def test_diags_vs_diag(self):
# Check that
#
# diags([a, b, ...], [i, j, ...]) == diag(a, i) + diag(b, j) + ...
#
np.random.seed(1234)
for n_diags in [1, 2, 3, 4, 5, 10]:
n = 1 + n_diags // 2 + np.random.randint(0, 10)
offsets = np.arange(-n + 1, n - 1)
np.random.shuffle(offsets)
offsets = offsets[:n_diags]
diagonals = [np.random.rand(n - abs(q)) for q in offsets]
mat = construct.diags(diagonals, offsets)
dense_mat = sum(
[np.diag(x, j) for x, j in zip(diagonals, offsets)])
assert_array_almost_equal_nulp(mat.todense(), dense_mat)
if len(offsets) == 1:
mat = construct.diags(diagonals[0], offsets[0])
dense_mat = np.diag(diagonals[0], offsets[0])
assert_array_almost_equal_nulp(mat.todense(), dense_mat)
def test_diags_vs_diag(self):
# Check that
#
# diags([a, b, ...], [i, j, ...]) == diag(a, i) + diag(b, j) + ...
#
np.random.seed(1234)
for n_diags in [1, 2, 3, 4, 5, 10]:
n = 1 + n_diags//2 + np.random.randint(0, 10)
offsets = np.arange(-n+1, n-1)
np.random.shuffle(offsets)
offsets = offsets[:n_diags]
diagonals = [np.random.rand(n - abs(q)) for q in offsets]
mat = construct.diags(diagonals, offsets)
dense_mat = sum([np.diag(x, j) for x, j in zip(diagonals, offsets)])
assert_array_almost_equal_nulp(mat.todense(), dense_mat)
if len(offsets) == 1:
mat = construct.diags(diagonals[0], offsets[0])
dense_mat = np.diag(diagonals[0], offsets[0])
assert_array_almost_equal_nulp(mat.todense(), dense_mat)
def test_diags(self):
a = array([1, 2, 3, 4, 5])
b = array([6, 7, 8, 9, 10])
c = array([11, 12, 13, 14, 15])
cases = []
cases.append((a[:1], 0, (1, 1), [[1]]))
cases.append(([a[:1]], [0], (1, 1), [[1]]))
cases.append(([a[:1]], [0], (2, 1), [[1],[0]]))
cases.append(([a[:1]], [0], (1, 2), [[1,0]]))
cases.append(([a[:1]], [1], (1, 2), [[0,1]]))
cases.append(([a[:2]], [0], (2, 2), [[1,0],[0,2]]))
cases.append(([a[:1]],[-1], (2, 2), [[0,0],[1,0]]))
cases.append(([a[:3]], [0], (3, 4), [[1,0,0,0],[0,2,0,0],[0,0,3,0]]))
cases.append(([a[:3]], [1], (3, 4), [[0,1,0,0],[0,0,2,0],[0,0,0,3]]))
cases.append(([a[:3]], [2], (3, 5), [[0,0,1,0,0],[0,0,0,2,0],[0,0,0,0,3]]))
cases.append(([a[:3],b[:1]], [0,2], (3, 3), [[1,0,6],[0,2,0],[0,0,3]]))
cases.append(([a[:2],b[:3]], [-1,0], (3, 4), [[6,0,0,0],[1,7,0,0],[0,2,8,0]]))
cases.append(([a[:4],b[:3]], [2,-3], (6, 6), [[0,0,1,0,0,0],
[0,0,0,2,0,0],
[0,0,0,0,3,0],
[6,0,0,0,0,4],
[0,7,0,0,0,0],
[0,0,8,0,0,0]]))
cases.append(([a[:4],b,c[:4]], [-1,0,1], (5, 5), [[6,11, 0, 0, 0],
[1, 7,12, 0, 0],
[0, 2, 8,13, 0],
[0, 0, 3, 9,14],
[0, 0, 0, 4,10]]))
cases.append(([a[:2],b[:3],c], [-4,2,-1], (6, 5), [[0, 0, 6, 0, 0],
[11, 0, 0, 7, 0],
[0,12, 0, 0, 8],
[0, 0,13, 0, 0],
[1, 0, 0,14, 0],
[0, 2, 0, 0,15]]))
# scalar case: broadcasting
cases.append(([1,-2,1], [1,0,-1], (3, 3), [[-2, 1, 0],
[1, -2, 1],
[0, 1, -2]]))
for d, o, shape, result in cases:
try:
assert_equal(construct.diags(d, o, shape=shape).todense(),
result)
if shape[0] == shape[1] and hasattr(d[0], '__len__'):
# should be able to find the shape automatically
assert_equal(construct.diags(d, o).todense(), result)
except:
print("%r %r %r" % (d, o, shape))
raise
def test_diags(self):
a = array([1, 2, 3, 4, 5])
b = array([6, 7, 8, 9, 10])
c = array([11, 12, 13, 14, 15])
cases = []
cases.append((a[:1], 0, (1, 1), [[1]]))
cases.append(([a[:1]], [0], (1, 1), [[1]]))
cases.append(([a[:1]], [0], (2, 1), [[1],[0]]))
cases.append(([a[:1]], [0], (1, 2), [[1,0]]))
cases.append(([a[:1]], [1], (1, 2), [[0,1]]))
cases.append(([a[:2]], [0], (2, 2), [[1,0],[0,2]]))
cases.append(([a[:1]],[-1], (2, 2), [[0,0],[1,0]]))
cases.append(([a[:3]], [0], (3, 4), [[1,0,0,0],[0,2,0,0],[0,0,3,0]]))
cases.append(([a[:3]], [1], (3, 4), [[0,1,0,0],[0,0,2,0],[0,0,0,3]]))
cases.append(([a[:3]], [2], (3, 5), [[0,0,1,0,0],[0,0,0,2,0],[0,0,0,0,3]]))
cases.append(([a[:3],b[:1]], [0,2], (3, 3), [[1,0,6],[0,2,0],[0,0,3]]))
cases.append(([a[:2],b[:3]], [-1,0], (3, 4), [[6,0,0,0],[1,7,0,0],[0,2,8,0]]))
cases.append(([a[:4],b[:3]], [2,-3], (6, 6), [[0,0,1,0,0,0],
[0,0,0,2,0,0],
[0,0,0,0,3,0],
[6,0,0,0,0,4],
[0,7,0,0,0,0],
[0,0,8,0,0,0]]))
cases.append(([a[:4],b,c[:4]], [-1,0,1], (5, 5), [[6,11, 0, 0, 0],
[1, 7,12, 0, 0],
[0, 2, 8,13, 0],
[0, 0, 3, 9,14],
[0, 0, 0, 4,10]]))
cases.append(([a[:2],b[:3],c], [-4,2,-1], (6, 5), [[0, 0, 6, 0, 0],
[11, 0, 0, 7, 0],
[0,12, 0, 0, 8],
[0, 0,13, 0, 0],
[1, 0, 0,14, 0],
[0, 2, 0, 0,15]]))
# scalar case: broadcasting
cases.append(([1,-2,1], [1,0,-1], (3, 3), [[-2, 1, 0],
[1, -2, 1],
[0, 1, -2]]))
for d, o, shape, result in cases:
try:
assert_equal(construct.diags(d, o, shape=shape).todense(),
result)
if shape[0] == shape[1] and hasattr(d[0], '__len__'):
# should be able to find the shape automatically
assert_equal(construct.diags(d, o).todense(), result)
except:
print("%r %r %r" % (d, o, shape))
raise
def is_reversible(T, mu=None, tol=1e-15):
r"""
checks whether T is reversible in terms of given stationary distribution.
If no distribution is given, it will be calculated out of T.
performs follwing check:
:math:`\pi_i P_{ij} = \pi_j P_{ji}
Parameters
----------
T : scipy.sparse matrix
Transition matrix
mu : numpy.ndarray vector
stationary distribution
tol : float
tolerance to check with
Returns
-------
Truth value : bool
True, if T is a stochastic matrix
False, otherwise
"""
if not is_transition_matrix(T, tol):
raise ValueError("given matrix is not a valid transition matrix.")
T = T.tocsr()
if mu is None:
from .decomposition import stationary_distribution
mu = stationary_distribution(T)
Mu = diags(mu, 0)
prod = Mu * T
return allclose_sparse(prod, prod.transpose(), rtol=tol)
def expected_counts_stationary(T, n, mu=None):
r"""Expected transition counts for Markov chain in equilibrium.
Since mu is stationary for T we have
.. math::
E(C^{(n)})=n diag(mu)*T.
Parameters
----------
T : (M, M) sparse matrix
Transition matrix.
n : int
Number of steps for chain.
mu : (M,) ndarray (optional)
Stationary distribution for T. If mu is not specified it will be
computed via diagonalization of T.
Returns
-------
EC : (M, M) sparse matrix
Expected value for transition counts after N steps.
"""
if (n <= 0):
EC = coo_matrix(T.shape, dtype=float)
return EC
else:
if mu is None:
mu = stationary_distribution(T)
D_mu = diags(mu, 0)
EC = n * D_mu.dot(T)
return EC
def getMatrix(n=10, isDiagDom=True):
'''
Return an nxn matrix which is the discretization matrix
from finite difference for a diffusion problem.
To visualize A: use plt.spy(A.toarray())
'''
# Representation of sparse matrix and right-hand side
assert n >= 2
n -= 1
diagonal = np.zeros(n+1)
lower = np.zeros(n)
upper = np.zeros(n)
# Precompute sparse matrix
if isDiagDom:
diagonal[:] = 2 + 1/n**2
else:
diagonal[:] = 2
lower[:] = -1 #1
upper[:] = -1 #1
# Insert boundary conditions
# diagonal[0] = 1
# upper[0] = 0
# diagonal[n] = 1
# lower[-1] = 0
A = diags(
diagonals=[diagonal, lower, upper],
offsets=[0, -1, 1], shape=(n+1, n+1),
format='csr')
return A
def is_rate_matrix(K, tol):
"""
True if K is a rate matrix
Parameters
----------
K : scipy.sparse matrix
Matrix to check
tol : float
tolerance to check with
Returns
-------
Truth value : bool
True, if K negated diagonal is positive and row sums up to zero.
False, otherwise
"""
K = K.tocsr()
# check rows sum up to zero.
row_sum = K.sum(axis=1)
sum_eq_zero = np.allclose(row_sum, np.zeros(shape=row_sum.shape), atol=tol)
# store copy of original diagonal
org_diag = K.diagonal()
# substract diagonal
K = K - diags(org_diag, 0)
# check off diagonals are > 0
values = K.data
values_gt_zero = np.allclose(values, np.abs(values), atol=tol)
# add diagonal
K = K + diags(org_diag, 0)
return values_gt_zero and sum_eq_zero
def expected_counts(p0, T, N):
r"""Compute expected transition counts for Markov chain after N steps.
Expected counts are computed according to ..math::
E[C_{ij}^{(n)}]=\sum_{k=0}^{N-1} (p_0^T T^{k})_{i} p_{ij}
Parameters
----------
p0 : (M,) ndarray
Starting (probability) vector of the chain.
T : (M, M) sparse matrix
Transition matrix of the chain.
N : int
Number of steps to take from initial state.
Returns
--------
EC : (M, M) sparse matrix
Expected value for transition counts after N steps.
"""
if (N <= 0):
EC = coo_matrix(T.shape, dtype=float)
return EC
else:
"""Probability vector after (k=0) propagations"""
p_k = 1.0 * p0
"""Sum of vectors after (k=0) propagations"""
p_sum = 1.0 * p_k
"""Transpose T to use sparse dot product"""
Tt = T.transpose()
for k in np.arange(N - 1):
"""Propagate one step p_{k} -> p_{k+1}"""
p_k = Tt.dot(p_k)
"""Update sum"""
p_sum += p_k
D_psum = diags(p_sum, 0)
EC = D_psum.dot(T)
return EC
def test_diags_dtype(self):
x = construct.diags([2.2], [0], shape=(2, 2), dtype=int)
assert_equal(x.dtype, int)
assert_equal(x.todense(), [[2, 0], [0, 2]])
def test_diags(self):
a = array([1, 2, 3, 4, 5])
b = array([6, 7, 8, 9, 10])
c = array([11, 12, 13, 14, 15])
cases = []
cases.append((a[:1], 0, (1, 1), [[1]]))
cases.append(([a[:1]], [0], (1, 1), [[1]]))
cases.append(([a[:1]], [0], (2, 1), [[1], [0]]))
cases.append(([a[:1]], [0], (1, 2), [[1, 0]]))
cases.append(([a[:1]], [1], (1, 2), [[0, 1]]))
cases.append(([a[:2]], [0], (2, 2), [[1, 0], [0, 2]]))
cases.append(([a[:1]], [-1], (2, 2), [[0, 0], [1, 0]]))
cases.append(([a[:3]], [0], (3, 4), [[1, 0, 0, 0], [0, 2, 0, 0],
[0, 0, 3, 0]]))
cases.append(([a[:3]], [1], (3, 4), [[0, 1, 0, 0], [0, 0, 2, 0],
[0, 0, 0, 3]]))
cases.append(([a[:1]], [-2], (3, 5), [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[1, 0, 0, 0, 0]]))
cases.append(([a[:2]], [-1], (3, 5), [[0, 0, 0, 0, 0], [1, 0, 0, 0, 0],
[0, 2, 0, 0, 0]]))
cases.append(([a[:3]], [0], (3, 5), [[1, 0, 0, 0, 0], [0, 2, 0, 0, 0],
[0, 0, 3, 0, 0]]))
cases.append(([a[:3]], [1], (3, 5), [[0, 1, 0, 0, 0], [0, 0, 2, 0, 0],
[0, 0, 0, 3, 0]]))
cases.append(([a[:3]], [2], (3, 5), [[0, 0, 1, 0, 0], [0, 0, 0, 2, 0],
[0, 0, 0, 0, 3]]))
cases.append(([a[:2]], [3], (3, 5), [[0, 0, 0, 1, 0], [0, 0, 0, 0, 2],
[0, 0, 0, 0, 0]]))
cases.append(([a[:1]], [4], (3, 5), [[0, 0, 0, 0, 1], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]))
cases.append(([a[:1]], [-4], (5, 3), [[0, 0, 0], [0, 0, 0], [0, 0, 0],
[0, 0, 0], [1, 0, 0]]))
cases.append(([a[:2]], [-3], (5, 3), [[0, 0, 0], [0, 0, 0], [0, 0, 0],
[1, 0, 0], [0, 2, 0]]))
cases.append(([a[:3]], [-2], (5, 3), [[0, 0, 0], [0, 0, 0], [1, 0, 0],
[0, 2, 0], [0, 0, 3]]))
cases.append(([a[:3]], [-1], (5, 3), [[0, 0, 0], [1, 0, 0], [0, 2, 0],
[0, 0, 3], [0, 0, 0]]))
cases.append(([a[:3]], [0], (5, 3), [[1, 0, 0], [0, 2, 0], [0, 0, 3],
[0, 0, 0], [0, 0, 0]]))
cases.append(([a[:2]], [1], (5, 3), [[0, 1, 0], [0, 0, 2], [0, 0, 0],
[0, 0, 0], [0, 0, 0]]))
cases.append(([a[:1]], [2], (5, 3), [[0, 0, 1], [0, 0, 0], [0, 0, 0],
[0, 0, 0], [0, 0, 0]]))
cases.append(([a[:3], b[:1]], [0, 2], (3, 3), [[1, 0, 6], [0, 2, 0],
[0, 0, 3]]))
cases.append(([a[:2], b[:3]], [-1, 0], (3, 4), [[6, 0, 0, 0],
[1, 7, 0, 0],
[0, 2, 8, 0]]))
cases.append(([a[:4], b[:3]], [2, -3], (6, 6), [[0, 0, 1, 0, 0, 0],
[0, 0, 0, 2, 0, 0],
[0, 0, 0, 0, 3, 0],
[6, 0, 0, 0, 0, 4],
[0, 7, 0, 0, 0, 0],
[0, 0, 8, 0, 0, 0]]))
cases.append(([a[:4], b, c[:4]], [-1, 0, 1], (5, 5), [[6, 11, 0, 0, 0],
[1, 7, 12, 0, 0],
[0, 2, 8, 13, 0],
[0, 0, 3, 9, 14],
[0, 0, 0, 4,
10]]))
cases.append(([a[:2], b[:3], c], [-4, 2,
-1], (6, 5), [[0, 0, 6, 0, 0],
[11, 0, 0, 7, 0],
[0, 12, 0, 0, 8],
[0, 0, 13, 0, 0],
[1, 0, 0, 14, 0],
[0, 2, 0, 0, 15]]))
# too long arrays are OK
cases.append(([a], [0], (1, 1), [[1]]))
cases.append(([a[:3], b], [0, 2], (3, 3), [[1, 0, 6], [0, 2, 0],
[0, 0, 3]]))
cases.append((np.array([[1, 2, 3],
[4, 5, 6]]), [0, -1], (3, 3), [[1, 0, 0],
[4, 2, 0],
[0, 5, 3]]))
# scalar case: broadcasting
cases.append(([1, -2, 1], [1, 0, -1], (3, 3), [[-2, 1, 0], [1, -2, 1],
[0, 1, -2]]))
for d, o, shape, result in cases:
err_msg = "%r %r %r %r" % (d, o, shape, result)
assert_equal(construct.diags(d, o, shape=shape).todense(),
result,
err_msg=err_msg)
if shape[0] == shape[1] and hasattr(
d[0], '__len__') and len(d[0]) <= max(shape):
# should be able to find the shape automatically
assert_equal(construct.diags(d, o).todense(),
result,
err_msg=err_msg)
def test_diags_empty(self):
x = construct.diags([])
assert_equal(x.shape, (0, 0))
def test_diags_one_diagonal(self):
d = list(range(5))
for k in range(-5, 6):
assert_equal(
construct.diags(d, k).toarray(),
construct.diags([d], [k]).toarray())
def test_diags_dtype(self):
x = construct.diags([2.2], [0], shape=(2, 2), dtype=int)
assert_equal(x.dtype, int)
assert_equal(x.todense(), [[2, 0], [0, 2]])
print "Predicting the probs"
t0 = time()
probs = clf.predict_proba(X_test)
duration = time() - t0
print
print "Predicting probs took %0.2fs." % duration
print
total_evi = neg_evi + pos_evi
evi_sorted = np.argsort(total_evi)
coef_diags = diags(clf.coef_[0], 0)
# Most negative
print
print "Most negative"
print
i = evi_sorted[0]
print total_evi[i], neg_evi[i], pos_evi[i], probs[i]
print test_corpus[i]
ind_evi = (X_test[i] * coef_diags).toarray()[0]
ind_evi_sorted = np.argsort(ind_evi)
print "Negative words"
for j in ind_evi_sorted:
if ind_evi[j] >= 0:
break
def test_diags_one_diagonal(self):
d = list(range(5))
for k in range(-5, 6):
assert_equal(construct.diags(d, k).toarray(),
construct.diags([d], [k]).toarray())
def test_diags_default(self):
a = array([1, 2, 3, 4, 5])
assert_equal(construct.diags(a).todense(), np.diag(a))
def test_diags_empty(self):
x = construct.diags([])
assert_equal(x.shape, (0, 0))
def test_diags_default(self):
a = array([1, 2, 3, 4, 5])
assert_equal(construct.diags(a).todense(), np.diag(a))
def test_diags(self):
a = array([1, 2, 3, 4, 5])
b = array([6, 7, 8, 9, 10])
c = array([11, 12, 13, 14, 15])
cases = []
cases.append((a[:1], 0, (1, 1), [[1]]))
cases.append(([a[:1]], [0], (1, 1), [[1]]))
cases.append(([a[:1]], [0], (2, 1), [[1],[0]]))
cases.append(([a[:1]], [0], (1, 2), [[1,0]]))
cases.append(([a[:1]], [1], (1, 2), [[0,1]]))
cases.append(([a[:2]], [0], (2, 2), [[1,0],[0,2]]))
cases.append(([a[:1]],[-1], (2, 2), [[0,0],[1,0]]))
cases.append(([a[:3]], [0], (3, 4), [[1,0,0,0],[0,2,0,0],[0,0,3,0]]))
cases.append(([a[:3]], [1], (3, 4), [[0,1,0,0],[0,0,2,0],[0,0,0,3]]))
cases.append(([a[:1]], [-2], (3, 5), [[0,0,0,0,0],[0,0,0,0,0],[1,0,0,0,0]]))
cases.append(([a[:2]], [-1], (3, 5), [[0,0,0,0,0],[1,0,0,0,0],[0,2,0,0,0]]))
cases.append(([a[:3]], [0], (3, 5), [[1,0,0,0,0],[0,2,0,0,0],[0,0,3,0,0]]))
cases.append(([a[:3]], [1], (3, 5), [[0,1,0,0,0],[0,0,2,0,0],[0,0,0,3,0]]))
cases.append(([a[:3]], [2], (3, 5), [[0,0,1,0,0],[0,0,0,2,0],[0,0,0,0,3]]))
cases.append(([a[:2]], [3], (3, 5), [[0,0,0,1,0],[0,0,0,0,2],[0,0,0,0,0]]))
cases.append(([a[:1]], [4], (3, 5), [[0,0,0,0,1],[0,0,0,0,0],[0,0,0,0,0]]))
cases.append(([a[:1]], [-4], (5, 3), [[0,0,0],[0,0,0],[0,0,0],[0,0,0],[1,0,0]]))
cases.append(([a[:2]], [-3], (5, 3), [[0,0,0],[0,0,0],[0,0,0],[1,0,0],[0,2,0]]))
cases.append(([a[:3]], [-2], (5, 3), [[0,0,0],[0,0,0],[1,0,0],[0,2,0],[0,0,3]]))
cases.append(([a[:3]], [-1], (5, 3), [[0,0,0],[1,0,0],[0,2,0],[0,0,3],[0,0,0]]))
cases.append(([a[:3]], [0], (5, 3), [[1,0,0],[0,2,0],[0,0,3],[0,0,0],[0,0,0]]))
cases.append(([a[:2]], [1], (5, 3), [[0,1,0],[0,0,2],[0,0,0],[0,0,0],[0,0,0]]))
cases.append(([a[:1]], [2], (5, 3), [[0,0,1],[0,0,0],[0,0,0],[0,0,0],[0,0,0]]))
cases.append(([a[:3],b[:1]], [0,2], (3, 3), [[1,0,6],[0,2,0],[0,0,3]]))
cases.append(([a[:2],b[:3]], [-1,0], (3, 4), [[6,0,0,0],[1,7,0,0],[0,2,8,0]]))
cases.append(([a[:4],b[:3]], [2,-3], (6, 6), [[0,0,1,0,0,0],
[0,0,0,2,0,0],
[0,0,0,0,3,0],
[6,0,0,0,0,4],
[0,7,0,0,0,0],
[0,0,8,0,0,0]]))
cases.append(([a[:4],b,c[:4]], [-1,0,1], (5, 5), [[6,11, 0, 0, 0],
[1, 7,12, 0, 0],
[0, 2, 8,13, 0],
[0, 0, 3, 9,14],
[0, 0, 0, 4,10]]))
cases.append(([a[:2],b[:3],c], [-4,2,-1], (6, 5), [[0, 0, 6, 0, 0],
[11, 0, 0, 7, 0],
[0,12, 0, 0, 8],
[0, 0,13, 0, 0],
[1, 0, 0,14, 0],
[0, 2, 0, 0,15]]))
# too long arrays are OK
cases.append(([a], [0], (1, 1), [[1]]))
cases.append(([a[:3],b], [0,2], (3, 3), [[1, 0, 6], [0, 2, 0], [0, 0, 3]]))
cases.append((np.array([[1, 2, 3], [4, 5, 6]]), [0,-1], (3, 3), [[1, 0, 0], [4, 2, 0], [0, 5, 3]]))
# scalar case: broadcasting
cases.append(([1,-2,1], [1,0,-1], (3, 3), [[-2, 1, 0],
[1, -2, 1],
[0, 1, -2]]))
for d, o, shape, result in cases:
err_msg = "%r %r %r %r" % (d, o, shape, result)
assert_equal(construct.diags(d, o, shape=shape).todense(),
result, err_msg=err_msg)
if shape[0] == shape[1] and hasattr(d[0], '__len__') and len(d[0]) <= max(shape):
# should be able to find the shape automatically
assert_equal(construct.diags(d, o).todense(), result,
err_msg=err_msg)
