def get_K(self, t):
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=PendingDeprecationWarning)
K = np.linalg.inv(np.matlib.eye(self.A.shape[0]) - t * self.A)
return np.array(np.log(K))
def get_K(self, t):
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=PendingDeprecationWarning)
K = _KernelR.mat_exp(t * self.A)
return np.array(np.log(K))
def make_legitimate_covariance_matrix(ndim=3, lo=2, hi=15) -> list:
from numpy import zeros, int16
from numpy.random import multivariate_normal, randint
from numpy import warnings
n = ndim
for _ in range(100000):
mx = zeros(shape=(n, n), dtype=int16)
for i in range(n):
for j in range(i, n):
mx[i, j:] = randint(lo, hi, size=len(mx[i, j:]))
cm = mx | mx.transpose()
with warnings.catch_warnings():
warnings.filterwarnings('error')
try:
mx = multivariate_normal(mean=[
0,
] * n, cov=cm, size=200)
print(cm)
return cm.tolist()
break
except RuntimeWarning:
continue
else:
from warnings import warn
warn("failed to find a covariance matrix. try again", Warning)
return None
def my_read_array(f):
with warnings.catch_warnings(): # prevent numpy from outputting empty array warning
warnings.simplefilter("ignore")
res = np.loadtxt(f)
if np.array_equal(np.round(res),res):
return res.astype(int)
else:
return res
def my_read_array(f):
with warnings.catch_warnings(
): # prevent numpy from outputting empty array warning
warnings.simplefilter("ignore")
res = np.loadtxt(f)
if np.array_equal(np.round(res), res):
return res.astype(int)
else:
return res
def get_K(self, t):
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=PendingDeprecationWarning)
K = _KernelR.mat_exp(-t * self.Ll, n=50)
if np.any(K < 0):
# logging.info(t, "K < 0")
return None
return np.array(np.log(K))
def get_K(self, alpha):
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=PendingDeprecationWarning)
K = np.linalg.inv(self.D - alpha * self.A)
if np.any(K < 0):
# logging.info(alpha, "K < 0")
return None
return np.array(np.log(K))
def get_K(self, beta):
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=PendingDeprecationWarning)
K = np.linalg.inv(np.matlib.eye(self.A.shape[0]) + beta * self.L)
if np.any(K < 0):
# logging.info(beta, "K < 0")
return None
return np.array(np.log(K))
def make_multidim_blobs(n_blobs=3,
n_points=100,
n_dim=3,
range=100,
relative_dispersion=10):
import builtins
from numpy import zeros, float16, warnings, diag, abs, uint8, argsort
from numpy.random import randint, multivariate_normal
m = n_points // n_blobs
working_range = 100
σ2 = (working_range / (n_blobs + 1)**(1 / n_dim) / relative_dispersion)**2
σ2 = int(σ2 * 0.5), int(σ2 * 1.5)
X = zeros(shape=(m * n_blobs, n_dim), dtype=float16)
y = zeros(shape=X.shape[0], dtype=uint8)
with warnings.catch_warnings():
warnings.filterwarnings("error")
for i in builtins.range(n_blobs):
while True:
diagonal = randint(*σ2, size=n_dim)
mx = diag(diagonal)
[
mx.__setitem__([i, slice(0, i, None)],
randint(-1, 1, size=n_dim)[:i])
for i in builtins.range(n_dim)
]
Σ = mx | mx.T
μ = randint(0, working_range, size=n_dim)
try:
mx = multivariate_normal(mean=μ, cov=Σ, size=m)
break
except RuntimeWarning:
continue
X[i * m:i * m + m, :] = mx
y[i * m:i * m + m] = i
#the last touches
X += abs(X.min())
X *= range / X.max()
nx = argsort(X[:, 0])
X = X[nx]
y = y[nx]
return X, y
def _update_actor(self):
number_of_points = self._vtk_points.GetNumberOfPoints()
cells = hstack((ones((number_of_points, 1), dtype=int64),
arange(number_of_points).reshape(-1, 1)))
cells = ascontiguousarray(cells, dtype=int64)
with warnings.catch_warnings(): #see issue #8
warnings.simplefilter("ignore", FutureWarning)
cell_array = numpy_support.numpy_to_vtk(
num_array=cells, deep=True, array_type=VTK_ID_TYPE)
vtk_cells = vtkCellArray()
vtk_cells.SetCells(number_of_points, cell_array)
vtk_poly = vtkPolyData()
vtk_poly.SetPoints(self._vtk_points)
vtk_poly.SetVerts(vtk_cells)
vtk_mapper = vtkPolyDataMapper()
vtk_mapper.SetInputData(vtk_poly)
self.actor.SetMapper(vtk_mapper)