
target (string)
query_field (string): name of multiindex level in which to find query
target_field (string): name of multiindex level in which to find target
test_df (pandas multi-index df)
is_test_df_sym (bool): only matters if query == target; set to True to
avoid double-counting in the case of a symmetric matrix
Returns:
vals (numpy array)
"""
assert query in test_df.columns.get_level_values(query_field), (
"query {} is not in the {} level of the columns of test_df.".format(
query, query_field))
assert target in test_df.index.get_level_values(target_field), (
"target {} is not in the {} level of the index of test_df.".format(
target, target_field))
# Extract elements where query is in columns and target is in rows
target_in_rows_query_in_cols_df = test_df.loc[
test_df.index.get_level_values(target_field) == target,
test_df.columns.get_level_values(query_field) == query]
# If query == target AND the matrix is symmetric, need to take only triu
# of the extracted values in order to avoid double-counting
if query == target and is_test_df_sym:
mask = np.triu(np.ones(target_in_rows_query_in_cols_df.shape), k=1).astype(np.bool)
vals_with_nans = target_in_rows_query_in_cols_df.where(mask).values.flatten()
vals = vals_with_nans[~np.isnan(vals_with_nans)]
else:
vals = target_in_rows_query_in_cols_df.values.flatten()
return vals Example 45
def get_attn_subsequent_mask(seq):
''' Get an attention mask to avoid using the subsequent info.'''
assert seq.dim() == 2
attn_shape = (seq.size(0), seq.size(1), seq.size(1))
subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
subsequent_mask = torch.from_numpy(subsequent_mask)
if seq.is_cuda:
subsequent_mask = subsequent_mask.cuda()
return subsequent_mask Example 46
def _update_covariance(self, it):
self.eigen_decomp_updated = it
self.cov[:, :] = np.triu(self.cov) + np.triu(self.cov, 1).T
D, B = np.linalg.eigh(self.cov)
# HACK: avoid numerical problems
D = np.maximum(D, np.finfo(np.float).eps)
D = np.diag(np.sqrt(1.0 / D))
self.invsqrtC = B.dot(D).dot(B.T) Example 47
def get_bias(length: int):
# matrix with lower triangle and main diagonal set to 0, upper triangle set to 1
upper_triangle = np.triu(np.ones((length, length)), k=1)
# (1, length, length)
bias = -99999999. * np.reshape(upper_triangle, (1, length, length))
return mx.nd.array(bias) Example 48
def test_tril_triu_ndim2():
for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
a = np.ones((2, 2), dtype=dtype)
b = np.tril(a)
c = np.triu(a)
yield assert_array_equal, b, [[1, 0], [1, 1]]
yield assert_array_equal, c, b.T
# should return the same dtype as the original array
yield assert_equal, b.dtype, a.dtype
yield assert_equal, c.dtype, a.dtype Example 49
def test_tril_triu_with_inf():
# Issue 4859
arr = np.array([[1, 1, np.inf],
[1, 1, 1],
[np.inf, 1, 1]])
out_tril = np.array([[1, 0, 0],
[1, 1, 0],
[np.inf, 1, 1]])
out_triu = out_tril.T
assert_array_equal(np.triu(arr), out_triu)
assert_array_equal(np.tril(arr), out_tril) Example 50
def test_mask_indices():
# simple test without offset
iu = mask_indices(3, np.triu)
a = np.arange(9).reshape(3, 3)
yield (assert_array_equal, a[iu], array([0, 1, 2, 4, 5, 8]))
# Now with an offset
iu1 = mask_indices(3, np.triu, 1)
yield (assert_array_equal, a[iu1], array([1, 2, 5])) 