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227,100 | rflamary/POT | ot/bregman.py | empirical_sinkhorn_divergence | def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
'''
Compute the sinkhorn divergence loss from empirical data
The function solves the following optimization problems and return the
sinkhorn diverg... | python | def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
'''
Compute the sinkhorn divergence loss from empirical data
The function solves the following optimization problems and return the
sinkhorn diverg... | [
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The function solves the following optimization problems and return the
sinkhorn divergence :math:`S`:
.. math::
W &= \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma)
W_a &= \min_{\gamma_a} <\gamma_a,M_a>_F + reg\cdot\Omega(\gamma_... | [
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227,101 | rflamary/POT | ot/lp/__init__.py | emd | def emd(a, b, M, numItermax=100000, log=False):
"""Solves the Earth Movers distance problem and returns the OT matrix
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F
s.t. \gamma 1 = a
\gamma^T 1= b
\gamma\geq 0
where :
- M is the metric cost matrix
- a an... | python | def emd(a, b, M, numItermax=100000, log=False):
"""Solves the Earth Movers distance problem and returns the OT matrix
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F
s.t. \gamma 1 = a
\gamma^T 1= b
\gamma\geq 0
where :
- M is the metric cost matrix
- a an... | [
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.. math::
\gamma = arg\min_\gamma <\gamma,M>_F
s.t. \gamma 1 = a
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\gamma\geq 0
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227,102 | rflamary/POT | ot/lp/__init__.py | emd2 | def emd2(a, b, M, processes=multiprocessing.cpu_count(),
numItermax=100000, log=False, return_matrix=False):
"""Solves the Earth Movers distance problem and returns the loss
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F
s.t. \gamma 1 = a
\gamma^T 1= b
\gamma... | python | def emd2(a, b, M, processes=multiprocessing.cpu_count(),
numItermax=100000, log=False, return_matrix=False):
"""Solves the Earth Movers distance problem and returns the loss
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F
s.t. \gamma 1 = a
\gamma^T 1= b
\gamma... | [
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.. math::
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s.t. \gamma 1 = a
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\gamma\geq 0
where :
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227,103 | rflamary/POT | ot/da.py | sinkhorn_l1l2_gl | def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
numInnerItermax=200, stopInnerThr=1e-9, verbose=False,
log=False):
"""
Solve the entropic regularization optimal transport problem with group
lasso regularization
The function solves the follo... | python | def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
numInnerItermax=200, stopInnerThr=1e-9, verbose=False,
log=False):
"""
Solve the entropic regularization optimal transport problem with group
lasso regularization
The function solves the follo... | [
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\gam... | [
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227,104 | rflamary/POT | ot/da.py | OT_mapping_linear | def OT_mapping_linear(xs, xt, reg=1e-6, ws=None,
wt=None, bias=True, log=False):
""" return OT linear operator between samples
The function estimates the optimal linear operator that aligns the two
empirical distributions. This is equivalent to estimating the closed
form mapping b... | python | def OT_mapping_linear(xs, xt, reg=1e-6, ws=None,
wt=None, bias=True, log=False):
""" return OT linear operator between samples
The function estimates the optimal linear operator that aligns the two
empirical distributions. This is equivalent to estimating the closed
form mapping b... | [
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The function estimates the optimal linear operator that aligns the two
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227,105 | rflamary/POT | ot/gpu/utils.py | euclidean_distances | def euclidean_distances(a, b, squared=False, to_numpy=True):
"""
Compute the pairwise euclidean distance between matrices a and b.
If the input matrix are in numpy format, they will be uploaded to the
GPU first which can incur significant time overhead.
Parameters
----------
a : np.ndarray... | python | def euclidean_distances(a, b, squared=False, to_numpy=True):
"""
Compute the pairwise euclidean distance between matrices a and b.
If the input matrix are in numpy format, they will be uploaded to the
GPU first which can incur significant time overhead.
Parameters
----------
a : np.ndarray... | [
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227,106 | rflamary/POT | ot/gpu/utils.py | dist | def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True):
"""Compute distance between samples in x1 and x2 on gpu
Parameters
----------
x1 : np.array (n1,d)
matrix with n1 samples of size d
x2 : np.array (n2,d), optional
matrix with n2 samples of size d (if None then x2=x1)
m... | python | def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True):
"""Compute distance between samples in x1 and x2 on gpu
Parameters
----------
x1 : np.array (n1,d)
matrix with n1 samples of size d
x2 : np.array (n2,d), optional
matrix with n2 samples of size d (if None then x2=x1)
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227,107 | rflamary/POT | ot/gpu/utils.py | to_gpu | def to_gpu(*args):
""" Upload numpy arrays to GPU and return them"""
if len(args) > 1:
return (cp.asarray(x) for x in args)
else:
return cp.asarray(args[0]) | python | def to_gpu(*args):
""" Upload numpy arrays to GPU and return them"""
if len(args) > 1:
return (cp.asarray(x) for x in args)
else:
return cp.asarray(args[0]) | [
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227,108 | rflamary/POT | ot/gpu/utils.py | to_np | def to_np(*args):
""" convert GPU arras to numpy and return them"""
if len(args) > 1:
return (cp.asnumpy(x) for x in args)
else:
return cp.asnumpy(args[0]) | python | def to_np(*args):
""" convert GPU arras to numpy and return them"""
if len(args) > 1:
return (cp.asnumpy(x) for x in args)
else:
return cp.asnumpy(args[0]) | [
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227,109 | rflamary/POT | ot/lp/cvx.py | scipy_sparse_to_spmatrix | def scipy_sparse_to_spmatrix(A):
"""Efficient conversion from scipy sparse matrix to cvxopt sparse matrix"""
coo = A.tocoo()
SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)
return SP | python | def scipy_sparse_to_spmatrix(A):
"""Efficient conversion from scipy sparse matrix to cvxopt sparse matrix"""
coo = A.tocoo()
SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)
return SP | [
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227,110 | rflamary/POT | ot/optim.py | line_search_armijo | def line_search_armijo(f, xk, pk, gfk, old_fval,
args=(), c1=1e-4, alpha0=0.99):
"""
Armijo linesearch function that works with matrices
find an approximate minimum of f(xk+alpha*pk) that satifies the
armijo conditions.
Parameters
----------
f : function
los... | python | def line_search_armijo(f, xk, pk, gfk, old_fval,
args=(), c1=1e-4, alpha0=0.99):
"""
Armijo linesearch function that works with matrices
find an approximate minimum of f(xk+alpha*pk) that satifies the
armijo conditions.
Parameters
----------
f : function
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227,111 | rflamary/POT | ot/optim.py | cg | def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
stopThr=1e-9, verbose=False, log=False):
"""
Solve the general regularized OT problem with conditional gradient
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + reg*f(\gamma)... | python | def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
stopThr=1e-9, verbose=False, log=False):
"""
Solve the general regularized OT problem with conditional gradient
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + reg*f(\gamma)... | [
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227,112 | rflamary/POT | ot/optim.py | gcg | def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10,
numInnerItermax=200, stopThr=1e-9, verbose=False, log=False):
"""
Solve the general regularized OT problem with the generalized conditional gradient
The function solves the following optimization problem:
.. math::
\gamma ... | python | def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10,
numInnerItermax=200, stopThr=1e-9, verbose=False, log=False):
"""
Solve the general regularized OT problem with the generalized conditional gradient
The function solves the following optimization problem:
.. math::
\gamma ... | [
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227,113 | rflamary/POT | ot/smooth.py | projection_simplex | def projection_simplex(V, z=1, axis=None):
""" Projection of x onto the simplex, scaled by z
P(x; z) = argmin_{y >= 0, sum(y) = z} ||y - x||^2
z: float or array
If array, len(z) must be compatible with V
axis: None or int
- axis=None: project V by P(V.ravel(); z)
- axis=1: p... | python | def projection_simplex(V, z=1, axis=None):
""" Projection of x onto the simplex, scaled by z
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227,114 | rflamary/POT | ot/smooth.py | dual_obj_grad | def dual_obj_grad(alpha, beta, a, b, C, regul):
"""
Compute objective value and gradients of dual objective.
Parameters
----------
alpha: array, shape = len(a)
beta: array, shape = len(b)
Current iterate of dual potentials.
a: array, shape = len(a)
b: array, shape = len(b)
... | python | def dual_obj_grad(alpha, beta, a, b, C, regul):
"""
Compute objective value and gradients of dual objective.
Parameters
----------
alpha: array, shape = len(a)
beta: array, shape = len(b)
Current iterate of dual potentials.
a: array, shape = len(a)
b: array, shape = len(b)
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227,115 | rflamary/POT | ot/smooth.py | solve_dual | def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500,
verbose=False):
"""
Solve the "smoothed" dual objective.
Parameters
----------
a: array, shape = len(a)
b: array, shape = len(b)
Input histograms (should be non-negative and sum to 1).
C: array,... | python | def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500,
verbose=False):
"""
Solve the "smoothed" dual objective.
Parameters
----------
a: array, shape = len(a)
b: array, shape = len(b)
Input histograms (should be non-negative and sum to 1).
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227,116 | rflamary/POT | ot/smooth.py | semi_dual_obj_grad | def semi_dual_obj_grad(alpha, a, b, C, regul):
"""
Compute objective value and gradient of semi-dual objective.
Parameters
----------
alpha: array, shape = len(a)
Current iterate of semi-dual potentials.
a: array, shape = len(a)
b: array, shape = len(b)
Input histograms (sho... | python | def semi_dual_obj_grad(alpha, a, b, C, regul):
"""
Compute objective value and gradient of semi-dual objective.
Parameters
----------
alpha: array, shape = len(a)
Current iterate of semi-dual potentials.
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b: array, shape = len(b)
Input histograms (sho... | [
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227,117 | rflamary/POT | ot/smooth.py | solve_semi_dual | def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500,
verbose=False):
"""
Solve the "smoothed" semi-dual objective.
Parameters
----------
a: array, shape = len(a)
b: array, shape = len(b)
Input histograms (should be non-negative and sum to 1)... | python | def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500,
verbose=False):
"""
Solve the "smoothed" semi-dual objective.
Parameters
----------
a: array, shape = len(a)
b: array, shape = len(b)
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227,118 | rflamary/POT | ot/smooth.py | get_plan_from_dual | def get_plan_from_dual(alpha, beta, C, regul):
"""
Retrieve optimal transportation plan from optimal dual potentials.
Parameters
----------
alpha: array, shape = len(a)
beta: array, shape = len(b)
Optimal dual potentials.
C: array, shape = len(a) x len(b)
Ground cost matrix.... | python | def get_plan_from_dual(alpha, beta, C, regul):
"""
Retrieve optimal transportation plan from optimal dual potentials.
Parameters
----------
alpha: array, shape = len(a)
beta: array, shape = len(b)
Optimal dual potentials.
C: array, shape = len(a) x len(b)
Ground cost matrix.... | [
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227,119 | rflamary/POT | ot/smooth.py | get_plan_from_semi_dual | def get_plan_from_semi_dual(alpha, b, C, regul):
"""
Retrieve optimal transportation plan from optimal semi-dual potentials.
Parameters
----------
alpha: array, shape = len(a)
Optimal semi-dual potentials.
b: array, shape = len(b)
Second input histogram (should be non-negative a... | python | def get_plan_from_semi_dual(alpha, b, C, regul):
"""
Retrieve optimal transportation plan from optimal semi-dual potentials.
Parameters
----------
alpha: array, shape = len(a)
Optimal semi-dual potentials.
b: array, shape = len(b)
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227,120 | rflamary/POT | ot/smooth.py | smooth_ot_dual | def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9,
numItermax=500, verbose=False, log=False):
r"""
Solve the regularized OT problem in the dual and return the OT matrix
The function solves the smooth relaxed dual formulation (7) in [17]_ :
.. math::
... | python | def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9,
numItermax=500, verbose=False, log=False):
r"""
Solve the regularized OT problem in the dual and return the OT matrix
The function solves the smooth relaxed dual formulation (7) in [17]_ :
.. math::
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227,121 | rflamary/POT | ot/smooth.py | smooth_ot_semi_dual | def smooth_ot_semi_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9,
numItermax=500, verbose=False, log=False):
r"""
Solve the regularized OT problem in the semi-dual and return the OT matrix
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... | python | def smooth_ot_semi_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9,
numItermax=500, verbose=False, log=False):
r"""
Solve the regularized OT problem in the semi-dual and return the OT matrix
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227,122 | rflamary/POT | ot/utils.py | kernel | def kernel(x1, x2, method='gaussian', sigma=1, **kwargs):
"""Compute kernel matrix"""
if method.lower() in ['gaussian', 'gauss', 'rbf']:
K = np.exp(-dist(x1, x2) / (2 * sigma**2))
return K | python | def kernel(x1, x2, method='gaussian', sigma=1, **kwargs):
"""Compute kernel matrix"""
if method.lower() in ['gaussian', 'gauss', 'rbf']:
K = np.exp(-dist(x1, x2) / (2 * sigma**2))
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227,123 | rflamary/POT | ot/utils.py | clean_zeros | def clean_zeros(a, b, M):
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"""
M2 = M[a > 0, :][:, b > 0].copy() # copy force c style matrix (froemd)
a2 = a[a > 0]
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""" Remove all components with zeros weights in a and b
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M2 = M[a > 0, :][:, b > 0].copy() # copy force c style matrix (froemd)
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227,124 | rflamary/POT | ot/utils.py | dist | def dist(x1, x2=None, metric='sqeuclidean'):
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Parameters
----------
x1 : np.array (n1,d)
matrix with n1 samples of size d
x2 : np.array (n2,d), optional
matrix with n2 samples of size d (if ... | python | def dist(x1, x2=None, metric='sqeuclidean'):
"""Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist
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----------
x1 : np.array (n1,d)
matrix with n1 samples of size d
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227,125 | rflamary/POT | ot/utils.py | cost_normalization | def cost_normalization(C, norm=None):
""" Apply normalization to the loss matrix
Parameters
----------
C : np.array (n1, n2)
The cost matrix to normalize.
norm : str
type of normalization from 'median','max','log','loglog'. Any other
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... | python | def cost_normalization(C, norm=None):
""" Apply normalization to the loss matrix
Parameters
----------
C : np.array (n1, n2)
The cost matrix to normalize.
norm : str
type of normalization from 'median','max','log','loglog'. Any other
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227,126 | rflamary/POT | ot/utils.py | parmap | def parmap(f, X, nprocs=multiprocessing.cpu_count()):
""" paralell map for multiprocessing """
q_in = multiprocessing.Queue(1)
q_out = multiprocessing.Queue()
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227,127 | rflamary/POT | ot/utils.py | _is_deprecated | def _is_deprecated(func):
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227,128 | rflamary/POT | ot/utils.py | deprecated._decorate_fun | def _decorate_fun(self, fun):
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227,129 | rflamary/POT | ot/dr.py | split_classes | def split_classes(X, y):
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"""split samples in X by classes in y
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227,130 | rflamary/POT | ot/dr.py | fda | def fda(X, y, p=2, reg=1e-16):
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Parameters
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X : numpy.ndarray (n,d)
Training samples
y : np.ndarray (n,)
labels for training samples
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size of dimensionnality reduction
reg : float, optional
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"""
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----------
X : numpy.ndarray (n,d)
Training samples
y : np.ndarray (n,)
labels for training samples
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size of dimensionnality reduction
reg : float, optional
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227,131 | rflamary/POT | ot/stochastic.py | sag_entropic_transport | def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None):
'''
Compute the SAG algorithm to solve the regularized discrete measures
optimal transport max problem
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\... | python | def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None):
'''
Compute the SAG algorithm to solve the regularized discrete measures
optimal transport max problem
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.. math::
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227,132 | rflamary/POT | ot/stochastic.py | averaged_sgd_entropic_transport | def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None):
'''
Compute the ASGD algorithm to solve the regularized semi continous measures optimal transport max problem
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + ... | python | def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None):
'''
Compute the ASGD algorithm to solve the regularized semi continous measures optimal transport max problem
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + ... | [
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227,133 | rflamary/POT | ot/stochastic.py | c_transform_entropic | def c_transform_entropic(b, M, reg, beta):
'''
The goal is to recover u from the c-transform.
The function computes the c_transform of a dual variable from the other
dual variable:
.. math::
u = v^{c,reg} = -reg \sum_j exp((v - M)/reg) b_j
Where :
- M is the (ns,nt) metric cost m... | python | def c_transform_entropic(b, M, reg, beta):
'''
The goal is to recover u from the c-transform.
The function computes the c_transform of a dual variable from the other
dual variable:
.. math::
u = v^{c,reg} = -reg \sum_j exp((v - M)/reg) b_j
Where :
- M is the (ns,nt) metric cost m... | [
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227,134 | rflamary/POT | ot/stochastic.py | solve_semi_dual_entropic | def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None,
log=False):
'''
Compute the transportation matrix to solve the regularized discrete
measures optimal transport max problem
The function solves the following optimization problem:
.. ma... | python | def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None,
log=False):
'''
Compute the transportation matrix to solve the regularized discrete
measures optimal transport max problem
The function solves the following optimization problem:
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227,135 | rflamary/POT | ot/stochastic.py | batch_grad_dual | def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha,
batch_beta):
'''
Computes the partial gradient of the dual optimal transport problem.
For each (i,j) in a batch of coordinates, the partial gradients are :
.. math::
\partial_{u_i} F = u_i * b_s/l_{v} -... | python | def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha,
batch_beta):
'''
Computes the partial gradient of the dual optimal transport problem.
For each (i,j) in a batch of coordinates, the partial gradients are :
.. math::
\partial_{u_i} F = u_i * b_s/l_{v} -... | [
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227,136 | rflamary/POT | ot/stochastic.py | sgd_entropic_regularization | def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr):
'''
Compute the sgd algorithm to solve the regularized discrete measures
optimal transport dual problem
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + re... | python | def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr):
'''
Compute the sgd algorithm to solve the regularized discrete measures
optimal transport dual problem
The function solves the following optimization problem:
.. math::
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227,137 | rflamary/POT | ot/stochastic.py | solve_dual_entropic | def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1,
log=False):
'''
Compute the transportation matrix to solve the regularized discrete measures
optimal transport dual problem
The function solves the following optimization problem:
.. math::
... | python | def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1,
log=False):
'''
Compute the transportation matrix to solve the regularized discrete measures
optimal transport dual problem
The function solves the following optimization problem:
.. math::
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227,138 | PyCQA/pyflakes | pyflakes/reporter.py | Reporter.flake | def flake(self, message):
"""
pyflakes found something wrong with the code.
@param: A L{pyflakes.messages.Message}.
"""
self._stdout.write(str(message))
self._stdout.write('\n') | python | def flake(self, message):
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pyflakes found something wrong with the code.
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227,139 | PyCQA/pyflakes | pyflakes/checker.py | counter | def counter(items):
"""
Simplest required implementation of collections.Counter. Required as 2.6
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for item in items:
results[item] = results.get(item, 0) + 1
return results | python | def counter(items):
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Simplest required implementation of collections.Counter. Required as 2.6
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227,140 | PyCQA/pyflakes | pyflakes/checker.py | Importation.source_statement | def source_statement(self):
"""Generate a source statement equivalent to the import."""
if self._has_alias():
return 'import %s as %s' % (self.fullName, self.name)
else:
return 'import %s' % self.fullName | python | def source_statement(self):
"""Generate a source statement equivalent to the import."""
if self._has_alias():
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227,143 | PyCQA/pyflakes | pyflakes/api.py | _exitOnSignal | def _exitOnSignal(sigName, message):
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227,144 | jazzband/django-model-utils | model_utils/managers.py | InheritanceQuerySetMixin._get_subclasses_recurse | def _get_subclasses_recurse(self, model, levels=None):
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"""
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227,145 | jazzband/django-model-utils | model_utils/managers.py | InheritanceQuerySetMixin._get_ancestors_path | def _get_ancestors_path(self, model, levels=None):
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227,146 | jazzband/django-model-utils | model_utils/managers.py | SoftDeletableManagerMixin.get_queryset | def get_queryset(self):
"""
Return queryset limited to not removed entries.
"""
kwargs = {'model': self.model, 'using': self._db}
if hasattr(self, '_hints'):
kwargs['hints'] = self._hints
return self._queryset_class(**kwargs).filter(is_removed=False) | python | def get_queryset(self):
"""
Return queryset limited to not removed entries.
"""
kwargs = {'model': self.model, 'using': self._db}
if hasattr(self, '_hints'):
kwargs['hints'] = self._hints
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227,147 | jazzband/django-model-utils | model_utils/tracker.py | FieldInstanceTracker.previous | def previous(self, field):
"""Returns currently saved value of given field"""
# handle deferred fields that have not yet been loaded from the database
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# if the field has not been assigned locall... | python | def previous(self, field):
"""Returns currently saved value of given field"""
# handle deferred fields that have not yet been loaded from the database
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227,148 | jazzband/django-model-utils | model_utils/tracker.py | FieldTracker.get_field_map | def get_field_map(self, cls):
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field_map.update(**dict((k, v) for (k, v) in all_fields.items()
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"""Returns dict mapping fields names to model attribute names"""
field_map = dict((field, field) for field in self.fields)
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227,149 | jazzband/django-model-utils | model_utils/models.py | add_status_query_managers | def add_status_query_managers(sender, **kwargs):
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return
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227,150 | jazzband/django-model-utils | model_utils/models.py | add_timeframed_query_manager | def add_timeframed_query_manager(sender, **kwargs):
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"""
Add a QueryManager for a specific timeframe.
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return
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227,151 | invoice-x/invoice2data | src/invoice2data/input/tesseract4.py | to_text | def to_text(path, language='fra'):
"""Wraps Tesseract 4 OCR with custom language model.
Parameters
----------
path : str
path of electronic invoice in JPG or PNG format
Returns
-------
extracted_str : str
returns extracted text from image in JPG or PNG format
"""
i... | python | def to_text(path, language='fra'):
"""Wraps Tesseract 4 OCR with custom language model.
Parameters
----------
path : str
path of electronic invoice in JPG or PNG format
Returns
-------
extracted_str : str
returns extracted text from image in JPG or PNG format
"""
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227,152 | invoice-x/invoice2data | src/invoice2data/input/gvision.py | to_text | def to_text(path, bucket_name='cloud-vision-84893', language='fr'):
"""Sends PDF files to Google Cloud Vision for OCR.
Before using invoice2data, make sure you have the auth json path set as
env var GOOGLE_APPLICATION_CREDENTIALS
Parameters
----------
path : str
path of electronic invo... | python | def to_text(path, bucket_name='cloud-vision-84893', language='fr'):
"""Sends PDF files to Google Cloud Vision for OCR.
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227,153 | invoice-x/invoice2data | src/invoice2data/output/to_csv.py | write_to_file | def write_to_file(data, path):
"""Export extracted fields to csv
Appends .csv to path if missing and generates csv file in specified directory, if not then in root
Parameters
----------
data : dict
Dictionary of extracted fields
path : str
directory to save generated csv file
... | python | def write_to_file(data, path):
"""Export extracted fields to csv
Appends .csv to path if missing and generates csv file in specified directory, if not then in root
Parameters
----------
data : dict
Dictionary of extracted fields
path : str
directory to save generated csv file
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227,154 | invoice-x/invoice2data | src/invoice2data/input/tesseract.py | to_text | def to_text(path):
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Parameters
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path : str
path of electronic invoice in JPG or PNG format
Returns
-------
extracted_str : str
returns extracted text from image in JPG or PNG format
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Parameters
----------
path : str
path of electronic invoice in JPG or PNG format
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extracted_str : str
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227,155 | invoice-x/invoice2data | src/invoice2data/extract/plugins/tables.py | extract | def extract(self, content, output):
"""Try to extract tables from an invoice"""
for table in self['tables']:
# First apply default options.
plugin_settings = DEFAULT_OPTIONS.copy()
plugin_settings.update(table)
table = plugin_settings
# Validate settings
assert... | python | def extract(self, content, output):
"""Try to extract tables from an invoice"""
for table in self['tables']:
# First apply default options.
plugin_settings = DEFAULT_OPTIONS.copy()
plugin_settings.update(table)
table = plugin_settings
# Validate settings
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227,156 | invoice-x/invoice2data | src/invoice2data/input/pdftotext.py | to_text | def to_text(path):
"""Wrapper around Poppler pdftotext.
Parameters
----------
path : str
path of electronic invoice in PDF
Returns
-------
out : str
returns extracted text from pdf
Raises
------
EnvironmentError:
If pdftotext library is not found
""... | python | def to_text(path):
"""Wrapper around Poppler pdftotext.
Parameters
----------
path : str
path of electronic invoice in PDF
Returns
-------
out : str
returns extracted text from pdf
Raises
------
EnvironmentError:
If pdftotext library is not found
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227,157 | invoice-x/invoice2data | src/invoice2data/extract/invoice_template.py | InvoiceTemplate.prepare_input | def prepare_input(self, extracted_str):
"""
Input raw string and do transformations, as set in template file.
"""
# Remove withspace
if self.options['remove_whitespace']:
optimized_str = re.sub(' +', '', extracted_str)
else:
optimized_str = extrac... | python | def prepare_input(self, extracted_str):
"""
Input raw string and do transformations, as set in template file.
"""
# Remove withspace
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227,158 | invoice-x/invoice2data | src/invoice2data/extract/invoice_template.py | InvoiceTemplate.matches_input | def matches_input(self, optimized_str):
"""See if string matches keywords set in template file"""
if all([keyword in optimized_str for keyword in self['keywords']]):
logger.debug('Matched template %s', self['template_name'])
return True | python | def matches_input(self, optimized_str):
"""See if string matches keywords set in template file"""
if all([keyword in optimized_str for keyword in self['keywords']]):
logger.debug('Matched template %s', self['template_name'])
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227,159 | invoice-x/invoice2data | src/invoice2data/extract/invoice_template.py | InvoiceTemplate.parse_date | def parse_date(self, value):
"""Parses date and returns date after parsing"""
res = dateparser.parse(
value, date_formats=self.options['date_formats'], languages=self.options['languages']
)
logger.debug("result of date parsing=%s", res)
return res | python | def parse_date(self, value):
"""Parses date and returns date after parsing"""
res = dateparser.parse(
value, date_formats=self.options['date_formats'], languages=self.options['languages']
)
logger.debug("result of date parsing=%s", res)
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227,160 | invoice-x/invoice2data | src/invoice2data/output/to_json.py | write_to_file | def write_to_file(data, path):
"""Export extracted fields to json
Appends .json to path if missing and generates json file in specified directory, if not then in root
Parameters
----------
data : dict
Dictionary of extracted fields
path : str
directory to save generated json fi... | python | def write_to_file(data, path):
"""Export extracted fields to json
Appends .json to path if missing and generates json file in specified directory, if not then in root
Parameters
----------
data : dict
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path : str
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227,161 | invoice-x/invoice2data | src/invoice2data/main.py | create_parser | def create_parser():
"""Returns argument parser """
parser = argparse.ArgumentParser(
description='Extract structured data from PDF files and save to CSV or JSON.'
)
parser.add_argument(
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choices=input_mapping.keys(),
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"""Returns argument parser """
parser = argparse.ArgumentParser(
description='Extract structured data from PDF files and save to CSV or JSON.'
)
parser.add_argument(
'--input-reader',
choices=input_mapping.keys(),
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227,162 | invoice-x/invoice2data | src/invoice2data/main.py | main | def main(args=None):
"""Take folder or single file and analyze each."""
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logging.basicConfig(level=logging.DEBUG)
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227,163 | invoice-x/invoice2data | src/invoice2data/output/to_xml.py | write_to_file | def write_to_file(data, path):
"""Export extracted fields to xml
Appends .xml to path if missing and generates xml file in specified directory, if not then in root
Parameters
----------
data : dict
Dictionary of extracted fields
path : str
directory to save generated xml file
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227,164 | invoice-x/invoice2data | src/invoice2data/extract/loader.py | read_templates | def read_templates(folder=None):
"""
Load yaml templates from template folder. Return list of dicts.
Use built-in templates if no folder is set.
Parameters
----------
folder : str
user defined folder where they stores their files, if None uses built-in templates
Returns
------... | python | def read_templates(folder=None):
"""
Load yaml templates from template folder. Return list of dicts.
Use built-in templates if no folder is set.
Parameters
----------
folder : str
user defined folder where they stores their files, if None uses built-in templates
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227,165 | invoice-x/invoice2data | src/invoice2data/input/pdfminer_wrapper.py | to_text | def to_text(path):
"""Wrapper around `pdfminer`.
Parameters
----------
path : str
path of electronic invoice in PDF
Returns
-------
str : str
returns extracted text from pdf
"""
try:
# python 2
from StringIO import StringIO
import sys
... | python | def to_text(path):
"""Wrapper around `pdfminer`.
Parameters
----------
path : str
path of electronic invoice in PDF
Returns
-------
str : str
returns extracted text from pdf
"""
try:
# python 2
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import sys
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227,166 | SALib/SALib | src/SALib/analyze/ff.py | analyze | def analyze(problem, X, Y, second_order=False, print_to_console=False,
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227,167 | SALib/SALib | src/SALib/analyze/ff.py | to_df | def to_df(self):
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Returns
-------
main_effect, inter_effect: tuple
A tuple of DataFrames for main effects and interaction effects.
The second element (for interactions) will be `None` if not available.
'''
na... | python | def to_df(self):
'''Conversion method to Pandas DataFrame. To be attached to ResultDict.
Returns
-------
main_effect, inter_effect: tuple
A tuple of DataFrames for main effects and interaction effects.
The second element (for interactions) will be `None` if not available.
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227,168 | SALib/SALib | src/SALib/analyze/ff.py | interactions | def interactions(problem, Y, print_to_console=False):
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---------
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Computes the second order effects (interactions) between
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227,169 | SALib/SALib | src/SALib/util/__init__.py | avail_approaches | def avail_approaches(pkg):
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Arguments
---------
pkg : module
module to inspect
Returns
---------
method : list
A list of available submodules
'''
methods = [modname for importer, modname, ispkg in
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'''Create list of available modules.
Arguments
---------
pkg : module
module to inspect
Returns
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method : list
A list of available submodules
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227,170 | SALib/SALib | src/SALib/util/__init__.py | scale_samples | def scale_samples(params, bounds):
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list of lists of dimensions `num_params`-by-2
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227,171 | SALib/SALib | src/SALib/util/__init__.py | nonuniform_scale_samples | def nonuniform_scale_samples(params, bounds, dists):
"""Rescale samples in 0-to-1 range to other distributions
Arguments
---------
problem : dict
problem definition including bounds
params : numpy.ndarray
numpy array of dimensions num_params-by-N,
where N is the number of sa... | python | def nonuniform_scale_samples(params, bounds, dists):
"""Rescale samples in 0-to-1 range to other distributions
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---------
problem : dict
problem definition including bounds
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numpy array of dimensions num_params-by-N,
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227,172 | SALib/SALib | src/SALib/util/__init__.py | read_param_file | def read_param_file(filename, delimiter=None):
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Reads a parameter file of format::
Param1,0,1,Group1,dist1
Param2,0,1,Group2,dist2
Param3,0,1,Group3,dist3
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Returns a dictionary containing:
... | python | def read_param_file(filename, delimiter=None):
"""Unpacks a parameter file into a dictionary
Reads a parameter file of format::
Param1,0,1,Group1,dist1
Param2,0,1,Group2,dist2
Param3,0,1,Group3,dist3
(Group and Dist columns are optional)
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227,173 | SALib/SALib | src/SALib/util/__init__.py | compute_groups_matrix | def compute_groups_matrix(groups):
"""Generate matrix which notes factor membership of groups
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"""Generate matrix which notes factor membership of groups
Computes a k-by-g matrix which notes factor membership of groups
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g is the number of groups
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227,174 | SALib/SALib | src/SALib/util/__init__.py | requires_gurobipy | def requires_gurobipy(_has_gurobi):
'''
Decorator function which takes a boolean _has_gurobi as an argument.
Use decorate any functions which require gurobi.
Raises an import error at runtime if gurobi is not present.
Note that all runtime errors should be avoided in the working code,
using brut... | python | def requires_gurobipy(_has_gurobi):
'''
Decorator function which takes a boolean _has_gurobi as an argument.
Use decorate any functions which require gurobi.
Raises an import error at runtime if gurobi is not present.
Note that all runtime errors should be avoided in the working code,
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227,175 | SALib/SALib | src/SALib/analyze/morris.py | compute_grouped_sigma | def compute_grouped_sigma(ungrouped_sigma, group_matrix):
'''
Returns sigma for the groups of parameter values in the
argument ungrouped_metric where the group consists of no more than
one parameter
'''
group_matrix = np.array(group_matrix, dtype=np.bool)
sigma_masked = np.ma.masked_array(... | python | def compute_grouped_sigma(ungrouped_sigma, group_matrix):
'''
Returns sigma for the groups of parameter values in the
argument ungrouped_metric where the group consists of no more than
one parameter
'''
group_matrix = np.array(group_matrix, dtype=np.bool)
sigma_masked = np.ma.masked_array(... | [
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227,176 | SALib/SALib | src/SALib/analyze/morris.py | compute_grouped_metric | def compute_grouped_metric(ungrouped_metric, group_matrix):
'''
Computes the mean value for the groups of parameter values in the
argument ungrouped_metric
'''
group_matrix = np.array(group_matrix, dtype=np.bool)
mu_star_masked = np.ma.masked_array(ungrouped_metric * group_matrix.T,
... | python | def compute_grouped_metric(ungrouped_metric, group_matrix):
'''
Computes the mean value for the groups of parameter values in the
argument ungrouped_metric
'''
group_matrix = np.array(group_matrix, dtype=np.bool)
mu_star_masked = np.ma.masked_array(ungrouped_metric * group_matrix.T,
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227,177 | SALib/SALib | src/SALib/analyze/morris.py | compute_mu_star_confidence | def compute_mu_star_confidence(ee, num_trajectories, num_resamples,
conf_level):
'''
Uses bootstrapping where the elementary effects are resampled with
replacement to produce a histogram of resampled mu_star metrics.
This resample is used to produce a confidence interval.
... | python | def compute_mu_star_confidence(ee, num_trajectories, num_resamples,
conf_level):
'''
Uses bootstrapping where the elementary effects are resampled with
replacement to produce a histogram of resampled mu_star metrics.
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227,178 | SALib/SALib | src/SALib/sample/morris/gurobi.py | timestamp | def timestamp(num_params, p_levels, k_choices, N):
"""
Returns a uniform timestamp with parameter values for file identification
"""
string = "_v%s_l%s_gs%s_k%s_N%s_%s.txt" % (num_params,
p_levels,
k_choices,
... | python | def timestamp(num_params, p_levels, k_choices, N):
"""
Returns a uniform timestamp with parameter values for file identification
"""
string = "_v%s_l%s_gs%s_k%s_N%s_%s.txt" % (num_params,
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227,179 | SALib/SALib | src/SALib/sample/morris/brute.py | BruteForce.brute_force_most_distant | def brute_force_most_distant(self, input_sample, num_samples,
num_params, k_choices,
num_groups=None):
"""Use brute force method to find most distant trajectories
Arguments
---------
input_sample : numpy.ndarray
n... | python | def brute_force_most_distant(self, input_sample, num_samples,
num_params, k_choices,
num_groups=None):
"""Use brute force method to find most distant trajectories
Arguments
---------
input_sample : numpy.ndarray
n... | [
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227,180 | SALib/SALib | src/SALib/sample/morris/brute.py | BruteForce.find_most_distant | def find_most_distant(self, input_sample, num_samples,
num_params, k_choices, num_groups=None):
"""
Finds the 'k_choices' most distant choices from the
'num_samples' trajectories contained in 'input_sample'
Arguments
---------
input_sample : num... | python | def find_most_distant(self, input_sample, num_samples,
num_params, k_choices, num_groups=None):
"""
Finds the 'k_choices' most distant choices from the
'num_samples' trajectories contained in 'input_sample'
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227,181 | SALib/SALib | src/SALib/sample/morris/brute.py | BruteForce.mappable | def mappable(combos, pairwise, distance_matrix):
'''
Obtains scores from the distance_matrix for each pairwise combination
held in the combos array
Arguments
----------
combos : numpy.ndarray
pairwise : numpy.ndarray
distance_matrix : numpy.ndarray
... | python | def mappable(combos, pairwise, distance_matrix):
'''
Obtains scores from the distance_matrix for each pairwise combination
held in the combos array
Arguments
----------
combos : numpy.ndarray
pairwise : numpy.ndarray
distance_matrix : numpy.ndarray
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227,182 | SALib/SALib | src/SALib/sample/morris/brute.py | BruteForce.find_maximum | def find_maximum(self, scores, N, k_choices):
"""Finds the `k_choices` maximum scores from `scores`
Arguments
---------
scores : numpy.ndarray
N : int
k_choices : int
Returns
-------
list
"""
if not isinstance(scores, np.ndarray):... | python | def find_maximum(self, scores, N, k_choices):
"""Finds the `k_choices` maximum scores from `scores`
Arguments
---------
scores : numpy.ndarray
N : int
k_choices : int
Returns
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227,183 | SALib/SALib | src/SALib/sample/morris/brute.py | BruteForce.nth | def nth(iterable, n, default=None):
"""Returns the nth item or a default value
Arguments
---------
iterable : iterable
n : int
default : default=None
The default value to return
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iterable : iterable
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227,184 | SALib/SALib | src/SALib/sample/morris/__init__.py | sample | def sample(problem, N, num_levels=4, optimal_trajectories=None,
local_optimization=True):
"""Generate model inputs using the Method of Morris
Returns a NumPy matrix containing the model inputs required for Method of
Morris. The resulting matrix has :math:`(G+1)*T` rows and :math:`D`
columns... | python | def sample(problem, N, num_levels=4, optimal_trajectories=None,
local_optimization=True):
"""Generate model inputs using the Method of Morris
Returns a NumPy matrix containing the model inputs required for Method of
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227,185 | SALib/SALib | src/SALib/sample/morris/__init__.py | _sample_oat | def _sample_oat(problem, N, num_levels=4):
"""Generate trajectories without groups
Arguments
---------
problem : dict
The problem definition
N : int
The number of samples to generate
num_levels : int, default=4
The number of grid levels
"""
group_membership = np.... | python | def _sample_oat(problem, N, num_levels=4):
"""Generate trajectories without groups
Arguments
---------
problem : dict
The problem definition
N : int
The number of samples to generate
num_levels : int, default=4
The number of grid levels
"""
group_membership = np.... | [
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227,186 | SALib/SALib | src/SALib/sample/morris/__init__.py | _sample_groups | def _sample_groups(problem, N, num_levels=4):
"""Generate trajectories for groups
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where :math:`g` is the number of groups and :math:`k` is the number
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Arguments
---------
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"""Generate trajectories for groups
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227,187 | SALib/SALib | src/SALib/sample/morris/__init__.py | generate_trajectory | def generate_trajectory(group_membership, num_levels=4):
"""Return a single trajectory
Return a single trajectory of size :math:`(g+1)`-by-:math:`k`
where :math:`g` is the number of groups,
and :math:`k` is the number of factors,
both implied by the dimensions of `group_membership`
Arguments
... | python | def generate_trajectory(group_membership, num_levels=4):
"""Return a single trajectory
Return a single trajectory of size :math:`(g+1)`-by-:math:`k`
where :math:`g` is the number of groups,
and :math:`k` is the number of factors,
both implied by the dimensions of `group_membership`
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227,188 | SALib/SALib | src/SALib/sample/morris/__init__.py | generate_p_star | def generate_p_star(num_groups):
"""Describe the order in which groups move
Arguments
---------
num_groups : int
Returns
-------
np.ndarray
Matrix P* - size (g-by-g)
"""
p_star = np.eye(num_groups, num_groups)
rd.shuffle(p_star)
return p_star | python | def generate_p_star(num_groups):
"""Describe the order in which groups move
Arguments
---------
num_groups : int
Returns
-------
np.ndarray
Matrix P* - size (g-by-g)
"""
p_star = np.eye(num_groups, num_groups)
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227,189 | SALib/SALib | src/SALib/scripts/salib.py | parse_subargs | def parse_subargs(module, parser, method, opts):
'''Attach argument parser for action specific options.
Arguments
---------
module : module
name of module to extract action from
parser : argparser
argparser object to attach additional arguments to
method : str
name of me... | python | def parse_subargs(module, parser, method, opts):
'''Attach argument parser for action specific options.
Arguments
---------
module : module
name of module to extract action from
parser : argparser
argparser object to attach additional arguments to
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name of module to extract action from
parser : argparser
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227,190 | SALib/SALib | src/SALib/sample/morris/local.py | LocalOptimisation.find_local_maximum | def find_local_maximum(self, input_sample, N, num_params,
k_choices, num_groups=None):
"""Find the most different trajectories in the input sample using a
local approach
An alternative by Ruano et al. (2012) for the brute force approach as
originally proposed ... | python | def find_local_maximum(self, input_sample, N, num_params,
k_choices, num_groups=None):
"""Find the most different trajectories in the input sample using a
local approach
An alternative by Ruano et al. (2012) for the brute force approach as
originally proposed ... | [
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227,191 | SALib/SALib | src/SALib/sample/morris/local.py | LocalOptimisation.sum_distances | def sum_distances(self, indices, distance_matrix):
"""Calculate combinatorial distance between a select group of
trajectories, indicated by indices
Arguments
---------
indices : tuple
distance_matrix : numpy.ndarray (M,M)
Returns
-------
numpy.nd... | python | def sum_distances(self, indices, distance_matrix):
"""Calculate combinatorial distance between a select group of
trajectories, indicated by indices
Arguments
---------
indices : tuple
distance_matrix : numpy.ndarray (M,M)
Returns
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227,192 | SALib/SALib | src/SALib/sample/morris/local.py | LocalOptimisation.get_max_sum_ind | def get_max_sum_ind(self, indices_list, distances, i, m):
'''Get the indices that belong to the maximum distance in `distances`
Arguments
---------
indices_list : list
list of tuples
distances : numpy.ndarray
size M
i : int
m : int
... | python | def get_max_sum_ind(self, indices_list, distances, i, m):
'''Get the indices that belong to the maximum distance in `distances`
Arguments
---------
indices_list : list
list of tuples
distances : numpy.ndarray
size M
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m : int
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227,193 | SALib/SALib | src/SALib/sample/morris/local.py | LocalOptimisation.add_indices | def add_indices(self, indices, distance_matrix):
'''Adds extra indices for the combinatorial problem.
Arguments
---------
indices : tuple
distance_matrix : numpy.ndarray (M,M)
Example
-------
>>> add_indices((1,2), numpy.array((5,5)))
[(1, 2, 3),... | python | def add_indices(self, indices, distance_matrix):
'''Adds extra indices for the combinatorial problem.
Arguments
---------
indices : tuple
distance_matrix : numpy.ndarray (M,M)
Example
-------
>>> add_indices((1,2), numpy.array((5,5)))
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227,194 | SALib/SALib | src/SALib/util/results.py | ResultDict.to_df | def to_df(self):
'''Convert dict structure into Pandas DataFrame.'''
return pd.DataFrame({k: v for k, v in self.items() if k is not 'names'},
index=self['names']) | python | def to_df(self):
'''Convert dict structure into Pandas DataFrame.'''
return pd.DataFrame({k: v for k, v in self.items() if k is not 'names'},
index=self['names']) | [
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227,195 | SALib/SALib | src/SALib/plotting/morris.py | horizontal_bar_plot | def horizontal_bar_plot(ax, Si, param_dict, sortby='mu_star', unit=''):
'''Updates a matplotlib axes instance with a horizontal bar plot
of mu_star, with error bars representing mu_star_conf
'''
assert sortby in ['mu_star', 'mu_star_conf', 'sigma', 'mu']
# Sort all the plotted elements by mu_star... | python | def horizontal_bar_plot(ax, Si, param_dict, sortby='mu_star', unit=''):
'''Updates a matplotlib axes instance with a horizontal bar plot
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'''
assert sortby in ['mu_star', 'mu_star_conf', 'sigma', 'mu']
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227,196 | SALib/SALib | src/SALib/plotting/morris.py | sample_histograms | def sample_histograms(fig, input_sample, problem, param_dict):
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'''
num_vars = problem['num_vars']
names = problem['names']
framing = 101 + (num_vars * 10)
# Find number of levels
num_levels = len(set(input_sample[:, 1]))
ou... | python | def sample_histograms(fig, input_sample, problem, param_dict):
'''Plots a set of subplots of histograms of the input sample
'''
num_vars = problem['num_vars']
names = problem['names']
framing = 101 + (num_vars * 10)
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227,197 | SALib/SALib | src/SALib/sample/ff.py | extend_bounds | def extend_bounds(problem):
"""Extends the problem bounds to the nearest power of two
Arguments
=========
problem : dict
The problem definition
"""
num_vars = problem['num_vars']
num_ff_vars = 2 ** find_smallest(num_vars)
num_dummy_variables = num_ff_vars - num_vars
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"""Extends the problem bounds to the nearest power of two
Arguments
=========
problem : dict
The problem definition
"""
num_vars = problem['num_vars']
num_ff_vars = 2 ** find_smallest(num_vars)
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227,198 | SALib/SALib | src/SALib/sample/ff.py | generate_contrast | def generate_contrast(problem):
"""Generates the raw sample from the problem file
Arguments
=========
problem : dict
The problem definition
"""
num_vars = problem['num_vars']
# Find the smallest n, such that num_vars < k
k = [2 ** n for n in range(16)]
k_chosen = 2 ** find... | python | def generate_contrast(problem):
"""Generates the raw sample from the problem file
Arguments
=========
problem : dict
The problem definition
"""
num_vars = problem['num_vars']
# Find the smallest n, such that num_vars < k
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] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/sample/ff.py#L59-L77 |
227,199 | SALib/SALib | src/SALib/sample/ff.py | sample | def sample(problem, seed=None):
"""Generates model inputs using a fractional factorial sample
Returns a NumPy matrix containing the model inputs required for a
fractional factorial analysis.
The resulting matrix has D columns, where D is smallest power of 2 that is
greater than the number of parame... | python | def sample(problem, seed=None):
"""Generates model inputs using a fractional factorial sample
Returns a NumPy matrix containing the model inputs required for a
fractional factorial analysis.
The resulting matrix has D columns, where D is smallest power of 2 that is
greater than the number of parame... | [
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Returns a NumPy matrix containing the model inputs required for a
fractional factorial analysis.
The resulting matrix has D columns, where D is smallest power of 2 that is
greater than the number of parameters.
These model inputs are intend... | [
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] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/sample/ff.py#L80-L113 |
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