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/dports/editors/cooledit/cooledit-3.17.28/syntax/
H A Dlatex.syntax10 keyword \\} yellow/Orange
11 keyword \\{ yellow/Orange
17 keyword whole \\item yellow/Orange
31 keyword \\$ yellow/Orange
34 keyword whole \\tiny yellow/Orange
47 keyword whole \\dag yellow/Orange
49 keyword whole \\S yellow/Orange
50 keyword whole \\P yellow/Orange
74 keyword whole \\\\ yellow/Orange
77 keyword \\% yellow/Orange
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/dports/misc/orange3/orange3-3.29.1/conda-recipe/
H A Dmeta.yaml73 - Orange
76 - Orange.widgets
84 - Orange.clustering
85 - Orange.data
86 - Orange.distance
87 - Orange.evaluation
88 - Orange.modelling
89 - Orange.preprocess
90 - Orange.projection
91 - Orange.regression
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/dports/misc/orange3/orange3-3.29.1/doc/data-mining-library/source/reference/
H A Devaluation.cd.rst1 .. py:currentmodule:: Orange.evaluation.scoring
11 .. autofunction:: Orange.evaluation.CA
18 .. autofunction:: Orange.evaluation.Precision
25 .. autofunction:: Orange.evaluation.Recall
32 .. autofunction:: Orange.evaluation.F1
46 .. autofunction:: Orange.evaluation.AUC
53 .. autofunction:: Orange.evaluation.LogLoss
60 .. autofunction:: Orange.evaluation.MSE
67 .. autofunction:: Orange.evaluation.MAE
74 .. autofunction:: Orange.evaluation.R2
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H A Dpreprocess.rst1 .. py:currentmodule:: Orange.preprocess
27 .. autoclass::Orange.preprocess.Impute
59 .. autoclass::Orange.preprocess.Discretize
75 import Orange
76 data = Orange.data.Table("iris.tab")
116 .. class:: Orange.preprocess.Continuize
131 import Orange
290 .. autoclass:: Orange.preprocess.Normalize
296 .. autoclass:: Orange.preprocess.Randomize
302 .. autoclass:: Orange.preprocess.Remove
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H A Ddata.variable.rst1 .. currentmodule:: Orange.data
22 >>> from Orange.data import Table
62 :obj:`~Orange.data.DiscreteVariable`::
148 Derived variables in Orange
157 >>> data = Orange.data.Table("iris")
176 >>> Orange.evaluation.scoring.CA(res)[0]
179 >>> Orange.evaluation.scoring.CA(res)[0]
204 >>> iris = Orange.data.Table("iris")
257 iris = Orange.data.Table("iris")
272 Orange.data.ContinuousVariable(
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H A Dregression.rst5 .. automodule:: Orange.regression
25 >>> mpg = Orange.data.Table('auto-mpg')
36 .. autoclass:: Orange.regression.linear.RidgeRegressionLearner
38 .. autoclass:: Orange.regression.linear.SGDRegressionLearner
39 .. autoclass:: Orange.regression.linear.LinearModel
53 .. autoclass:: Orange.regression.linear.PolynomialLearner
69 >>> from Orange.data import Table
70 >>> from Orange.regression import MeanLearner
134 .. automodule:: Orange.regression.gb
139 .. automodule:: Orange.regression.catgb
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H A Ddata.rst1 .. currentmodule:: Orange.data
7 Orange stores data in :obj:`Orange.data.Storage` classes. The most commonly used
8 storage is :obj:`Orange.data.Table`, which stores all data in two-dimensional
12 :obj:`Orange.data.Instance`. Different storage classes may derive subclasses
13 of :obj:`~Orange.data.Instance` to represent the retrieved rows in the data
15 instance. For example, if `table` is :obj:`Orange.data.Table`, `table[0]`
16 returns the row as :obj:`Orange.data.RowInstance`.
22 (:obj:`Orange.data.ContinuousVariable`), discrete variables
23 (:obj:`Orange.data.DiscreteVariable`) and string variables
24 (:obj:`Orange.data.StringVariable`). These descriptors contain the
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H A Ddata.storage.rst1 .. currentmodule:: Orange.data.storage
65 of :obj:`~Orange.data.Value`.
99 :obj:`Orange.data.filter`. Methods in :obj:`Orange.data.filter` also provide
111 :rtype: Orange.data.storage.Storage
122 :rtype: Orange.data.storage.Storage
136 :rtype: Orange.data.storage.Storage
144 :type filter: Orange.data.Filter
146 :rtype: Orange.data.storage.Storage
154 within :obj:`Orange.statistics`.
166 :obj:`Orange.data.Variable`
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/dports/misc/dartsim/dart-6.11.1/unittests/unit/
H A Dtest_Factory.cpp52 Orange enumerator
133 class Orange : public Fruit class
141 static RegistrarEnum<Orange> mRegistrarEnum;
146 Orange::RegistrarEnum<Orange> Orange::mRegistrarEnum{OT_Orange};
147 Orange::RegistrarEnumClass<Orange> Orange::mRegistrarEnumClass{
148 ObjectTypeEnumClass::Orange};
149 Orange::RegistrarString<Orange> Orange::mRegistrarString{"orange"};
276 ObjectTypeEnumClass::Orange); in TEST()
281 ->create(ObjectTypeEnumClass::Orange) in TEST()
311 static std::shared_ptr<Orange> createOrange() in createOrange()
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/dports/devel/R-cran-broom/broom/inst/doc/
H A Dbroom_and_dplyr.R15 data(Orange)
17 Orange <- as_tibble(Orange) globalVar
18 Orange
21 cor(Orange$age, Orange$circumference)
23 ggplot(Orange, aes(age, circumference, color = Tree)) +
27 Orange %>%
32 ct <- cor.test(Orange$age, Orange$circumference)
39 nested <- Orange %>%
54 Orange %>%
63 lm_fit <- lm(age ~ circumference, data = Orange)
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/report/tests/
H A Dtest_report.py8 from Orange.data.table import Table
9 from Orange.classification import LogisticRegressionLearner
10 from Orange.classification.tree import TreeLearner
11 from Orange.evaluation import CrossValidation
12 from Orange.distance import Euclidean
13 from Orange.widgets.report.owreport import OWReport
14 from Orange.widgets.widget import OWWidget
15 from Orange.widgets.tests.base import WidgetTest
25 from Orange.widgets.unsupervised.owkmeans import OWKMeans
26 from Orange.widgets.unsupervised.owmds import OWMDS
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/dports/misc/orange3/orange3-3.29.1/Orange/tests/
H A Dtest_data_util.py6 from Orange.data.util import scale, one_hot, SharedComputeValue
7 import Orange
31 class DummyTable(Orange.data.Table):
38 data = Orange.data.Table("iris")
47 data = Orange.data.Table("iris")
48 domain = Orange.data.Domain([Orange.data.ContinuousVariable("cv", compute_value=obj)])
49 data1 = Orange.data.Table.from_table(domain, data)[:10]
56 data = Orange.data.Table("iris")[45:55] # two classes
57 domain = Orange.data.Domain([at.copy(compute_value=obj)
61 Orange.data.Table.from_table(domain, data)
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H A Dtest_simple_tree.py9 import Orange
10 from Orange.classification import SimpleTreeLearner as SimpleTreeCls
11 from Orange.regression import SimpleTreeLearner as SimpleTreeReg
12 from Orange.data import ContinuousVariable, Domain, DiscreteVariable, Table
13 from Orange.tests import test_filename
33 di = [Orange.data.domain.DiscreteVariable(
35 df = [Orange.data.domain.ContinuousVariable(
38 dreg = Orange.data.domain.ContinuousVariable('yr')
39 domain_cls = Orange.data.domain.Domain(di + df, dcls)
40 domain_reg = Orange.data.domain.Domain(di + df, dreg)
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/dports/misc/orange3/orange3-3.29.1/doc/data-mining-library/source/tutorial/code/
H A Dregression-cv.py1 import Orange
3 data = Orange.data.Table("housing.tab")
5 lin = Orange.regression.linear.LinearRegressionLearner()
6 rf = Orange.regression.random_forest.RandomForestRegressionLearner()
8 ridge = Orange.regression.RidgeRegressionLearner()
9 mean = Orange.regression.MeanLearner()
13 res = Orange.evaluation.CrossValidation(data, learners, k=5)
14 rmse = Orange.evaluation.RMSE(res)
15 r2 = Orange.evaluation.R2(res)
H A Dclassification-cv2.py1 import Orange
3 data = Orange.data.Table("titanic")
4 tree = Orange.classification.tree.TreeLearner(max_depth=3)
5 knn = Orange.classification.knn.KNNLearner(n_neighbors=3)
6 lr = Orange.classification.LogisticRegressionLearner(C=0.1)
10 res = Orange.evaluation.CrossValidation(data, learners, k=5)
11 print("Accuracy %s" % " ".join("%.2f" % s for s in Orange.evaluation.CA(res)))
12 print("AUC %s" % " ".join("%.2f" % s for s in Orange.evaluation.AUC(res)))
H A Dclassification-cv3.py1 import Orange
3 data = Orange.data.Table("voting")
4 lr = Orange.classification.LogisticRegressionLearner()
5 rf = Orange.classification.RandomForestLearner(n_estimators=100)
6 res = Orange.evaluation.CrossValidation(data, [lr, rf], k=5)
8 print("Accuracy:", Orange.evaluation.scoring.CA(res))
9 print("AUC:", Orange.evaluation.scoring.AUC(res))
H A Ddata-domain-numpy.py1 import Orange
4 size = Orange.data.DiscreteVariable("size", ["small", "big"])
5 height = Orange.data.ContinuousVariable("height")
6 shape = Orange.data.DiscreteVariable("shape", ["circle", "square", "oval"])
7 speed = Orange.data.ContinuousVariable("speed")
9 domain = Orange.data.Domain([size, height, shape], speed)
14 data = Orange.data.Table(domain, X, Y)
H A Dregression-other.py1 import Orange
5 data = Orange.data.Table("housing")
6 test = Orange.data.Table(data.domain, random.sample(data, 5))
7 train = Orange.data.Table(data.domain, [d for d in data if d not in test])
9 lin = Orange.regression.linear.LinearRegressionLearner()
10 rf = Orange.regression.random_forest.RandomForestRegressionLearner()
12 ridge = Orange.regression.RidgeRegressionLearner()
H A Dclassification-cv.py1 import Orange
3 data = Orange.data.Table("titanic")
4 lr = Orange.classification.LogisticRegressionLearner()
5 res = Orange.evaluation.CrossValidation(data, [lr], k=5)
6 print("Accuracy: %.3f" % Orange.evaluation.scoring.CA(res)[0])
7 print("AUC: %.3f" % Orange.evaluation.scoring.AUC(res)[0])
/dports/misc/orange3/orange3-3.29.1/
H A DREADME.pypi1 Orange 3
4 [Orange] is a component-based data mining software. It includes a range of data
9 This is the latest version of Orange (for Python 3). The deprecated version of
10 Orange 2.7 (for Python 2.7) is still available ([binaries] and [sources]).
12 [Orange]: https://orange.biolab.si/
19 To install Orange with pip, run the following.
24 # Create a separate Python environment for Orange and its dependencies ...
29 # Install Orange
32 Starting Orange GUI
35 To start Orange GUI from the command line, run:
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H A DMANIFEST.in2 recursive-include Orange *.pyx *.py *.c *.cpp README* LICENSE
3 recursive-include Orange/datasets *.tab *.basket *.info *.dst *.metadata
5 recursive-include Orange/tests *.tab *.basket *.xlsx *.xls *.pkl *.pkl.gz
7 recursive-include Orange/canvas *ico *.png *.svg *.ico
8 recursive-include Orange/canvas/workflows *.ows
10 recursive-include Orange/widgets *.png *.svg *.js *.css *.html
11 recursive-include Orange/widgets/tests *.tab
12 recursive-include Orange/widgets/data/tests *.tab *.txt
13 recursive-include Orange/widgets/tests/workflows *.ows
/dports/textproc/p5-Spreadsheet-WriteExcelXML/Spreadsheet-WriteExcelXML-0.15/examples/
H A Dautofilter.pl89 South Orange 9000 September
94 West Orange 1000 December
98 South Orange 3000 May
110 North Orange 7000 July
114 South Orange 10000 November
116 North Orange 5000 August
117 East Orange 1000 November
118 East Orange 4000 October
125 South Orange 8000 March
127 South Orange 5000 July
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/evaluate/tests/
H A Dtest_owrocanalysis.py10 from Orange.data import Table
11 import Orange.evaluation
12 import Orange.classification
14 from Orange.widgets.evaluate import owrocanalysis
17 from Orange.widgets.tests.base import WidgetTest
19 from Orange.tests import test_filename
24 data = Orange.data.Table("iris")
28 Orange.classification.TreeLearner()
46 loo = Orange.evaluation.LeaveOneOut()
128 res = Orange.evaluation.Results(
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/dports/security/signify/outils-0.10/src/usr.bin/calendar/calendars/
H A Dcalendar.discord14 01/05 Setting Orange, day 5 in the season of Chaos, 3172.
20 01/10 Setting Orange, day 10 in the season of Chaos, 3172.
25 01/15 Setting Orange, day 15 in the season of Chaos, 3172.
30 01/20 Setting Orange, day 20 in the season of Chaos, 3172.
35 01/25 Setting Orange, day 25 in the season of Chaos, 3172.
40 01/30 Setting Orange, day 30 in the season of Chaos, 3172.
45 02/04 Setting Orange, day 35 in the season of Chaos, 3172.
50 02/09 Setting Orange, day 40 in the season of Chaos, 3172.
55 02/14 Setting Orange, day 45 in the season of Chaos, 3172.
60 02/19 Setting Orange, day 50 in the season of Chaos, 3172.
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/dports/biology/py-orange3-bioinformatics/Orange3-Bioinformatics-4.3.1/doc/
H A Dindex.rst17 **Bioinformatics** add-on for Orange data mining software package.
23 Orange Bioinformatics extends Orange, a data mining software package,
26 interface (Orange Canvas). The latter is also suitable for
29 In Orange Canvas the analyst connects basic computational units, called
32 like Taverna, Orange widgets are high-level, integrated potentially
40 Orange Bioinformatics provides access to publicly available data, like
43 Orange data mining framework.
54 To register this add-on with Orange, but keep the code in the
74 Orange. To run Orange from the terminal, use
78 python3 -m Orange.canvas

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