/dports/editors/cooledit/cooledit-3.17.28/syntax/ |
H A D | latex.syntax | 10 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 [all …]
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/dports/misc/orange3/orange3-3.29.1/conda-recipe/ |
H A D | meta.yaml | 73 - 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 [all …]
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/dports/misc/orange3/orange3-3.29.1/doc/data-mining-library/source/reference/ |
H A D | evaluation.cd.rst | 1 .. 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 [all …]
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H A D | preprocess.rst | 1 .. 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 [all …]
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H A D | data.variable.rst | 1 .. 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( [all …]
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H A D | regression.rst | 5 .. 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 [all …]
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H A D | data.rst | 1 .. 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 [all …]
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H A D | data.storage.rst | 1 .. 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` [all …]
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/dports/misc/dartsim/dart-6.11.1/unittests/unit/ |
H A D | test_Factory.cpp | 52 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() [all …]
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/dports/devel/R-cran-broom/broom/inst/doc/ |
H A D | broom_and_dplyr.R | 15 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) [all …]
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/report/tests/ |
H A D | test_report.py | 8 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 [all …]
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/dports/misc/orange3/orange3-3.29.1/Orange/tests/ |
H A D | test_data_util.py | 6 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) [all …]
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H A D | test_simple_tree.py | 9 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) [all …]
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/dports/misc/orange3/orange3-3.29.1/doc/data-mining-library/source/tutorial/code/ |
H A D | regression-cv.py | 1 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)
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H A D | classification-cv2.py | 1 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)))
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H A D | classification-cv3.py | 1 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))
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H A D | data-domain-numpy.py | 1 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)
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H A D | regression-other.py | 1 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()
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H A D | classification-cv.py | 1 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])
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/dports/misc/orange3/orange3-3.29.1/ |
H A D | README.pypi | 1 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: [all …]
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H A D | MANIFEST.in | 2 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
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/dports/textproc/p5-Spreadsheet-WriteExcelXML/Spreadsheet-WriteExcelXML-0.15/examples/ |
H A D | autofilter.pl | 89 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 [all …]
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/evaluate/tests/ |
H A D | test_owrocanalysis.py | 10 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( [all …]
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/dports/security/signify/outils-0.10/src/usr.bin/calendar/calendars/ |
H A D | calendar.discord | 14 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. [all …]
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/dports/biology/py-orange3-bioinformatics/Orange3-Bioinformatics-4.3.1/doc/ |
H A D | index.rst | 17 **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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