id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
240,900 | rflamary/POT | ot/plot.py | plot1D_mat | def plot1D_mat(a, b, M, title=''):
""" Plot matrix M with the source and target 1D distribution
Creates a subplot with the source distribution a on the left and
target distribution b on the tot. The matrix M is shown in between.
Parameters
----------
a : np.array, shape (na,)
Source ... | python | def plot1D_mat(a, b, M, title=''):
na, nb = M.shape
gs = gridspec.GridSpec(3, 3)
xa = np.arange(na)
xb = np.arange(nb)
ax1 = pl.subplot(gs[0, 1:])
pl.plot(xb, b, 'r', label='Target distribution')
pl.yticks(())
pl.title(title)
ax2 = pl.subplot(gs[1:, 0])
pl.plot(a, xa, 'b', la... | [
"def",
"plot1D_mat",
"(",
"a",
",",
"b",
",",
"M",
",",
"title",
"=",
"''",
")",
":",
"na",
",",
"nb",
"=",
"M",
".",
"shape",
"gs",
"=",
"gridspec",
".",
"GridSpec",
"(",
"3",
",",
"3",
")",
"xa",
"=",
"np",
".",
"arange",
"(",
"na",
")",
... | Plot matrix M with the source and target 1D distribution
Creates a subplot with the source distribution a on the left and
target distribution b on the tot. The matrix M is shown in between.
Parameters
----------
a : np.array, shape (na,)
Source distribution
b : np.array, shape (nb,)
... | [
"Plot",
"matrix",
"M",
"with",
"the",
"source",
"and",
"target",
"1D",
"distribution"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/plot.py#L14-L54 |
240,901 | rflamary/POT | ot/plot.py | plot2D_samples_mat | def plot2D_samples_mat(xs, xt, G, thr=1e-8, **kwargs):
""" Plot matrix M in 2D with lines using alpha values
Plot lines between source and target 2D samples with a color
proportional to the value of the matrix G between samples.
Parameters
----------
xs : ndarray, shape (ns,2)
Sourc... | python | def plot2D_samples_mat(xs, xt, G, thr=1e-8, **kwargs):
if ('color' not in kwargs) and ('c' not in kwargs):
kwargs['color'] = 'k'
mx = G.max()
for i in range(xs.shape[0]):
for j in range(xt.shape[0]):
if G[i, j] / mx > thr:
pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], ... | [
"def",
"plot2D_samples_mat",
"(",
"xs",
",",
"xt",
",",
"G",
",",
"thr",
"=",
"1e-8",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"(",
"'color'",
"not",
"in",
"kwargs",
")",
"and",
"(",
"'c'",
"not",
"in",
"kwargs",
")",
":",
"kwargs",
"[",
"'color'... | Plot matrix M in 2D with lines using alpha values
Plot lines between source and target 2D samples with a color
proportional to the value of the matrix G between samples.
Parameters
----------
xs : ndarray, shape (ns,2)
Source samples positions
b : ndarray, shape (nt,2)
Targe... | [
"Plot",
"matrix",
"M",
"in",
"2D",
"with",
"lines",
"using",
"alpha",
"values"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/plot.py#L57-L85 |
240,902 | rflamary/POT | ot/gpu/da.py | sinkhorn_lpl1_mm | def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
numInnerItermax=200, stopInnerThr=1e-9, verbose=False,
log=False, to_numpy=True):
"""
Solve the entropic regularization optimal transport problem with nonconvex
group lasso regularization on GPU
... | python | def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
numInnerItermax=200, stopInnerThr=1e-9, verbose=False,
log=False, to_numpy=True):
a, labels_a, b, M = utils.to_gpu(a, labels_a, b, M)
p = 0.5
epsilon = 1e-3
indices_labels = []
labels_a2 ... | [
"def",
"sinkhorn_lpl1_mm",
"(",
"a",
",",
"labels_a",
",",
"b",
",",
"M",
",",
"reg",
",",
"eta",
"=",
"0.1",
",",
"numItermax",
"=",
"10",
",",
"numInnerItermax",
"=",
"200",
",",
"stopInnerThr",
"=",
"1e-9",
",",
"verbose",
"=",
"False",
",",
"log"... | Solve the entropic regularization optimal transport problem with nonconvex
group lasso regularization on GPU
If the input matrix are in numpy format, they will be uploaded to the
GPU first which can incur significant time overhead.
The function solves the following optimization problem:
.. math:... | [
"Solve",
"the",
"entropic",
"regularization",
"optimal",
"transport",
"problem",
"with",
"nonconvex",
"group",
"lasso",
"regularization",
"on",
"GPU"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/gpu/da.py#L22-L144 |
240,903 | rflamary/POT | ot/datasets.py | get_2D_samples_gauss | def get_2D_samples_gauss(n, m, sigma, random_state=None):
""" Deprecated see make_2D_samples_gauss """
return make_2D_samples_gauss(n, m, sigma, random_state=None) | python | def get_2D_samples_gauss(n, m, sigma, random_state=None):
return make_2D_samples_gauss(n, m, sigma, random_state=None) | [
"def",
"get_2D_samples_gauss",
"(",
"n",
",",
"m",
",",
"sigma",
",",
"random_state",
"=",
"None",
")",
":",
"return",
"make_2D_samples_gauss",
"(",
"n",
",",
"m",
",",
"sigma",
",",
"random_state",
"=",
"None",
")"
] | Deprecated see make_2D_samples_gauss | [
"Deprecated",
"see",
"make_2D_samples_gauss"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/datasets.py#L83-L85 |
240,904 | rflamary/POT | ot/datasets.py | get_data_classif | def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs):
""" Deprecated see make_data_classif """
return make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs) | python | def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs):
return make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs) | [
"def",
"get_data_classif",
"(",
"dataset",
",",
"n",
",",
"nz",
"=",
".5",
",",
"theta",
"=",
"0",
",",
"random_state",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"make_data_classif",
"(",
"dataset",
",",
"n",
",",
"nz",
"=",
".5",
"... | Deprecated see make_data_classif | [
"Deprecated",
"see",
"make_data_classif"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/datasets.py#L170-L172 |
240,905 | rflamary/POT | ot/bregman.py | sinkhorn | def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000,
stopThr=1e-9, verbose=False, log=False, **kwargs):
u"""
Solve the entropic regularization optimal transport problem and return the OT matrix
The function solves the following optimization problem:
.. math::
\gamma = ar... | python | def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000,
stopThr=1e-9, verbose=False, log=False, **kwargs):
u"""
Solve the entropic regularization optimal transport problem and return the OT matrix
The function solves the following optimization problem:
.. math::
\gamma = ar... | [
"def",
"sinkhorn",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"method",
"=",
"'sinkhorn'",
",",
"numItermax",
"=",
"1000",
",",
"stopThr",
"=",
"1e-9",
",",
"verbose",
"=",
"False",
",",
"log",
"=",
"False",
",",
"*",
"*",
"kwargs",
")",
":",... | u"""
Solve the entropic regularization optimal transport problem and return the OT matrix
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
\gamma\geq ... | [
"u",
"Solve",
"the",
"entropic",
"regularization",
"optimal",
"transport",
"problem",
"and",
"return",
"the",
"OT",
"matrix"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L16-L128 |
240,906 | rflamary/POT | ot/bregman.py | sinkhorn2 | def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000,
stopThr=1e-9, verbose=False, log=False, **kwargs):
u"""
Solve the entropic regularization optimal transport problem and return the loss
The function solves the following optimization problem:
.. math::
W = \min_\gamm... | python | def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000,
stopThr=1e-9, verbose=False, log=False, **kwargs):
u"""
Solve the entropic regularization optimal transport problem and return the loss
The function solves the following optimization problem:
.. math::
W = \min_\gamm... | [
"def",
"sinkhorn2",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"method",
"=",
"'sinkhorn'",
",",
"numItermax",
"=",
"1000",
",",
"stopThr",
"=",
"1e-9",
",",
"verbose",
"=",
"False",
",",
"log",
"=",
"False",
",",
"*",
"*",
"kwargs",
")",
":"... | u"""
Solve the entropic regularization optimal transport problem and return the loss
The function solves the following optimization problem:
.. math::
W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
\gamma\geq 0
where :... | [
"u",
"Solve",
"the",
"entropic",
"regularization",
"optimal",
"transport",
"problem",
"and",
"return",
"the",
"loss"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L131-L245 |
240,907 | rflamary/POT | ot/bregman.py | geometricBar | def geometricBar(weights, alldistribT):
"""return the weighted geometric mean of distributions"""
assert(len(weights) == alldistribT.shape[1])
return np.exp(np.dot(np.log(alldistribT), weights.T)) | python | def geometricBar(weights, alldistribT):
assert(len(weights) == alldistribT.shape[1])
return np.exp(np.dot(np.log(alldistribT), weights.T)) | [
"def",
"geometricBar",
"(",
"weights",
",",
"alldistribT",
")",
":",
"assert",
"(",
"len",
"(",
"weights",
")",
"==",
"alldistribT",
".",
"shape",
"[",
"1",
"]",
")",
"return",
"np",
".",
"exp",
"(",
"np",
".",
"dot",
"(",
"np",
".",
"log",
"(",
... | return the weighted geometric mean of distributions | [
"return",
"the",
"weighted",
"geometric",
"mean",
"of",
"distributions"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L968-L971 |
240,908 | rflamary/POT | ot/bregman.py | geometricMean | def geometricMean(alldistribT):
"""return the geometric mean of distributions"""
return np.exp(np.mean(np.log(alldistribT), axis=1)) | python | def geometricMean(alldistribT):
return np.exp(np.mean(np.log(alldistribT), axis=1)) | [
"def",
"geometricMean",
"(",
"alldistribT",
")",
":",
"return",
"np",
".",
"exp",
"(",
"np",
".",
"mean",
"(",
"np",
".",
"log",
"(",
"alldistribT",
")",
",",
"axis",
"=",
"1",
")",
")"
] | return the geometric mean of distributions | [
"return",
"the",
"geometric",
"mean",
"of",
"distributions"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L974-L976 |
240,909 | rflamary/POT | ot/bregman.py | projR | def projR(gamma, p):
"""return the KL projection on the row constrints """
return np.multiply(gamma.T, p / np.maximum(np.sum(gamma, axis=1), 1e-10)).T | python | def projR(gamma, p):
return np.multiply(gamma.T, p / np.maximum(np.sum(gamma, axis=1), 1e-10)).T | [
"def",
"projR",
"(",
"gamma",
",",
"p",
")",
":",
"return",
"np",
".",
"multiply",
"(",
"gamma",
".",
"T",
",",
"p",
"/",
"np",
".",
"maximum",
"(",
"np",
".",
"sum",
"(",
"gamma",
",",
"axis",
"=",
"1",
")",
",",
"1e-10",
")",
")",
".",
"T... | return the KL projection on the row constrints | [
"return",
"the",
"KL",
"projection",
"on",
"the",
"row",
"constrints"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L979-L981 |
240,910 | rflamary/POT | ot/bregman.py | projC | def projC(gamma, q):
"""return the KL projection on the column constrints """
return np.multiply(gamma, q / np.maximum(np.sum(gamma, axis=0), 1e-10)) | python | def projC(gamma, q):
return np.multiply(gamma, q / np.maximum(np.sum(gamma, axis=0), 1e-10)) | [
"def",
"projC",
"(",
"gamma",
",",
"q",
")",
":",
"return",
"np",
".",
"multiply",
"(",
"gamma",
",",
"q",
"/",
"np",
".",
"maximum",
"(",
"np",
".",
"sum",
"(",
"gamma",
",",
"axis",
"=",
"0",
")",
",",
"1e-10",
")",
")"
] | return the KL projection on the column constrints | [
"return",
"the",
"KL",
"projection",
"on",
"the",
"column",
"constrints"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L984-L986 |
240,911 | rflamary/POT | ot/bregman.py | barycenter | def barycenter(A, M, reg, weights=None, numItermax=1000,
stopThr=1e-4, verbose=False, log=False):
"""Compute the entropic regularized wasserstein barycenter of distributions A
The function solves the following optimization problem:
.. math::
\mathbf{a} = arg\min_\mathbf{a} \sum_i W_... | python | def barycenter(A, M, reg, weights=None, numItermax=1000,
stopThr=1e-4, verbose=False, log=False):
if weights is None:
weights = np.ones(A.shape[1]) / A.shape[1]
else:
assert(len(weights) == A.shape[1])
if log:
log = {'err': []}
# M = M/np.median(M) # suggested by... | [
"def",
"barycenter",
"(",
"A",
",",
"M",
",",
"reg",
",",
"weights",
"=",
"None",
",",
"numItermax",
"=",
"1000",
",",
"stopThr",
"=",
"1e-4",
",",
"verbose",
"=",
"False",
",",
"log",
"=",
"False",
")",
":",
"if",
"weights",
"is",
"None",
":",
"... | Compute the entropic regularized wasserstein barycenter of distributions A
The function solves the following optimization problem:
.. math::
\mathbf{a} = arg\min_\mathbf{a} \sum_i W_{reg}(\mathbf{a},\mathbf{a}_i)
where :
- :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein ... | [
"Compute",
"the",
"entropic",
"regularized",
"wasserstein",
"barycenter",
"of",
"distributions",
"A"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L989-L1082 |
240,912 | rflamary/POT | ot/bregman.py | convolutional_barycenter2d | def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, stabThr=1e-30, verbose=False, log=False):
"""Compute the entropic regularized wasserstein barycenter of distributions A
where A is a collection of 2D images.
The function solves the following optimization problem:
..... | python | def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, stabThr=1e-30, verbose=False, log=False):
if weights is None:
weights = np.ones(A.shape[0]) / A.shape[0]
else:
assert(len(weights) == A.shape[0])
if log:
log = {'err': []}
b = np.zeros_like(A[0... | [
"def",
"convolutional_barycenter2d",
"(",
"A",
",",
"reg",
",",
"weights",
"=",
"None",
",",
"numItermax",
"=",
"10000",
",",
"stopThr",
"=",
"1e-9",
",",
"stabThr",
"=",
"1e-30",
",",
"verbose",
"=",
"False",
",",
"log",
"=",
"False",
")",
":",
"if",
... | Compute the entropic regularized wasserstein barycenter of distributions A
where A is a collection of 2D images.
The function solves the following optimization problem:
.. math::
\mathbf{a} = arg\min_\mathbf{a} \sum_i W_{reg}(\mathbf{a},\mathbf{a}_i)
where :
- :math:`W_{reg}(\cdot,\cdot)... | [
"Compute",
"the",
"entropic",
"regularized",
"wasserstein",
"barycenter",
"of",
"distributions",
"A",
"where",
"A",
"is",
"a",
"collection",
"of",
"2D",
"images",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L1085-L1192 |
240,913 | rflamary/POT | ot/bregman.py | unmix | def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000,
stopThr=1e-3, verbose=False, log=False):
"""
Compute the unmixing of an observation with a given dictionary using Wasserstein distance
The function solve the following optimization problem:
.. math::
\mathbf{h} = arg\min_\m... | python | def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000,
stopThr=1e-3, verbose=False, log=False):
# M = M/np.median(M)
K = np.exp(-M / reg)
# M0 = M0/np.median(M0)
K0 = np.exp(-M0 / reg0)
old = h0
err = 1
cpt = 0
# log = {'niter':0, 'all_err':[]}
if log:
log ... | [
"def",
"unmix",
"(",
"a",
",",
"D",
",",
"M",
",",
"M0",
",",
"h0",
",",
"reg",
",",
"reg0",
",",
"alpha",
",",
"numItermax",
"=",
"1000",
",",
"stopThr",
"=",
"1e-3",
",",
"verbose",
"=",
"False",
",",
"log",
"=",
"False",
")",
":",
"# M = M/n... | Compute the unmixing of an observation with a given dictionary using Wasserstein distance
The function solve the following optimization problem:
.. math::
\mathbf{h} = arg\min_\mathbf{h} (1- \\alpha) W_{M,reg}(\mathbf{a},\mathbf{Dh})+\\alpha W_{M0,reg0}(\mathbf{h}_0,\mathbf{h})
where :
- :m... | [
"Compute",
"the",
"unmixing",
"of",
"an",
"observation",
"with",
"a",
"given",
"dictionary",
"using",
"Wasserstein",
"distance"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L1195-L1300 |
240,914 | rflamary/POT | ot/bregman.py | empirical_sinkhorn | def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
'''
Solve the entropic regularization optimal transport problem and return the
OT matrix from empirical data
The function solves the following optimization pr... | python | def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
'''
Solve the entropic regularization optimal transport problem and return the
OT matrix from empirical data
The function solves the following optimization pr... | [
"def",
"empirical_sinkhorn",
"(",
"X_s",
",",
"X_t",
",",
"reg",
",",
"a",
"=",
"None",
",",
"b",
"=",
"None",
",",
"metric",
"=",
"'sqeuclidean'",
",",
"numIterMax",
"=",
"10000",
",",
"stopThr",
"=",
"1e-9",
",",
"verbose",
"=",
"False",
",",
"log"... | Solve the entropic regularization optimal transport problem and return the
OT matrix from empirical data
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
... | [
"Solve",
"the",
"entropic",
"regularization",
"optimal",
"transport",
"problem",
"and",
"return",
"the",
"OT",
"matrix",
"from",
"empirical",
"data"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L1303-L1390 |
240,915 | rflamary/POT | ot/bregman.py | empirical_sinkhorn2 | def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
'''
Solve the entropic regularization optimal transport problem from empirical
data and return the OT loss
The function solves the following optimization pr... | python | def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs):
'''
Solve the entropic regularization optimal transport problem from empirical
data and return the OT loss
The function solves the following optimization pr... | [
"def",
"empirical_sinkhorn2",
"(",
"X_s",
",",
"X_t",
",",
"reg",
",",
"a",
"=",
"None",
",",
"b",
"=",
"None",
",",
"metric",
"=",
"'sqeuclidean'",
",",
"numIterMax",
"=",
"10000",
",",
"stopThr",
"=",
"1e-9",
",",
"verbose",
"=",
"False",
",",
"log... | Solve the entropic regularization optimal transport problem from empirical
data and return the OT loss
The function solves the following optimization problem:
.. math::
W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
\gamm... | [
"Solve",
"the",
"entropic",
"regularization",
"optimal",
"transport",
"problem",
"from",
"empirical",
"data",
"and",
"return",
"the",
"OT",
"loss"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L1393-L1480 |
240,916 | 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... | [
"def",
"empirical_sinkhorn_divergence",
"(",
"X_s",
",",
"X_t",
",",
"reg",
",",
"a",
"=",
"None",
",",
"b",
"=",
"None",
",",
"metric",
"=",
"'sqeuclidean'",
",",
"numIterMax",
"=",
"10000",
",",
"stopThr",
"=",
"1e-9",
",",
"verbose",
"=",
"False",
"... | Compute the sinkhorn divergence loss from empirical data
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_... | [
"Compute",
"the",
"sinkhorn",
"divergence",
"loss",
"from",
"empirical",
"data"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/bregman.py#L1483-L1599 |
240,917 | 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):
a = np.asarray(a, dtype=np.float64)
b = np.asarray(b, dtype=np.float64)
M = np.asarray(M, dtype=np.float64)
# if empty array given then use unifor distributions
if len(a) == 0:
a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0]
if l... | [
"def",
"emd",
"(",
"a",
",",
"b",
",",
"M",
",",
"numItermax",
"=",
"100000",
",",
"log",
"=",
"False",
")",
":",
"a",
"=",
"np",
".",
"asarray",
"(",
"a",
",",
"dtype",
"=",
"np",
".",
"float64",
")",
"b",
"=",
"np",
".",
"asarray",
"(",
"... | 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 and b are the sample weights
Uses the algorithm prop... | [
"Solves",
"the",
"Earth",
"Movers",
"distance",
"problem",
"and",
"returns",
"the",
"OT",
"matrix"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/lp/__init__.py#L25-L113 |
240,918 | 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):
a = np.asarray(a, dtype=np.float64)
b = np.asarray(b, dtype=np.float64)
M = np.asarray(M, dtype=np.float64)
# if empty array given then use unifor distributions
if len(a) == 0:
... | [
"def",
"emd2",
"(",
"a",
",",
"b",
",",
"M",
",",
"processes",
"=",
"multiprocessing",
".",
"cpu_count",
"(",
")",
",",
"numItermax",
"=",
"100000",
",",
"log",
"=",
"False",
",",
"return_matrix",
"=",
"False",
")",
":",
"a",
"=",
"np",
".",
"asarr... | 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\geq 0
where :
- M is the metric cost matrix
- a and b are the sample weights
Uses the algorithm proposed i... | [
"Solves",
"the",
"Earth",
"Movers",
"distance",
"problem",
"and",
"returns",
"the",
"loss"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/lp/__init__.py#L116-L219 |
240,919 | 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):
lstlab = np.unique(labels_a)
def f(G):
res = 0
for i in range(G.shape[1]):
for lab in lstlab:
... | [
"def",
"sinkhorn_l1l2_gl",
"(",
"a",
",",
"labels_a",
",",
"b",
",",
"M",
",",
"reg",
",",
"eta",
"=",
"0.1",
",",
"numItermax",
"=",
"10",
",",
"numInnerItermax",
"=",
"200",
",",
"stopInnerThr",
"=",
"1e-9",
",",
"verbose",
"=",
"False",
",",
"log"... | Solve the entropic regularization optimal transport problem with group
lasso regularization
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+
\eta \Omega_g(\gamma)
s.t. \gamma 1 = a
\gam... | [
"Solve",
"the",
"entropic",
"regularization",
"optimal",
"transport",
"problem",
"with",
"group",
"lasso",
"regularization"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/da.py#L134-L238 |
240,920 | 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):
d = xs.shape[1]
if bias:
mxs = xs.mean(0, keepdims=True)
mxt = xt.mean(0, keepdims=True)
xs = xs - mxs
xt = xt - mxt
else:
mxs = np.zeros((1, d))
mxt = np.... | [
"def",
"OT_mapping_linear",
"(",
"xs",
",",
"xt",
",",
"reg",
"=",
"1e-6",
",",
"ws",
"=",
"None",
",",
"wt",
"=",
"None",
",",
"bias",
"=",
"True",
",",
"log",
"=",
"False",
")",
":",
"d",
"=",
"xs",
".",
"shape",
"[",
"1",
"]",
"if",
"bias"... | 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 between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)`
and :math:`N(\mu_t,\Sigma_t)` as proposed in [1... | [
"return",
"OT",
"linear",
"operator",
"between",
"samples"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/da.py#L639-L740 |
240,921 | 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):
a, b = to_gpu(a, b)
a2 = np.sum(np.square(a), 1)
b2 = np.sum(np.square(b), 1)
c = -2 * np.dot(a, b.T)
c += a2[:, None]
c += b2[None, :]
if not squared:
np.sqrt(c, out=c)
if to_numpy:
return to_np(c)
else:... | [
"def",
"euclidean_distances",
"(",
"a",
",",
"b",
",",
"squared",
"=",
"False",
",",
"to_numpy",
"=",
"True",
")",
":",
"a",
",",
"b",
"=",
"to_gpu",
"(",
"a",
",",
"b",
")",
"a2",
"=",
"np",
".",
"sum",
"(",
"np",
".",
"square",
"(",
"a",
")... | 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 (n, f)
first matrix
b : np.ndarray (m, f)
second mat... | [
"Compute",
"the",
"pairwise",
"euclidean",
"distance",
"between",
"matrices",
"a",
"and",
"b",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/gpu/utils.py#L16-L54 |
240,922 | 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):
if x2 is None:
x2 = x1
if metric == "sqeuclidean":
return euclidean_distances(x1, x2, squared=True, to_numpy=to_numpy)
elif metric == "euclidean":
return euclidean_distances(x1, x2, squared=False, to_numpy=to_numpy)
else... | [
"def",
"dist",
"(",
"x1",
",",
"x2",
"=",
"None",
",",
"metric",
"=",
"'sqeuclidean'",
",",
"to_numpy",
"=",
"True",
")",
":",
"if",
"x2",
"is",
"None",
":",
"x2",
"=",
"x1",
"if",
"metric",
"==",
"\"sqeuclidean\"",
":",
"return",
"euclidean_distances"... | 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)
metric : str
Metric from 'sqeuclidean', 'euclidean',
R... | [
"Compute",
"distance",
"between",
"samples",
"in",
"x1",
"and",
"x2",
"on",
"gpu"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/gpu/utils.py#L57-L85 |
240,923 | 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):
if len(args) > 1:
return (cp.asarray(x) for x in args)
else:
return cp.asarray(args[0]) | [
"def",
"to_gpu",
"(",
"*",
"args",
")",
":",
"if",
"len",
"(",
"args",
")",
">",
"1",
":",
"return",
"(",
"cp",
".",
"asarray",
"(",
"x",
")",
"for",
"x",
"in",
"args",
")",
"else",
":",
"return",
"cp",
".",
"asarray",
"(",
"args",
"[",
"0",
... | Upload numpy arrays to GPU and return them | [
"Upload",
"numpy",
"arrays",
"to",
"GPU",
"and",
"return",
"them"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/gpu/utils.py#L88-L93 |
240,924 | 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):
if len(args) > 1:
return (cp.asnumpy(x) for x in args)
else:
return cp.asnumpy(args[0]) | [
"def",
"to_np",
"(",
"*",
"args",
")",
":",
"if",
"len",
"(",
"args",
")",
">",
"1",
":",
"return",
"(",
"cp",
".",
"asnumpy",
"(",
"x",
")",
"for",
"x",
"in",
"args",
")",
"else",
":",
"return",
"cp",
".",
"asnumpy",
"(",
"args",
"[",
"0",
... | convert GPU arras to numpy and return them | [
"convert",
"GPU",
"arras",
"to",
"numpy",
"and",
"return",
"them"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/gpu/utils.py#L96-L101 |
240,925 | 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):
coo = A.tocoo()
SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape)
return SP | [
"def",
"scipy_sparse_to_spmatrix",
"(",
"A",
")",
":",
"coo",
"=",
"A",
".",
"tocoo",
"(",
")",
"SP",
"=",
"spmatrix",
"(",
"coo",
".",
"data",
".",
"tolist",
"(",
")",
",",
"coo",
".",
"row",
".",
"tolist",
"(",
")",
",",
"coo",
".",
"col",
".... | Efficient conversion from scipy sparse matrix to cvxopt sparse matrix | [
"Efficient",
"conversion",
"from",
"scipy",
"sparse",
"matrix",
"to",
"cvxopt",
"sparse",
"matrix"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/lp/cvx.py#L22-L26 |
240,926 | 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):
xk = np.atleast_1d(xk)
fc = [0]
def phi(alpha1):
fc[0] += 1
return f(xk + alpha1 * pk, *args)
if old_fval is None:
phi0 = phi(0.)
else:
phi0 = old_fval
derph... | [
"def",
"line_search_armijo",
"(",
"f",
",",
"xk",
",",
"pk",
",",
"gfk",
",",
"old_fval",
",",
"args",
"=",
"(",
")",
",",
"c1",
"=",
"1e-4",
",",
"alpha0",
"=",
"0.99",
")",
":",
"xk",
"=",
"np",
".",
"atleast_1d",
"(",
"xk",
")",
"fc",
"=",
... | Armijo linesearch function that works with matrices
find an approximate minimum of f(xk+alpha*pk) that satifies the
armijo conditions.
Parameters
----------
f : function
loss function
xk : np.ndarray
initial position
pk : np.ndarray
descent direction
gfk : np.n... | [
"Armijo",
"linesearch",
"function",
"that",
"works",
"with",
"matrices"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/optim.py#L18-L72 |
240,927 | 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):
loop = 1
if log:
log = {'loss': []}
if G0 is None:
G = np.outer(a, b)
else:
G = G0
def cost(G):
return np.sum(M * G) + reg * f(G)
f_val = cost(G)
if log:
... | [
"def",
"cg",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"f",
",",
"df",
",",
"G0",
"=",
"None",
",",
"numItermax",
"=",
"200",
",",
"stopThr",
"=",
"1e-9",
",",
"verbose",
"=",
"False",
",",
"log",
"=",
"False",
")",
":",
"loop",
"=",
"... | 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)
s.t. \gamma 1 = a
\gamma^T 1= b
\gamma\geq 0
where :
- M is the (n... | [
"Solve",
"the",
"general",
"regularized",
"OT",
"problem",
"with",
"conditional",
"gradient"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/optim.py#L75-L204 |
240,928 | 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):
loop = 1
if log:
log = {'loss': []}
if G0 is None:
G = np.outer(a, b)
else:
G = G0
def cost(G):
return np.sum(M * G) + reg1 * np.sum(G ... | [
"def",
"gcg",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg1",
",",
"reg2",
",",
"f",
",",
"df",
",",
"G0",
"=",
"None",
",",
"numItermax",
"=",
"10",
",",
"numInnerItermax",
"=",
"200",
",",
"stopThr",
"=",
"1e-9",
",",
"verbose",
"=",
"False",
",",... | Solve the general regularized OT problem with the generalized conditional gradient
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + reg1\cdot\Omega(\gamma) + reg2\cdot f(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
... | [
"Solve",
"the",
"general",
"regularized",
"OT",
"problem",
"with",
"the",
"generalized",
"conditional",
"gradient"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/optim.py#L207-L341 |
240,929 | 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):
if axis == 1:
n_features = V.shape[1]
U = np.sort(V, axis=1)[:, ::-1]
z = np.ones(len(V)) * z
cssv = np.cumsum(U, axis=1) - z[:, np.newaxis]
ind = np.arange(n_features) + 1
cond = U - cssv / ind > 0
rho = np.count_non... | [
"def",
"projection_simplex",
"(",
"V",
",",
"z",
"=",
"1",
",",
"axis",
"=",
"None",
")",
":",
"if",
"axis",
"==",
"1",
":",
"n_features",
"=",
"V",
".",
"shape",
"[",
"1",
"]",
"U",
"=",
"np",
".",
"sort",
"(",
"V",
",",
"axis",
"=",
"1",
... | 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: project each V[i] by P(V[i]; z[i])
- axis=0:... | [
"Projection",
"of",
"x",
"onto",
"the",
"simplex",
"scaled",
"by",
"z"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/smooth.py#L47-L74 |
240,930 | 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):
obj = np.dot(alpha, a) + np.dot(beta, b)
grad_alpha = a.copy()
grad_beta = b.copy()
# X[:, j] = alpha + beta[j] - C[:, j]
X = alpha[:, np.newaxis] + beta - C
# val.shape = len(b)
# G.shape = len(a) x len(b)
val, G = regul.delta_Omega(X)
... | [
"def",
"dual_obj_grad",
"(",
"alpha",
",",
"beta",
",",
"a",
",",
"b",
",",
"C",
",",
"regul",
")",
":",
"obj",
"=",
"np",
".",
"dot",
"(",
"alpha",
",",
"a",
")",
"+",
"np",
".",
"dot",
"(",
"beta",
",",
"b",
")",
"grad_alpha",
"=",
"a",
"... | 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)
Input histograms (should be non-negative and sum to 1).
... | [
"Compute",
"objective",
"value",
"and",
"gradients",
"of",
"dual",
"objective",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/smooth.py#L192-L233 |
240,931 | 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):
def _func(params):
# Unpack alpha and beta.
alpha = params[:len(a)]
beta = params[len(a):]
obj, grad_alpha, grad_beta = dual_obj_grad(alpha, beta, a, b, C, regul)
# Pack... | [
"def",
"solve_dual",
"(",
"a",
",",
"b",
",",
"C",
",",
"regul",
",",
"method",
"=",
"\"L-BFGS-B\"",
",",
"tol",
"=",
"1e-3",
",",
"max_iter",
"=",
"500",
",",
"verbose",
"=",
"False",
")",
":",
"def",
"_func",
"(",
"params",
")",
":",
"# Unpack al... | 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, shape = len(a) x len(b)
Ground cost matrix.
regul: Regularization object
Should implement a delt... | [
"Solve",
"the",
"smoothed",
"dual",
"objective",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/smooth.py#L236-L289 |
240,932 | 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):
obj = np.dot(alpha, a)
grad = a.copy()
# X[:, j] = alpha - C[:, j]
X = alpha[:, np.newaxis] - C
# val.shape = len(b)
# G.shape = len(a) x len(b)
val, G = regul.max_Omega(X, b)
obj -= np.dot(b, val)
grad -= np.dot(G, b)
return obj... | [
"def",
"semi_dual_obj_grad",
"(",
"alpha",
",",
"a",
",",
"b",
",",
"C",
",",
"regul",
")",
":",
"obj",
"=",
"np",
".",
"dot",
"(",
"alpha",
",",
"a",
")",
"grad",
"=",
"a",
".",
"copy",
"(",
")",
"# X[:, j] = alpha - C[:, j]",
"X",
"=",
"alpha",
... | 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 (should be non-negative and sum to 1).
C: array, shape = le... | [
"Compute",
"objective",
"value",
"and",
"gradient",
"of",
"semi",
"-",
"dual",
"objective",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/smooth.py#L292-L328 |
240,933 | 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):
def _func(alpha):
obj, grad = semi_dual_obj_grad(alpha, a, b, C, regul)
# We need to maximize the semi-dual.
return -obj, -grad
alpha_init = np.zeros(len(a))
res = min... | [
"def",
"solve_semi_dual",
"(",
"a",
",",
"b",
",",
"C",
",",
"regul",
",",
"method",
"=",
"\"L-BFGS-B\"",
",",
"tol",
"=",
"1e-3",
",",
"max_iter",
"=",
"500",
",",
"verbose",
"=",
"False",
")",
":",
"def",
"_func",
"(",
"alpha",
")",
":",
"obj",
... | 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).
C: array, shape = len(a) x len(b)
Ground cost matrix.
regul: Regularization object
Should implement a... | [
"Solve",
"the",
"smoothed",
"semi",
"-",
"dual",
"objective",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/smooth.py#L331-L368 |
240,934 | 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):
X = alpha[:, np.newaxis] + beta - C
return regul.delta_Omega(X)[1] | [
"def",
"get_plan_from_dual",
"(",
"alpha",
",",
"beta",
",",
"C",
",",
"regul",
")",
":",
"X",
"=",
"alpha",
"[",
":",
",",
"np",
".",
"newaxis",
"]",
"+",
"beta",
"-",
"C",
"return",
"regul",
".",
"delta_Omega",
"(",
"X",
")",
"[",
"1",
"]"
] | 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.
regul: Regularization object
Should implement ... | [
"Retrieve",
"optimal",
"transportation",
"plan",
"from",
"optimal",
"dual",
"potentials",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/smooth.py#L371-L391 |
240,935 | 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):
X = alpha[:, np.newaxis] - C
return regul.max_Omega(X, b)[1] * b | [
"def",
"get_plan_from_semi_dual",
"(",
"alpha",
",",
"b",
",",
"C",
",",
"regul",
")",
":",
"X",
"=",
"alpha",
"[",
":",
",",
"np",
".",
"newaxis",
"]",
"-",
"C",
"return",
"regul",
".",
"max_Omega",
"(",
"X",
",",
"b",
")",
"[",
"1",
"]",
"*",... | 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 and sum to 1).
C: array, shape = len(a) x len(b)
G... | [
"Retrieve",
"optimal",
"transportation",
"plan",
"from",
"optimal",
"semi",
"-",
"dual",
"potentials",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/smooth.py#L394-L415 |
240,936 | 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::
... | [
"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::
\max_{\alpha,\beta}\quad a^T\alpha+b^T\beta-\sum_j\delta_\Omega(\alpha+\beta_j-\mathbf{m}_j)
where :
- :math:`\mathbf{m}_j` is the j... | [
"r",
"Solve",
"the",
"regularized",
"OT",
"problem",
"in",
"the",
"dual",
"and",
"return",
"the",
"OT",
"matrix"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/smooth.py#L418-L507 |
240,937 | 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
The function solves the smooth relaxed dual formulation (10) in [17]_ :
... | 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
The function solves the smooth relaxed dual formulation (10) in [17]_ :
... | [
"def",
"smooth_ot_semi_dual",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"reg_type",
"=",
"'l2'",
",",
"method",
"=",
"\"L-BFGS-B\"",
",",
"stopThr",
"=",
"1e-9",
",",
"numItermax",
"=",
"500",
",",
"verbose",
"=",
"False",
",",
"log",
"=",
"Fals... | r"""
Solve the regularized OT problem in the semi-dual and return the OT matrix
The function solves the smooth relaxed dual formulation (10) in [17]_ :
.. math::
\max_{\alpha}\quad a^T\alpha-OT_\Omega^*(\alpha,b)
where :
.. math::
OT_\Omega^*(\alpha,b)=\sum_j b_j
- :math:`\m... | [
"r",
"Solve",
"the",
"regularized",
"OT",
"problem",
"in",
"the",
"semi",
"-",
"dual",
"and",
"return",
"the",
"OT",
"matrix"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/smooth.py#L510-L600 |
240,938 | 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):
if method.lower() in ['gaussian', 'gauss', 'rbf']:
K = np.exp(-dist(x1, x2) / (2 * sigma**2))
return K | [
"def",
"kernel",
"(",
"x1",
",",
"x2",
",",
"method",
"=",
"'gaussian'",
",",
"sigma",
"=",
"1",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"method",
".",
"lower",
"(",
")",
"in",
"[",
"'gaussian'",
",",
"'gauss'",
",",
"'rbf'",
"]",
":",
"K",
"=... | Compute kernel matrix | [
"Compute",
"kernel",
"matrix"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/utils.py#L45-L49 |
240,939 | rflamary/POT | ot/utils.py | clean_zeros | def clean_zeros(a, b, M):
""" Remove all components with zeros weights in a and b
"""
M2 = M[a > 0, :][:, b > 0].copy() # copy force c style matrix (froemd)
a2 = a[a > 0]
b2 = b[b > 0]
return a2, b2, M2 | python | def clean_zeros(a, b, M):
M2 = M[a > 0, :][:, b > 0].copy() # copy force c style matrix (froemd)
a2 = a[a > 0]
b2 = b[b > 0]
return a2, b2, M2 | [
"def",
"clean_zeros",
"(",
"a",
",",
"b",
",",
"M",
")",
":",
"M2",
"=",
"M",
"[",
"a",
">",
"0",
",",
":",
"]",
"[",
":",
",",
"b",
">",
"0",
"]",
".",
"copy",
"(",
")",
"# copy force c style matrix (froemd)",
"a2",
"=",
"a",
"[",
"a",
">",
... | Remove all components with zeros weights in a and b | [
"Remove",
"all",
"components",
"with",
"zeros",
"weights",
"in",
"a",
"and",
"b"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/utils.py#L71-L77 |
240,940 | rflamary/POT | ot/utils.py | dist | def dist(x1, x2=None, metric='sqeuclidean'):
"""Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist
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'):
if x2 is None:
x2 = x1
if metric == "sqeuclidean":
return euclidean_distances(x1, x2, squared=True)
return cdist(x1, x2, metric=metric) | [
"def",
"dist",
"(",
"x1",
",",
"x2",
"=",
"None",
",",
"metric",
"=",
"'sqeuclidean'",
")",
":",
"if",
"x2",
"is",
"None",
":",
"x2",
"=",
"x1",
"if",
"metric",
"==",
"\"sqeuclidean\"",
":",
"return",
"euclidean_distances",
"(",
"x1",
",",
"x2",
",",... | Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist
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)
metric : str, fun, optional
... | [
"Compute",
"distance",
"between",
"samples",
"in",
"x1",
"and",
"x2",
"using",
"function",
"scipy",
".",
"spatial",
".",
"distance",
".",
"cdist"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/utils.py#L108-L137 |
240,941 | 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
value do not normalize.
Returns
... | python | def cost_normalization(C, norm=None):
if norm == "median":
C /= float(np.median(C))
elif norm == "max":
C /= float(np.max(C))
elif norm == "log":
C = np.log(1 + C)
elif norm == "loglog":
C = np.log(1 + np.log(1 + C))
return C | [
"def",
"cost_normalization",
"(",
"C",
",",
"norm",
"=",
"None",
")",
":",
"if",
"norm",
"==",
"\"median\"",
":",
"C",
"/=",
"float",
"(",
"np",
".",
"median",
"(",
"C",
")",
")",
"elif",
"norm",
"==",
"\"max\"",
":",
"C",
"/=",
"float",
"(",
"np... | 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
value do not normalize.
Returns
-------
C : np.array (n1, n2)
T... | [
"Apply",
"normalization",
"to",
"the",
"loss",
"matrix"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/utils.py#L169-L199 |
240,942 | 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()
proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out))
for _ in range(nprocs)]
for p in proc:
p.daemon = Tru... | python | def parmap(f, X, nprocs=multiprocessing.cpu_count()):
q_in = multiprocessing.Queue(1)
q_out = multiprocessing.Queue()
proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out))
for _ in range(nprocs)]
for p in proc:
p.daemon = True
p.start()
sent = [q_in.put((i,... | [
"def",
"parmap",
"(",
"f",
",",
"X",
",",
"nprocs",
"=",
"multiprocessing",
".",
"cpu_count",
"(",
")",
")",
":",
"q_in",
"=",
"multiprocessing",
".",
"Queue",
"(",
"1",
")",
"q_out",
"=",
"multiprocessing",
".",
"Queue",
"(",
")",
"proc",
"=",
"[",
... | paralell map for multiprocessing | [
"paralell",
"map",
"for",
"multiprocessing"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/utils.py#L216-L233 |
240,943 | rflamary/POT | ot/utils.py | _is_deprecated | def _is_deprecated(func):
"""Helper to check if func is wraped by our deprecated decorator"""
if sys.version_info < (3, 5):
raise NotImplementedError("This is only available for python3.5 "
"or above")
closures = getattr(func, '__closure__', [])
if closures is N... | python | def _is_deprecated(func):
if sys.version_info < (3, 5):
raise NotImplementedError("This is only available for python3.5 "
"or above")
closures = getattr(func, '__closure__', [])
if closures is None:
closures = []
is_deprecated = ('deprecated' in ''.join(... | [
"def",
"_is_deprecated",
"(",
"func",
")",
":",
"if",
"sys",
".",
"version_info",
"<",
"(",
"3",
",",
"5",
")",
":",
"raise",
"NotImplementedError",
"(",
"\"This is only available for python3.5 \"",
"\"or above\"",
")",
"closures",
"=",
"getattr",
"(",
"func",
... | Helper to check if func is wraped by our deprecated decorator | [
"Helper",
"to",
"check",
"if",
"func",
"is",
"wraped",
"by",
"our",
"deprecated",
"decorator"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/utils.py#L361-L372 |
240,944 | rflamary/POT | ot/utils.py | deprecated._decorate_fun | def _decorate_fun(self, fun):
"""Decorate function fun"""
msg = "Function %s is deprecated" % fun.__name__
if self.extra:
msg += "; %s" % self.extra
def wrapped(*args, **kwargs):
warnings.warn(msg, category=DeprecationWarning)
return fun(*args, **kwa... | python | def _decorate_fun(self, fun):
msg = "Function %s is deprecated" % fun.__name__
if self.extra:
msg += "; %s" % self.extra
def wrapped(*args, **kwargs):
warnings.warn(msg, category=DeprecationWarning)
return fun(*args, **kwargs)
wrapped.__name__ = fun.... | [
"def",
"_decorate_fun",
"(",
"self",
",",
"fun",
")",
":",
"msg",
"=",
"\"Function %s is deprecated\"",
"%",
"fun",
".",
"__name__",
"if",
"self",
".",
"extra",
":",
"msg",
"+=",
"\"; %s\"",
"%",
"self",
".",
"extra",
"def",
"wrapped",
"(",
"*",
"args",
... | Decorate function fun | [
"Decorate",
"function",
"fun"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/utils.py#L335-L350 |
240,945 | rflamary/POT | ot/dr.py | split_classes | def split_classes(X, y):
"""split samples in X by classes in y
"""
lstsclass = np.unique(y)
return [X[y == i, :].astype(np.float32) for i in lstsclass] | python | def split_classes(X, y):
lstsclass = np.unique(y)
return [X[y == i, :].astype(np.float32) for i in lstsclass] | [
"def",
"split_classes",
"(",
"X",
",",
"y",
")",
":",
"lstsclass",
"=",
"np",
".",
"unique",
"(",
"y",
")",
"return",
"[",
"X",
"[",
"y",
"==",
"i",
",",
":",
"]",
".",
"astype",
"(",
"np",
".",
"float32",
")",
"for",
"i",
"in",
"lstsclass",
... | split samples in X by classes in y | [
"split",
"samples",
"in",
"X",
"by",
"classes",
"in",
"y"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/dr.py#L38-L42 |
240,946 | rflamary/POT | ot/dr.py | fda | def fda(X, y, p=2, reg=1e-16):
"""
Fisher Discriminant Analysis
Parameters
----------
X : numpy.ndarray (n,d)
Training samples
y : np.ndarray (n,)
labels for training samples
p : int, optional
size of dimensionnality reduction
reg : float, optional
Regul... | python | def fda(X, y, p=2, reg=1e-16):
mx = np.mean(X)
X -= mx.reshape((1, -1))
# data split between classes
d = X.shape[1]
xc = split_classes(X, y)
nc = len(xc)
p = min(nc - 1, p)
Cw = 0
for x in xc:
Cw += np.cov(x, rowvar=False)
Cw /= nc
mxc = np.zeros((d, nc))
for... | [
"def",
"fda",
"(",
"X",
",",
"y",
",",
"p",
"=",
"2",
",",
"reg",
"=",
"1e-16",
")",
":",
"mx",
"=",
"np",
".",
"mean",
"(",
"X",
")",
"X",
"-=",
"mx",
".",
"reshape",
"(",
"(",
"1",
",",
"-",
"1",
")",
")",
"# data split between classes",
... | Fisher Discriminant Analysis
Parameters
----------
X : numpy.ndarray (n,d)
Training samples
y : np.ndarray (n,)
labels for training samples
p : int, optional
size of dimensionnality reduction
reg : float, optional
Regularization term >0 (ridge regularization)
... | [
"Fisher",
"Discriminant",
"Analysis"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/dr.py#L45-L107 |
240,947 | 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
The function solves the following optimization problem:
.. math::
\gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\... | [
"def",
"sag_entropic_transport",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"numItermax",
"=",
"10000",
",",
"lr",
"=",
"None",
")",
":",
"if",
"lr",
"is",
"None",
":",
"lr",
"=",
"1.",
"/",
"max",
"(",
"a",
"/",
"reg",
")",
"n_source",
"="... | 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\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1 = b
... | [
"Compute",
"the",
"SAG",
"algorithm",
"to",
"solve",
"the",
"regularized",
"discrete",
"measures",
"optimal",
"transport",
"max",
"problem"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/stochastic.py#L86-L175 |
240,948 | 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 + ... | [
"def",
"averaged_sgd_entropic_transport",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"numItermax",
"=",
"300000",
",",
"lr",
"=",
"None",
")",
":",
"if",
"lr",
"is",
"None",
":",
"lr",
"=",
"1.",
"/",
"max",
"(",
"a",
"/",
"reg",
")",
"n_sour... | 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 + reg\cdot\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
... | [
"Compute",
"the",
"ASGD",
"algorithm",
"to",
"solve",
"the",
"regularized",
"semi",
"continous",
"measures",
"optimal",
"transport",
"max",
"problem"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/stochastic.py#L178-L263 |
240,949 | 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... | [
"def",
"c_transform_entropic",
"(",
"b",
",",
"M",
",",
"reg",
",",
"beta",
")",
":",
"n_source",
"=",
"np",
".",
"shape",
"(",
"M",
")",
"[",
"0",
"]",
"alpha",
"=",
"np",
".",
"zeros",
"(",
"n_source",
")",
"for",
"i",
"in",
"range",
"(",
"n_... | 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 matrix
- u, v are dual variables in R^IxR^J
- re... | [
"The",
"goal",
"is",
"to",
"recover",
"u",
"from",
"the",
"c",
"-",
"transform",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/stochastic.py#L266-L338 |
240,950 | 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:
.. ma... | [
"def",
"solve_semi_dual_entropic",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"method",
",",
"numItermax",
"=",
"10000",
",",
"lr",
"=",
"None",
",",
"log",
"=",
"False",
")",
":",
"if",
"method",
".",
"lower",
"(",
")",
"==",
"\"sag\"",
":",
... | Compute the transportation matrix 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\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
... | [
"Compute",
"the",
"transportation",
"matrix",
"to",
"solve",
"the",
"regularized",
"discrete",
"measures",
"optimal",
"transport",
"max",
"problem"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/stochastic.py#L341-L444 |
240,951 | 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} -... | [
"def",
"batch_grad_dual",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"alpha",
",",
"beta",
",",
"batch_size",
",",
"batch_alpha",
",",
"batch_beta",
")",
":",
"G",
"=",
"-",
"(",
"np",
".",
"exp",
"(",
"(",
"alpha",
"[",
"batch_alpha",
",",
"... | 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} - \sum_{j \in B_v} exp((u_i + v_j - M_{i,j})/reg) * a_i * b_j
\partial_{v_j} F = v_j * b_s/l_{u} - \sum_{i \i... | [
"Computes",
"the",
"partial",
"gradient",
"of",
"the",
"dual",
"optimal",
"transport",
"problem",
"."
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/stochastic.py#L452-L547 |
240,952 | 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::
\gamma = arg\min_\gamma <\gamma,M>_F + re... | [
"def",
"sgd_entropic_regularization",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"batch_size",
",",
"numItermax",
",",
"lr",
")",
":",
"n_source",
"=",
"np",
".",
"shape",
"(",
"M",
")",
"[",
"0",
"]",
"n_target",
"=",
"np",
".",
"shape",
"(",
... | 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 + reg\cdot\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
... | [
"Compute",
"the",
"sgd",
"algorithm",
"to",
"solve",
"the",
"regularized",
"discrete",
"measures",
"optimal",
"transport",
"dual",
"problem"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/stochastic.py#L550-L642 |
240,953 | 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::
... | [
"def",
"solve_dual_entropic",
"(",
"a",
",",
"b",
",",
"M",
",",
"reg",
",",
"batch_size",
",",
"numItermax",
"=",
"10000",
",",
"lr",
"=",
"1",
",",
"log",
"=",
"False",
")",
":",
"opt_alpha",
",",
"opt_beta",
"=",
"sgd_entropic_regularization",
"(",
... | Compute the transportation matrix 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 + reg\cdot\Omega(\gamma)
s.t. \gamma 1 = a
\gamma^T 1= b
... | [
"Compute",
"the",
"transportation",
"matrix",
"to",
"solve",
"the",
"regularized",
"discrete",
"measures",
"optimal",
"transport",
"dual",
"problem"
] | c5108efc7b6702e1af3928bef1032e6b37734d1c | https://github.com/rflamary/POT/blob/c5108efc7b6702e1af3928bef1032e6b37734d1c/ot/stochastic.py#L645-L736 |
240,954 | 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):
self._stdout.write(str(message))
self._stdout.write('\n') | [
"def",
"flake",
"(",
"self",
",",
"message",
")",
":",
"self",
".",
"_stdout",
".",
"write",
"(",
"str",
"(",
"message",
")",
")",
"self",
".",
"_stdout",
".",
"write",
"(",
"'\\n'",
")"
] | pyflakes found something wrong with the code.
@param: A L{pyflakes.messages.Message}. | [
"pyflakes",
"found",
"something",
"wrong",
"with",
"the",
"code",
"."
] | 232cb1d27ee134bf96adc8f37e53589dc259b159 | https://github.com/PyCQA/pyflakes/blob/232cb1d27ee134bf96adc8f37e53589dc259b159/pyflakes/reporter.py#L68-L75 |
240,955 | PyCQA/pyflakes | pyflakes/checker.py | counter | def counter(items):
"""
Simplest required implementation of collections.Counter. Required as 2.6
does not have Counter in collections.
"""
results = {}
for item in items:
results[item] = results.get(item, 0) + 1
return results | python | def counter(items):
results = {}
for item in items:
results[item] = results.get(item, 0) + 1
return results | [
"def",
"counter",
"(",
"items",
")",
":",
"results",
"=",
"{",
"}",
"for",
"item",
"in",
"items",
":",
"results",
"[",
"item",
"]",
"=",
"results",
".",
"get",
"(",
"item",
",",
"0",
")",
"+",
"1",
"return",
"results"
] | Simplest required implementation of collections.Counter. Required as 2.6
does not have Counter in collections. | [
"Simplest",
"required",
"implementation",
"of",
"collections",
".",
"Counter",
".",
"Required",
"as",
"2",
".",
"6",
"does",
"not",
"have",
"Counter",
"in",
"collections",
"."
] | 232cb1d27ee134bf96adc8f37e53589dc259b159 | https://github.com/PyCQA/pyflakes/blob/232cb1d27ee134bf96adc8f37e53589dc259b159/pyflakes/checker.py#L103-L111 |
240,956 | 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):
if self._has_alias():
return 'import %s as %s' % (self.fullName, self.name)
else:
return 'import %s' % self.fullName | [
"def",
"source_statement",
"(",
"self",
")",
":",
"if",
"self",
".",
"_has_alias",
"(",
")",
":",
"return",
"'import %s as %s'",
"%",
"(",
"self",
".",
"fullName",
",",
"self",
".",
"name",
")",
"else",
":",
"return",
"'import %s'",
"%",
"self",
".",
"... | Generate a source statement equivalent to the import. | [
"Generate",
"a",
"source",
"statement",
"equivalent",
"to",
"the",
"import",
"."
] | 232cb1d27ee134bf96adc8f37e53589dc259b159 | https://github.com/PyCQA/pyflakes/blob/232cb1d27ee134bf96adc8f37e53589dc259b159/pyflakes/checker.py#L265-L270 |
240,957 | PyCQA/pyflakes | pyflakes/checker.py | Checker.CLASSDEF | def CLASSDEF(self, node):
"""
Check names used in a class definition, including its decorators, base
classes, and the body of its definition. Additionally, add its name to
the current scope.
"""
for deco in node.decorator_list:
self.handleNode(deco, node)
... | python | def CLASSDEF(self, node):
for deco in node.decorator_list:
self.handleNode(deco, node)
for baseNode in node.bases:
self.handleNode(baseNode, node)
if not PY2:
for keywordNode in node.keywords:
self.handleNode(keywordNode, node)
self.pus... | [
"def",
"CLASSDEF",
"(",
"self",
",",
"node",
")",
":",
"for",
"deco",
"in",
"node",
".",
"decorator_list",
":",
"self",
".",
"handleNode",
"(",
"deco",
",",
"node",
")",
"for",
"baseNode",
"in",
"node",
".",
"bases",
":",
"self",
".",
"handleNode",
"... | Check names used in a class definition, including its decorators, base
classes, and the body of its definition. Additionally, add its name to
the current scope. | [
"Check",
"names",
"used",
"in",
"a",
"class",
"definition",
"including",
"its",
"decorators",
"base",
"classes",
"and",
"the",
"body",
"of",
"its",
"definition",
".",
"Additionally",
"add",
"its",
"name",
"to",
"the",
"current",
"scope",
"."
] | 232cb1d27ee134bf96adc8f37e53589dc259b159 | https://github.com/PyCQA/pyflakes/blob/232cb1d27ee134bf96adc8f37e53589dc259b159/pyflakes/checker.py#L1519-L1542 |
240,958 | PyCQA/pyflakes | pyflakes/api.py | isPythonFile | def isPythonFile(filename):
"""Return True if filename points to a Python file."""
if filename.endswith('.py'):
return True
# Avoid obvious Emacs backup files
if filename.endswith("~"):
return False
max_bytes = 128
try:
with open(filename, 'rb') as f:
text ... | python | def isPythonFile(filename):
if filename.endswith('.py'):
return True
# Avoid obvious Emacs backup files
if filename.endswith("~"):
return False
max_bytes = 128
try:
with open(filename, 'rb') as f:
text = f.read(max_bytes)
if not text:
... | [
"def",
"isPythonFile",
"(",
"filename",
")",
":",
"if",
"filename",
".",
"endswith",
"(",
"'.py'",
")",
":",
"return",
"True",
"# Avoid obvious Emacs backup files",
"if",
"filename",
".",
"endswith",
"(",
"\"~\"",
")",
":",
"return",
"False",
"max_bytes",
"=",... | Return True if filename points to a Python file. | [
"Return",
"True",
"if",
"filename",
"points",
"to",
"a",
"Python",
"file",
"."
] | 232cb1d27ee134bf96adc8f37e53589dc259b159 | https://github.com/PyCQA/pyflakes/blob/232cb1d27ee134bf96adc8f37e53589dc259b159/pyflakes/api.py#L102-L122 |
240,959 | PyCQA/pyflakes | pyflakes/api.py | _exitOnSignal | def _exitOnSignal(sigName, message):
"""Handles a signal with sys.exit.
Some of these signals (SIGPIPE, for example) don't exist or are invalid on
Windows. So, ignore errors that might arise.
"""
import signal
try:
sigNumber = getattr(signal, sigName)
except AttributeError:
... | python | def _exitOnSignal(sigName, message):
import signal
try:
sigNumber = getattr(signal, sigName)
except AttributeError:
# the signal constants defined in the signal module are defined by
# whether the C library supports them or not. So, SIGPIPE might not
# even be defined.
... | [
"def",
"_exitOnSignal",
"(",
"sigName",
",",
"message",
")",
":",
"import",
"signal",
"try",
":",
"sigNumber",
"=",
"getattr",
"(",
"signal",
",",
"sigName",
")",
"except",
"AttributeError",
":",
"# the signal constants defined in the signal module are defined by",
"#... | Handles a signal with sys.exit.
Some of these signals (SIGPIPE, for example) don't exist or are invalid on
Windows. So, ignore errors that might arise. | [
"Handles",
"a",
"signal",
"with",
"sys",
".",
"exit",
"."
] | 232cb1d27ee134bf96adc8f37e53589dc259b159 | https://github.com/PyCQA/pyflakes/blob/232cb1d27ee134bf96adc8f37e53589dc259b159/pyflakes/api.py#L160-L184 |
240,960 | jazzband/django-model-utils | model_utils/managers.py | InheritanceQuerySetMixin._get_subclasses_recurse | def _get_subclasses_recurse(self, model, levels=None):
"""
Given a Model class, find all related objects, exploring children
recursively, returning a `list` of strings representing the
relations for select_related
"""
related_objects = [
f for f in model._meta... | python | def _get_subclasses_recurse(self, model, levels=None):
related_objects = [
f for f in model._meta.get_fields()
if isinstance(f, OneToOneRel)]
rels = [
rel for rel in related_objects
if isinstance(rel.field, OneToOneField)
and issubclass(rel.fi... | [
"def",
"_get_subclasses_recurse",
"(",
"self",
",",
"model",
",",
"levels",
"=",
"None",
")",
":",
"related_objects",
"=",
"[",
"f",
"for",
"f",
"in",
"model",
".",
"_meta",
".",
"get_fields",
"(",
")",
"if",
"isinstance",
"(",
"f",
",",
"OneToOneRel",
... | Given a Model class, find all related objects, exploring children
recursively, returning a `list` of strings representing the
relations for select_related | [
"Given",
"a",
"Model",
"class",
"find",
"all",
"related",
"objects",
"exploring",
"children",
"recursively",
"returning",
"a",
"list",
"of",
"strings",
"representing",
"the",
"relations",
"for",
"select_related"
] | d557c4253312774a7c2f14bcd02675e9ac2ea05f | https://github.com/jazzband/django-model-utils/blob/d557c4253312774a7c2f14bcd02675e9ac2ea05f/model_utils/managers.py#L146-L174 |
240,961 | jazzband/django-model-utils | model_utils/managers.py | InheritanceQuerySetMixin._get_ancestors_path | def _get_ancestors_path(self, model, levels=None):
"""
Serves as an opposite to _get_subclasses_recurse, instead walking from
the Model class up the Model's ancestry and constructing the desired
select_related string backwards.
"""
if not issubclass(model, self.model):
... | python | def _get_ancestors_path(self, model, levels=None):
if not issubclass(model, self.model):
raise ValueError(
"%r is not a subclass of %r" % (model, self.model))
ancestry = []
# should be a OneToOneField or None
parent_link = model._meta.get_ancestor_link(self.m... | [
"def",
"_get_ancestors_path",
"(",
"self",
",",
"model",
",",
"levels",
"=",
"None",
")",
":",
"if",
"not",
"issubclass",
"(",
"model",
",",
"self",
".",
"model",
")",
":",
"raise",
"ValueError",
"(",
"\"%r is not a subclass of %r\"",
"%",
"(",
"model",
",... | Serves as an opposite to _get_subclasses_recurse, instead walking from
the Model class up the Model's ancestry and constructing the desired
select_related string backwards. | [
"Serves",
"as",
"an",
"opposite",
"to",
"_get_subclasses_recurse",
"instead",
"walking",
"from",
"the",
"Model",
"class",
"up",
"the",
"Model",
"s",
"ancestry",
"and",
"constructing",
"the",
"desired",
"select_related",
"string",
"backwards",
"."
] | d557c4253312774a7c2f14bcd02675e9ac2ea05f | https://github.com/jazzband/django-model-utils/blob/d557c4253312774a7c2f14bcd02675e9ac2ea05f/model_utils/managers.py#L176-L200 |
240,962 | 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):
kwargs = {'model': self.model, 'using': self._db}
if hasattr(self, '_hints'):
kwargs['hints'] = self._hints
return self._queryset_class(**kwargs).filter(is_removed=False) | [
"def",
"get_queryset",
"(",
"self",
")",
":",
"kwargs",
"=",
"{",
"'model'",
":",
"self",
".",
"model",
",",
"'using'",
":",
"self",
".",
"_db",
"}",
"if",
"hasattr",
"(",
"self",
",",
"'_hints'",
")",
":",
"kwargs",
"[",
"'hints'",
"]",
"=",
"self... | Return queryset limited to not removed entries. | [
"Return",
"queryset",
"limited",
"to",
"not",
"removed",
"entries",
"."
] | d557c4253312774a7c2f14bcd02675e9ac2ea05f | https://github.com/jazzband/django-model-utils/blob/d557c4253312774a7c2f14bcd02675e9ac2ea05f/model_utils/managers.py#L295-L303 |
240,963 | 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
if self.instance.pk and field in self.deferred_fields and field not in self.saved_data:
# if the field has not been assigned locall... | python | def previous(self, field):
# handle deferred fields that have not yet been loaded from the database
if self.instance.pk and field in self.deferred_fields and field not in self.saved_data:
# if the field has not been assigned locally, simply fetch and un-defer the value
if field ... | [
"def",
"previous",
"(",
"self",
",",
"field",
")",
":",
"# handle deferred fields that have not yet been loaded from the database",
"if",
"self",
".",
"instance",
".",
"pk",
"and",
"field",
"in",
"self",
".",
"deferred_fields",
"and",
"field",
"not",
"in",
"self",
... | Returns currently saved value of given field | [
"Returns",
"currently",
"saved",
"value",
"of",
"given",
"field"
] | d557c4253312774a7c2f14bcd02675e9ac2ea05f | https://github.com/jazzband/django-model-utils/blob/d557c4253312774a7c2f14bcd02675e9ac2ea05f/model_utils/tracker.py#L142-L160 |
240,964 | jazzband/django-model-utils | model_utils/tracker.py | FieldTracker.get_field_map | def get_field_map(self, cls):
"""Returns dict mapping fields names to model attribute names"""
field_map = dict((field, field) for field in self.fields)
all_fields = dict((f.name, f.attname) for f in cls._meta.fields)
field_map.update(**dict((k, v) for (k, v) in all_fields.items()
... | python | def get_field_map(self, cls):
field_map = dict((field, field) for field in self.fields)
all_fields = dict((f.name, f.attname) for f in cls._meta.fields)
field_map.update(**dict((k, v) for (k, v) in all_fields.items()
if k in field_map))
return field_map | [
"def",
"get_field_map",
"(",
"self",
",",
"cls",
")",
":",
"field_map",
"=",
"dict",
"(",
"(",
"field",
",",
"field",
")",
"for",
"field",
"in",
"self",
".",
"fields",
")",
"all_fields",
"=",
"dict",
"(",
"(",
"f",
".",
"name",
",",
"f",
".",
"at... | Returns dict mapping fields names to model attribute names | [
"Returns",
"dict",
"mapping",
"fields",
"names",
"to",
"model",
"attribute",
"names"
] | d557c4253312774a7c2f14bcd02675e9ac2ea05f | https://github.com/jazzband/django-model-utils/blob/d557c4253312774a7c2f14bcd02675e9ac2ea05f/model_utils/tracker.py#L202-L208 |
240,965 | jazzband/django-model-utils | model_utils/models.py | add_status_query_managers | def add_status_query_managers(sender, **kwargs):
"""
Add a Querymanager for each status item dynamically.
"""
if not issubclass(sender, StatusModel):
return
if django.VERSION >= (1, 10):
# First, get current manager name...
default_manager = sender._meta.default_manager
... | python | def add_status_query_managers(sender, **kwargs):
if not issubclass(sender, StatusModel):
return
if django.VERSION >= (1, 10):
# First, get current manager name...
default_manager = sender._meta.default_manager
for value, display in getattr(sender, 'STATUS', ()):
if _field_e... | [
"def",
"add_status_query_managers",
"(",
"sender",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"not",
"issubclass",
"(",
"sender",
",",
"StatusModel",
")",
":",
"return",
"if",
"django",
".",
"VERSION",
">=",
"(",
"1",
",",
"10",
")",
":",
"# First, get cur... | Add a Querymanager for each status item dynamically. | [
"Add",
"a",
"Querymanager",
"for",
"each",
"status",
"item",
"dynamically",
"."
] | d557c4253312774a7c2f14bcd02675e9ac2ea05f | https://github.com/jazzband/django-model-utils/blob/d557c4253312774a7c2f14bcd02675e9ac2ea05f/model_utils/models.py#L60-L83 |
240,966 | jazzband/django-model-utils | model_utils/models.py | add_timeframed_query_manager | def add_timeframed_query_manager(sender, **kwargs):
"""
Add a QueryManager for a specific timeframe.
"""
if not issubclass(sender, TimeFramedModel):
return
if _field_exists(sender, 'timeframed'):
raise ImproperlyConfigured(
"Model '%s' has a field named 'timeframed' "
... | python | def add_timeframed_query_manager(sender, **kwargs):
if not issubclass(sender, TimeFramedModel):
return
if _field_exists(sender, 'timeframed'):
raise ImproperlyConfigured(
"Model '%s' has a field named 'timeframed' "
"which conflicts with the TimeFramedModel manager."
... | [
"def",
"add_timeframed_query_manager",
"(",
"sender",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"not",
"issubclass",
"(",
"sender",
",",
"TimeFramedModel",
")",
":",
"return",
"if",
"_field_exists",
"(",
"sender",
",",
"'timeframed'",
")",
":",
"raise",
"Impr... | Add a QueryManager for a specific timeframe. | [
"Add",
"a",
"QueryManager",
"for",
"a",
"specific",
"timeframe",
"."
] | d557c4253312774a7c2f14bcd02675e9ac2ea05f | https://github.com/jazzband/django-model-utils/blob/d557c4253312774a7c2f14bcd02675e9ac2ea05f/model_utils/models.py#L86-L102 |
240,967 | 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'):
import subprocess
from distutils import spawn
import tempfile
import time
# Check for dependencies. Needs Tesseract and Imagemagick installed.
if not spawn.find_executable('tesseract'):
raise EnvironmentError('tesseract not installed.')
if not spaw... | [
"def",
"to_text",
"(",
"path",
",",
"language",
"=",
"'fra'",
")",
":",
"import",
"subprocess",
"from",
"distutils",
"import",
"spawn",
"import",
"tempfile",
"import",
"time",
"# Check for dependencies. Needs Tesseract and Imagemagick installed.",
"if",
"not",
"spawn",
... | 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 | [
"Wraps",
"Tesseract",
"4",
"OCR",
"with",
"custom",
"language",
"model",
"."
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/input/tesseract4.py#L2-L69 |
240,968 | 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'):
"""OCR with PDF/TIFF as source files on GCS"""
import os
from google.cloud import vision
from google.cloud import storage
from google.protobuf import json_format
# Supported mime_types are: 'application/pdf' and 'image/tiff'
... | [
"def",
"to_text",
"(",
"path",
",",
"bucket_name",
"=",
"'cloud-vision-84893'",
",",
"language",
"=",
"'fr'",
")",
":",
"\"\"\"OCR with PDF/TIFF as source files on GCS\"\"\"",
"import",
"os",
"from",
"google",
".",
"cloud",
"import",
"vision",
"from",
"google",
".",... | 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 invoice in JPG or PNG format
bucket_name : str
name of bucket to us... | [
"Sends",
"PDF",
"files",
"to",
"Google",
"Cloud",
"Vision",
"for",
"OCR",
"."
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/input/gvision.py#L2-L83 |
240,969 | 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):
if path.endswith('.csv'):
filename = path
else:
filename = path + '.csv'
if sys.version_info[0] < 3:
openfile = open(filename, "wb")
else:
openfile = open(filename, "w", newline='')
with openfile as csv_file:
writer = csv.write... | [
"def",
"write_to_file",
"(",
"data",
",",
"path",
")",
":",
"if",
"path",
".",
"endswith",
"(",
"'.csv'",
")",
":",
"filename",
"=",
"path",
"else",
":",
"filename",
"=",
"path",
"+",
"'.csv'",
"if",
"sys",
".",
"version_info",
"[",
"0",
"]",
"<",
... | 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
Notes
----
Do give file na... | [
"Export",
"extracted",
"fields",
"to",
"csv"
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/output/to_csv.py#L5-L54 |
240,970 | invoice-x/invoice2data | src/invoice2data/input/tesseract.py | to_text | def to_text(path):
"""Wraps Tesseract OCR.
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
"""
import subprocess
from distutils import sp... | python | def to_text(path):
import subprocess
from distutils import spawn
# Check for dependencies. Needs Tesseract and Imagemagick installed.
if not spawn.find_executable('tesseract'):
raise EnvironmentError('tesseract not installed.')
if not spawn.find_executable('convert'):
raise Environm... | [
"def",
"to_text",
"(",
"path",
")",
":",
"import",
"subprocess",
"from",
"distutils",
"import",
"spawn",
"# Check for dependencies. Needs Tesseract and Imagemagick installed.",
"if",
"not",
"spawn",
".",
"find_executable",
"(",
"'tesseract'",
")",
":",
"raise",
"Environ... | Wraps Tesseract OCR.
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 | [
"Wraps",
"Tesseract",
"OCR",
"."
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/input/tesseract.py#L4-L38 |
240,971 | 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):
for table in self['tables']:
# First apply default options.
plugin_settings = DEFAULT_OPTIONS.copy()
plugin_settings.update(table)
table = plugin_settings
# Validate settings
assert 'start' in table, 'Table start regex missing'
... | [
"def",
"extract",
"(",
"self",
",",
"content",
",",
"output",
")",
":",
"for",
"table",
"in",
"self",
"[",
"'tables'",
"]",
":",
"# First apply default options.",
"plugin_settings",
"=",
"DEFAULT_OPTIONS",
".",
"copy",
"(",
")",
"plugin_settings",
".",
"update... | Try to extract tables from an invoice | [
"Try",
"to",
"extract",
"tables",
"from",
"an",
"invoice"
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/extract/plugins/tables.py#L11-L56 |
240,972 | 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):
import subprocess
from distutils import spawn # py2 compat
if spawn.find_executable("pdftotext"): # shutil.which('pdftotext'):
out, err = subprocess.Popen(
["pdftotext", '-layout', '-enc', 'UTF-8', path, '-'], stdout=subprocess.PIPE
).communicate()
r... | [
"def",
"to_text",
"(",
"path",
")",
":",
"import",
"subprocess",
"from",
"distutils",
"import",
"spawn",
"# py2 compat",
"if",
"spawn",
".",
"find_executable",
"(",
"\"pdftotext\"",
")",
":",
"# shutil.which('pdftotext'):",
"out",
",",
"err",
"=",
"subprocess",
... | 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 | [
"Wrapper",
"around",
"Poppler",
"pdftotext",
"."
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/input/pdftotext.py#L2-L31 |
240,973 | 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):
# Remove withspace
if self.options['remove_whitespace']:
optimized_str = re.sub(' +', '', extracted_str)
else:
optimized_str = extracted_str
# Remove accents
if self.options['remove_accents']:
optimized_... | [
"def",
"prepare_input",
"(",
"self",
",",
"extracted_str",
")",
":",
"# Remove withspace",
"if",
"self",
".",
"options",
"[",
"'remove_whitespace'",
"]",
":",
"optimized_str",
"=",
"re",
".",
"sub",
"(",
"' +'",
",",
"''",
",",
"extracted_str",
")",
"else",
... | Input raw string and do transformations, as set in template file. | [
"Input",
"raw",
"string",
"and",
"do",
"transformations",
"as",
"set",
"in",
"template",
"file",
"."
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/extract/invoice_template.py#L64-L88 |
240,974 | 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):
if all([keyword in optimized_str for keyword in self['keywords']]):
logger.debug('Matched template %s', self['template_name'])
return True | [
"def",
"matches_input",
"(",
"self",
",",
"optimized_str",
")",
":",
"if",
"all",
"(",
"[",
"keyword",
"in",
"optimized_str",
"for",
"keyword",
"in",
"self",
"[",
"'keywords'",
"]",
"]",
")",
":",
"logger",
".",
"debug",
"(",
"'Matched template %s'",
",",
... | See if string matches keywords set in template file | [
"See",
"if",
"string",
"matches",
"keywords",
"set",
"in",
"template",
"file"
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/extract/invoice_template.py#L90-L95 |
240,975 | 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):
res = dateparser.parse(
value, date_formats=self.options['date_formats'], languages=self.options['languages']
)
logger.debug("result of date parsing=%s", res)
return res | [
"def",
"parse_date",
"(",
"self",
",",
"value",
")",
":",
"res",
"=",
"dateparser",
".",
"parse",
"(",
"value",
",",
"date_formats",
"=",
"self",
".",
"options",
"[",
"'date_formats'",
"]",
",",
"languages",
"=",
"self",
".",
"options",
"[",
"'languages'... | Parses date and returns date after parsing | [
"Parses",
"date",
"and",
"returns",
"date",
"after",
"parsing"
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/extract/invoice_template.py#L108-L114 |
240,976 | 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):
if path.endswith('.json'):
filename = path
else:
filename = path + '.json'
with codecs.open(filename, "w", encoding='utf-8') as json_file:
for line in data:
line['date'] = line['date'].strftime('%d/%m/%Y')
print(type(json))
... | [
"def",
"write_to_file",
"(",
"data",
",",
"path",
")",
":",
"if",
"path",
".",
"endswith",
"(",
"'.json'",
")",
":",
"filename",
"=",
"path",
"else",
":",
"filename",
"=",
"path",
"+",
"'.json'",
"with",
"codecs",
".",
"open",
"(",
"filename",
",",
"... | 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 file
Notes
----
Do give fil... | [
"Export",
"extracted",
"fields",
"to",
"json"
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/output/to_json.py#L12-L47 |
240,977 | 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(
'--input-reader',
choices=input_mapping.keys(),
default='pdftotext',
help='... | python | def create_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(),
default='pdftotext',
help='Choose text extraction function. Def... | [
"def",
"create_parser",
"(",
")",
":",
"parser",
"=",
"argparse",
".",
"ArgumentParser",
"(",
"description",
"=",
"'Extract structured data from PDF files and save to CSV or JSON.'",
")",
"parser",
".",
"add_argument",
"(",
"'--input-reader'",
",",
"choices",
"=",
"inpu... | Returns argument parser | [
"Returns",
"argument",
"parser"
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/main.py#L99-L167 |
240,978 | invoice-x/invoice2data | src/invoice2data/main.py | main | def main(args=None):
"""Take folder or single file and analyze each."""
if args is None:
parser = create_parser()
args = parser.parse_args()
if args.debug:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
input_module = input_ma... | python | def main(args=None):
if args is None:
parser = create_parser()
args = parser.parse_args()
if args.debug:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.INFO)
input_module = input_mapping[args.input_reader]
output_module = output_map... | [
"def",
"main",
"(",
"args",
"=",
"None",
")",
":",
"if",
"args",
"is",
"None",
":",
"parser",
"=",
"create_parser",
"(",
")",
"args",
"=",
"parser",
".",
"parse_args",
"(",
")",
"if",
"args",
".",
"debug",
":",
"logging",
".",
"basicConfig",
"(",
"... | Take folder or single file and analyze each. | [
"Take",
"folder",
"or",
"single",
"file",
"and",
"analyze",
"each",
"."
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/main.py#L170-L215 |
240,979 | 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
... | python | def write_to_file(data, path):
if path.endswith('.xml'):
filename = path
else:
filename = path + '.xml'
tag_data = ET.Element('data')
xml_file = open(filename, "w")
i = 0
for line in data:
i += 1
tag_item = ET.SubElement(tag_data, 'item')
tag_date = ET.Su... | [
"def",
"write_to_file",
"(",
"data",
",",
"path",
")",
":",
"if",
"path",
".",
"endswith",
"(",
"'.xml'",
")",
":",
"filename",
"=",
"path",
"else",
":",
"filename",
"=",
"path",
"+",
"'.xml'",
"tag_data",
"=",
"ET",
".",
"Element",
"(",
"'data'",
")... | 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
Notes
----
Do give file na... | [
"Export",
"extracted",
"fields",
"to",
"xml"
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/output/to_xml.py#L12-L59 |
240,980 | 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):
output = []
if folder is None:
folder = pkg_resources.resource_filename(__name__, 'templates')
for path, subdirs, files in os.walk(folder):
for name in sorted(files):
if name.endswith('.yml'):
with open(os.path.join(path, name), ... | [
"def",
"read_templates",
"(",
"folder",
"=",
"None",
")",
":",
"output",
"=",
"[",
"]",
"if",
"folder",
"is",
"None",
":",
"folder",
"=",
"pkg_resources",
".",
"resource_filename",
"(",
"__name__",
",",
"'templates'",
")",
"for",
"path",
",",
"subdirs",
... | 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
-------
output : Instance of `InvoiceTemplate`
... | [
"Load",
"yaml",
"templates",
"from",
"template",
"folder",
".",
"Return",
"list",
"of",
"dicts",
"."
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/extract/loader.py#L39-L99 |
240,981 | 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):
try:
# python 2
from StringIO import StringIO
import sys
reload(sys) # noqa: F821
sys.setdefaultencoding('utf8')
except ImportError:
from io import StringIO
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
from pd... | [
"def",
"to_text",
"(",
"path",
")",
":",
"try",
":",
"# python 2",
"from",
"StringIO",
"import",
"StringIO",
"import",
"sys",
"reload",
"(",
"sys",
")",
"# noqa: F821",
"sys",
".",
"setdefaultencoding",
"(",
"'utf8'",
")",
"except",
"ImportError",
":",
"from... | Wrapper around `pdfminer`.
Parameters
----------
path : str
path of electronic invoice in PDF
Returns
-------
str : str
returns extracted text from pdf | [
"Wrapper",
"around",
"pdfminer",
"."
] | d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20 | https://github.com/invoice-x/invoice2data/blob/d97fdc5db9c1844fd77fa64f8ea7c42fefd0ba20/src/invoice2data/input/pdfminer_wrapper.py#L2-L57 |
240,982 | SALib/SALib | src/SALib/analyze/ff.py | analyze | def analyze(problem, X, Y, second_order=False, print_to_console=False,
seed=None):
"""Perform a fractional factorial analysis
Returns a dictionary with keys 'ME' (main effect) and 'IE' (interaction
effect). The techniques bulks out the number of parameters with dummy
parameters to the neare... | python | def analyze(problem, X, Y, second_order=False, print_to_console=False,
seed=None):
if seed:
np.random.seed(seed)
problem = extend_bounds(problem)
num_vars = problem['num_vars']
X = generate_contrast(problem)
main_effect = (1. / (2 * num_vars)) * np.dot(Y, X)
Si = ResultDi... | [
"def",
"analyze",
"(",
"problem",
",",
"X",
",",
"Y",
",",
"second_order",
"=",
"False",
",",
"print_to_console",
"=",
"False",
",",
"seed",
"=",
"None",
")",
":",
"if",
"seed",
":",
"np",
".",
"random",
".",
"seed",
"(",
"seed",
")",
"problem",
"=... | Perform a fractional factorial analysis
Returns a dictionary with keys 'ME' (main effect) and 'IE' (interaction
effect). The techniques bulks out the number of parameters with dummy
parameters to the nearest 2**n. Any results involving dummy parameters
could indicate a problem with the model runs.
... | [
"Perform",
"a",
"fractional",
"factorial",
"analysis"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/analyze/ff.py#L18-L81 |
240,983 | SALib/SALib | src/SALib/analyze/ff.py | to_df | 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.
'''
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.
'''
na... | [
"def",
"to_df",
"(",
"self",
")",
":",
"names",
"=",
"self",
"[",
"'names'",
"]",
"main_effect",
"=",
"self",
"[",
"'ME'",
"]",
"interactions",
"=",
"self",
".",
"get",
"(",
"'IE'",
",",
"None",
")",
"inter_effect",
"=",
"None",
"if",
"interactions",
... | 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. | [
"Conversion",
"method",
"to",
"Pandas",
"DataFrame",
".",
"To",
"be",
"attached",
"to",
"ResultDict",
"."
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/analyze/ff.py#L84-L106 |
240,984 | SALib/SALib | src/SALib/analyze/ff.py | interactions | def interactions(problem, Y, print_to_console=False):
"""Computes the second order effects
Computes the second order effects (interactions) between
all combinations of pairs of input factors
Arguments
---------
problem: dict
The problem definition
Y: numpy.array
The NumPy a... | python | def interactions(problem, Y, print_to_console=False):
names = problem['names']
num_vars = problem['num_vars']
X = generate_contrast(problem)
ie_names = []
IE = []
for col in range(X.shape[1]):
for col_2 in range(col):
x = X[:, col] * X[:, col_2]
var_names = (na... | [
"def",
"interactions",
"(",
"problem",
",",
"Y",
",",
"print_to_console",
"=",
"False",
")",
":",
"names",
"=",
"problem",
"[",
"'names'",
"]",
"num_vars",
"=",
"problem",
"[",
"'num_vars'",
"]",
"X",
"=",
"generate_contrast",
"(",
"problem",
")",
"ie_name... | Computes the second order effects
Computes the second order effects (interactions) between
all combinations of pairs of input factors
Arguments
---------
problem: dict
The problem definition
Y: numpy.array
The NumPy array containing the model outputs
print_to_console: bool,... | [
"Computes",
"the",
"second",
"order",
"effects"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/analyze/ff.py#L109-L150 |
240,985 | SALib/SALib | src/SALib/util/__init__.py | avail_approaches | def avail_approaches(pkg):
'''Create list of available modules.
Arguments
---------
pkg : module
module to inspect
Returns
---------
method : list
A list of available submodules
'''
methods = [modname for importer, modname, ispkg in
pkgutil.walk_packa... | python | def avail_approaches(pkg):
'''Create list of available modules.
Arguments
---------
pkg : module
module to inspect
Returns
---------
method : list
A list of available submodules
'''
methods = [modname for importer, modname, ispkg in
pkgutil.walk_packa... | [
"def",
"avail_approaches",
"(",
"pkg",
")",
":",
"methods",
"=",
"[",
"modname",
"for",
"importer",
",",
"modname",
",",
"ispkg",
"in",
"pkgutil",
".",
"walk_packages",
"(",
"path",
"=",
"pkg",
".",
"__path__",
")",
"if",
"modname",
"not",
"in",
"[",
"... | Create list of available modules.
Arguments
---------
pkg : module
module to inspect
Returns
---------
method : list
A list of available submodules | [
"Create",
"list",
"of",
"available",
"modules",
"."
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/util/__init__.py#L18-L35 |
240,986 | SALib/SALib | src/SALib/util/__init__.py | scale_samples | def scale_samples(params, bounds):
'''Rescale samples in 0-to-1 range to arbitrary bounds
Arguments
---------
bounds : list
list of lists of dimensions `num_params`-by-2
params : numpy.ndarray
numpy array of dimensions `num_params`-by-:math:`N`,
where :math:`N` is the number... | python | def scale_samples(params, bounds):
'''Rescale samples in 0-to-1 range to arbitrary bounds
Arguments
---------
bounds : list
list of lists of dimensions `num_params`-by-2
params : numpy.ndarray
numpy array of dimensions `num_params`-by-:math:`N`,
where :math:`N` is the number... | [
"def",
"scale_samples",
"(",
"params",
",",
"bounds",
")",
":",
"# Check bounds are legal (upper bound is greater than lower bound)",
"b",
"=",
"np",
".",
"array",
"(",
"bounds",
")",
"lower_bounds",
"=",
"b",
"[",
":",
",",
"0",
"]",
"upper_bounds",
"=",
"b",
... | Rescale samples in 0-to-1 range to arbitrary bounds
Arguments
---------
bounds : list
list of lists of dimensions `num_params`-by-2
params : numpy.ndarray
numpy array of dimensions `num_params`-by-:math:`N`,
where :math:`N` is the number of samples | [
"Rescale",
"samples",
"in",
"0",
"-",
"to",
"-",
"1",
"range",
"to",
"arbitrary",
"bounds"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/util/__init__.py#L38-L65 |
240,987 | 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):
b = np.array(bounds)
# initializing matrix for converted values
conv_params = np.zeros_like(params)
# loop over the parameters
for i in range(conv_params.shape[1]):
# setting first and second arguments for distributions
b1 = b[i]... | [
"def",
"nonuniform_scale_samples",
"(",
"params",
",",
"bounds",
",",
"dists",
")",
":",
"b",
"=",
"np",
".",
"array",
"(",
"bounds",
")",
"# initializing matrix for converted values",
"conv_params",
"=",
"np",
".",
"zeros_like",
"(",
"params",
")",
"# loop over... | 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 samples
dists : list
list of distributions, one fo... | [
"Rescale",
"samples",
"in",
"0",
"-",
"to",
"-",
"1",
"range",
"to",
"other",
"distributions"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/util/__init__.py#L96-L165 |
240,988 | SALib/SALib | src/SALib/util/__init__.py | read_param_file | 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)
Returns a dictionary containing:
... | python | def read_param_file(filename, delimiter=None):
names = []
bounds = []
groups = []
dists = []
num_vars = 0
fieldnames = ['name', 'lower_bound', 'upper_bound', 'group', 'dist']
with open(filename, 'rU') as csvfile:
dialect = csv.Sniffer().sniff(csvfile.read(1024), delimiters=delimiter... | [
"def",
"read_param_file",
"(",
"filename",
",",
"delimiter",
"=",
"None",
")",
":",
"names",
"=",
"[",
"]",
"bounds",
"=",
"[",
"]",
"groups",
"=",
"[",
"]",
"dists",
"=",
"[",
"]",
"num_vars",
"=",
"0",
"fieldnames",
"=",
"[",
"'name'",
",",
"'low... | 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)
Returns a dictionary containing:
- names - the names of the parameters
- bou... | [
"Unpacks",
"a",
"parameter",
"file",
"into",
"a",
"dictionary"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/util/__init__.py#L168-L245 |
240,989 | SALib/SALib | src/SALib/util/__init__.py | compute_groups_matrix | def compute_groups_matrix(groups):
"""Generate matrix which notes factor membership of groups
Computes a k-by-g matrix which notes factor membership of groups
where:
k is the number of variables (factors)
g is the number of groups
Also returns a g-length list of unique group_names whose... | python | def compute_groups_matrix(groups):
if not groups:
return None
num_vars = len(groups)
# Get a unique set of the group names
unique_group_names = list(OrderedDict.fromkeys(groups))
number_of_groups = len(unique_group_names)
indices = dict([(x, i) for (i, x) in enumerate(unique_group_nam... | [
"def",
"compute_groups_matrix",
"(",
"groups",
")",
":",
"if",
"not",
"groups",
":",
"return",
"None",
"num_vars",
"=",
"len",
"(",
"groups",
")",
"# Get a unique set of the group names",
"unique_group_names",
"=",
"list",
"(",
"OrderedDict",
".",
"fromkeys",
"(",... | Generate matrix which notes factor membership of groups
Computes a k-by-g matrix which notes factor membership of groups
where:
k is the number of variables (factors)
g is the number of groups
Also returns a g-length list of unique group_names whose positions
correspond to the order of ... | [
"Generate",
"matrix",
"which",
"notes",
"factor",
"membership",
"of",
"groups"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/util/__init__.py#L248-L286 |
240,990 | 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,
using brut... | [
"def",
"requires_gurobipy",
"(",
"_has_gurobi",
")",
":",
"def",
"_outer_wrapper",
"(",
"wrapped_function",
")",
":",
"def",
"_wrapper",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"_has_gurobi",
":",
"result",
"=",
"wrapped_function",
"(",
... | 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 brute force options as preference. | [
"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",
... | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/util/__init__.py#L289-L306 |
240,991 | 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(... | [
"def",
"compute_grouped_sigma",
"(",
"ungrouped_sigma",
",",
"group_matrix",
")",
":",
"group_matrix",
"=",
"np",
".",
"array",
"(",
"group_matrix",
",",
"dtype",
"=",
"np",
".",
"bool",
")",
"sigma_masked",
"=",
"np",
".",
"ma",
".",
"masked_array",
"(",
... | Returns sigma for the groups of parameter values in the
argument ungrouped_metric where the group consists of no more than
one parameter | [
"Returns",
"sigma",
"for",
"the",
"groups",
"of",
"parameter",
"values",
"in",
"the",
"argument",
"ungrouped_metric",
"where",
"the",
"group",
"consists",
"of",
"no",
"more",
"than",
"one",
"parameter"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/analyze/morris.py#L170-L186 |
240,992 | 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,
... | [
"def",
"compute_grouped_metric",
"(",
"ungrouped_metric",
",",
"group_matrix",
")",
":",
"group_matrix",
"=",
"np",
".",
"array",
"(",
"group_matrix",
",",
"dtype",
"=",
"np",
".",
"bool",
")",
"mu_star_masked",
"=",
"np",
".",
"ma",
".",
"masked_array",
"("... | Computes the mean value for the groups of parameter values in the
argument ungrouped_metric | [
"Computes",
"the",
"mean",
"value",
"for",
"the",
"groups",
"of",
"parameter",
"values",
"in",
"the",
"argument",
"ungrouped_metric"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/analyze/morris.py#L189-L201 |
240,993 | 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.
This resample is used to produce a confidence interval.
... | [
"def",
"compute_mu_star_confidence",
"(",
"ee",
",",
"num_trajectories",
",",
"num_resamples",
",",
"conf_level",
")",
":",
"ee_resampled",
"=",
"np",
".",
"zeros",
"(",
"[",
"num_trajectories",
"]",
")",
"mu_star_resampled",
"=",
"np",
".",
"zeros",
"(",
"[",... | 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. | [
"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",
"i... | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/analyze/morris.py#L261-L280 |
240,994 | 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):
string = "_v%s_l%s_gs%s_k%s_N%s_%s.txt" % (num_params,
p_levels,
k_choices,
N,
... | [
"def",
"timestamp",
"(",
"num_params",
",",
"p_levels",
",",
"k_choices",
",",
"N",
")",
":",
"string",
"=",
"\"_v%s_l%s_gs%s_k%s_N%s_%s.txt\"",
"%",
"(",
"num_params",
",",
"p_levels",
",",
"k_choices",
",",
"N",
",",
"dt",
".",
"strftime",
"(",
"dt",
"."... | Returns a uniform timestamp with parameter values for file identification | [
"Returns",
"a",
"uniform",
"timestamp",
"with",
"parameter",
"values",
"for",
"file",
"identification"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/sample/morris/gurobi.py#L131-L141 |
240,995 | 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):
scores = self.find_most_distant(input_sample,
num_samples,
num_... | [
"def",
"brute_force_most_distant",
"(",
"self",
",",
"input_sample",
",",
"num_samples",
",",
"num_params",
",",
"k_choices",
",",
"num_groups",
"=",
"None",
")",
":",
"scores",
"=",
"self",
".",
"find_most_distant",
"(",
"input_sample",
",",
"num_samples",
",",... | Use brute force method to find most distant trajectories
Arguments
---------
input_sample : numpy.ndarray
num_samples : int
The number of samples to generate
num_params : int
The number of parameters
k_choices : int
The number of optim... | [
"Use",
"brute",
"force",
"method",
"to",
"find",
"most",
"distant",
"trajectories"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/sample/morris/brute.py#L19-L48 |
240,996 | 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):
# Now evaluate the (N choose k_choices) possible combinations
if nchoosek(num_samples, k_choices) >= sys.maxsize:
raise ValueError("Number of combinations is too large")
... | [
"def",
"find_most_distant",
"(",
"self",
",",
"input_sample",
",",
"num_samples",
",",
"num_params",
",",
"k_choices",
",",
"num_groups",
"=",
"None",
")",
":",
"# Now evaluate the (N choose k_choices) possible combinations",
"if",
"nchoosek",
"(",
"num_samples",
",",
... | Finds the 'k_choices' most distant choices from the
'num_samples' trajectories contained in 'input_sample'
Arguments
---------
input_sample : numpy.ndarray
num_samples : int
The number of samples to generate
num_params : int
The number of paramete... | [
"Finds",
"the",
"k_choices",
"most",
"distant",
"choices",
"from",
"the",
"num_samples",
"trajectories",
"contained",
"in",
"input_sample"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/sample/morris/brute.py#L50-L101 |
240,997 | 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
... | [
"def",
"mappable",
"(",
"combos",
",",
"pairwise",
",",
"distance_matrix",
")",
":",
"combos",
"=",
"np",
".",
"array",
"(",
"combos",
")",
"# Create a list of all pairwise combination for each combo in combos",
"combo_list",
"=",
"combos",
"[",
":",
",",
"pairwise"... | 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 | [
"Obtains",
"scores",
"from",
"the",
"distance_matrix",
"for",
"each",
"pairwise",
"combination",
"held",
"in",
"the",
"combos",
"array"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/sample/morris/brute.py#L113-L133 |
240,998 | 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):
if not isinstance(scores, np.ndarray):
raise TypeError("Scores input is not a numpy array")
index_of_maximum = int(scores.argmax())
maximum_combo = self.nth(combinations(
list(range(N)), k_choices), index_of_maximum, None)
... | [
"def",
"find_maximum",
"(",
"self",
",",
"scores",
",",
"N",
",",
"k_choices",
")",
":",
"if",
"not",
"isinstance",
"(",
"scores",
",",
"np",
".",
"ndarray",
")",
":",
"raise",
"TypeError",
"(",
"\"Scores input is not a numpy array\"",
")",
"index_of_maximum",... | Finds the `k_choices` maximum scores from `scores`
Arguments
---------
scores : numpy.ndarray
N : int
k_choices : int
Returns
-------
list | [
"Finds",
"the",
"k_choices",
"maximum",
"scores",
"from",
"scores"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/sample/morris/brute.py#L135-L154 |
240,999 | 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
"""
if type(n) != int:
raise TypeError("n is not an ... | python | def nth(iterable, n, default=None):
if type(n) != int:
raise TypeError("n is not an integer")
return next(islice(iterable, n, None), default) | [
"def",
"nth",
"(",
"iterable",
",",
"n",
",",
"default",
"=",
"None",
")",
":",
"if",
"type",
"(",
"n",
")",
"!=",
"int",
":",
"raise",
"TypeError",
"(",
"\"n is not an integer\"",
")",
"return",
"next",
"(",
"islice",
"(",
"iterable",
",",
"n",
",",... | Returns the nth item or a default value
Arguments
---------
iterable : iterable
n : int
default : default=None
The default value to return | [
"Returns",
"the",
"nth",
"item",
"or",
"a",
"default",
"value"
] | 9744d73bb17cfcffc8282c7dc4a727efdc4bea3f | https://github.com/SALib/SALib/blob/9744d73bb17cfcffc8282c7dc4a727efdc4bea3f/src/SALib/sample/morris/brute.py#L157-L171 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.