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Marko Toplak committed 67c08de

Removed obsolete files from Orange/doc (old reference, tutorial, widget documentation, web page properties, a copy of GPL).

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Files changed (877)

Orange/doc/modules/K5.net

-### This file was generated with Qt Graph Drawer ### 
-
-
-*Network "Qt random undirected graph" 
-
-*Vertices 5
-1 
-2 
-3 
-4 
-5 
-*Edges 
-1 2 1
-1 3 1
-1 4 1
-1 5 1
-2 3 1
-2 4 1
-2 5 1
-3 4 1
-3 5 1
-4 5 1
-*Arcs 
-

Orange/doc/modules/PCA1.py

-# Description: PCA with attribute and row selection
-# Category:    projection
-# Uses:        iris
-# Referenced:  orngPCA.htm
-# Classes:     orngPCA.PCA
-
-import orange, orngPCA
-
-data = orange.ExampleTable("iris.tab")
-
-pca = orngPCA.PCA(data, standardize = True)
-print "PCA on all data:"
-print pca
-
-attributes = ['sepal length', 'sepal width', 'petal length', 'petal width']
-pca = orngPCA.PCA(data, standardize = True, attributes = attributes)
-print "PCA on attributes sepal.length, sepal.width, petal.length, petal.width:"
-print pca
-
-rows = [1, 0] * (len(data) / 2)
-pca = orngPCA.PCA(data, standardize = True, rows = rows)
-print "PCA on every second row:"
-print pca

Orange/doc/modules/PCA2.py

-# Description: using your own imputer and continuizer in PCA
-# Category:    projection
-# Uses:        adult_sample
-# Referenced:  orngPCA.htm
-# Classes:     orngPCA.PCA
-
-import orange, orngPCA
-
-data = orange.ExampleTable("bridges.tab")
-
-imputer = orange.ImputerConstructor_maximal
-
-continuizer = orange.DomainContinuizer()
-continuizer.multinomialTreatment = continuizer.AsNormalizedOrdinal
-continuizer.classTreatment = continuizer.Ignore
-continuizer.continuousTreatment = continuizer.Leave
-
-pca = orngPCA.PCA(data, standardize = True, imputer = imputer, continuizer = continuizer)
-print pca

Orange/doc/modules/PCA3.py

-# Description: setting number of retained components and variance covered, using generalized eigenvectors
-# Category:    projection
-# Uses:        iris
-# Referenced:  orngPCA.htm
-# Classes:     orngPCA.PCA
-
-import orange, orngPCA
-
-data = orange.ExampleTable("iris.tab")
-
-attributes = ['sepal length', 'sepal width', 'petal length', 'petal width']
-pca = orngPCA.PCA(data, standardize = True, attributes = attributes,
-          maxNumberOfComponents = -1, varianceCovered = 1.0)
-print "Retain all vectors and full variance:"
-print pca
-
-pca = orngPCA.PCA(data, standardize = True, maxNumberOfComponents = -1,
-                  varianceCovered = 1.0, useGeneralizedVectors = 1)
-print "As above, only with generalized vectors:"
-print pca
-

Orange/doc/modules/PCA4.py

-# Description: projecting data with trained PCA
-# Category:    projection
-# Uses:        iris
-# Referenced:  orngPCA.htm
-# Classes:     orngPCA.PCA
-
-import orange, orngPCA
-
-data = orange.ExampleTable("iris.tab")
-
-attributes = ['sepal length', 'sepal width', 'petal length', 'petal width']
-pca = orngPCA.PCA(data, attributes = attributes, standardize = True)
-
-projected = pca(data)
-print "Projection on first two components:"
-for d in projected[:5]:
-    print d

Orange/doc/modules/PCA5.py

-# Description: Screeplot and biplot for PCA
-# Category:    projection
-# Uses:        iris
-# Referenced:  orngPCA.htm
-# Classes:     orngPCA.PCA
-
-import orange, orngPCA
-
-data = orange.ExampleTable("iris.tab")
-
-attributes = ['sepal length', 'sepal width', 'petal length', 'petal width']
-pca = orngPCA.PCA(data, standardize = True, attributes = attributes)
-
-pca.plot(filename = None)
-
-pca(data)
-pca.biplot()

Orange/doc/modules/abcn2-rules.py

-# Description: Demonstrates the use of orngABCN2 rules
-# Category:    classification, rules
-# Classes:     ABCN2, ABCN2Ordered, ABCN2M
-# Uses:        titanic.tab
-
-import orange
-import orngABCN2
-import orngCN2
-data = orange.ExampleTable("titanic.tab")
-
-# create learner
-learner = orngABCN2.ABCN2()
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)
-print "*****"
-
-
-learner = orngABCN2.ABCN2Ordered()
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)
-print "*****"
-
-
-learner = orngABCN2.ABCN2M()
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)
-print "*****"

Orange/doc/modules/bayes.py

-import orange, orngBayes, orngTest, orngStat
-
-data = orange.ExampleTable("lung-cancer")
-
-bayes = orngBayes.BayesLearner()
-bayes_m = orngBayes.BayesLearner(m=2)
-
-res = orngTest.crossValidation([bayes, bayes_m], data)
-CAs = orngStat.CA(res)
-print
-print "Without m: %5.3f" % CAs[0]
-print "With m=2: %5.3f" % CAs[1]
-
-data = orange.ExampleTable("voting")
-model = orngBayes.BayesLearner(data)
-orngBayes.printModel(model)

Orange/doc/modules/bridges.tab

-IDENTIF	RIVER	LOCATION	ERECTED	PURPOSE	LENGTH	LANES	CLEAR-G	T-OR-D	MATERIAL	SPAN	REL-L	TYPE
-d	d	d	c	d	c	c	d	d	d	d	d	d
-i		i
-E1	M	3	1818	HIGHWAY	?	2	N	THROUGH	WOOD	SHORT	S	WOOD
-E2	A	25	1819	HIGHWAY	1037	2	N	THROUGH	WOOD	SHORT	S	WOOD
-E3	A	39	1829	AQUEDUCT	?	1	N	THROUGH	WOOD	?	S	WOOD
-E5	A	29	1837	HIGHWAY	1000	2	N	THROUGH	WOOD	SHORT	S	WOOD
-E6	M	23	1838	HIGHWAY	?	2	N	THROUGH	WOOD	?	S	WOOD
-E7	A	27	1840	HIGHWAY	990	2	N	THROUGH	WOOD	MEDIUM	S	WOOD
-E8	A	28	1844	AQUEDUCT	1000	1	N	THROUGH	IRON	SHORT	S	SUSPEN
-E9	M	3	1846	HIGHWAY	1500	2	N	THROUGH	IRON	SHORT	S	SUSPEN
-E10	A	39	1848	AQUEDUCT	?	1	N	DECK	WOOD	?	S	WOOD
-E11	A	29	1851	HIGHWAY	1000	2	N	THROUGH	WOOD	MEDIUM	S	WOOD
-E12	A	39	1853	RR	?	2	N	DECK	WOOD	?	S	WOOD
-E14	M	6	1856	HIGHWAY	1200	2	N	THROUGH	WOOD	MEDIUM	S	WOOD
-E13	A	33	1856	HIGHWAY	?	2	N	THROUGH	WOOD	?	S	WOOD
-E15	A	28	1857	RR	?	2	N	THROUGH	WOOD	?	S	WOOD
-E16	A	25	1859	HIGHWAY	1030	2	N	THROUGH	IRON	MEDIUM	S-F	SUSPEN
-E17	M	4	1863	RR	1000	2	N	THROUGH	IRON	MEDIUM	?	SIMPLE-T
-E18	A	28	1864	RR	1200	2	N	THROUGH	IRON	SHORT	S	SIMPLE-T
-E19	A	29	1866	HIGHWAY	1000	2	N	THROUGH	WOOD	MEDIUM	S	WOOD
-E20	A	32	1870	HIGHWAY	1000	2	N	THROUGH	WOOD	MEDIUM	S	WOOD
-E21	M	16	1874	RR	?	2	?	THROUGH	IRON	?	?	SIMPLE-T
-E23	M	1	1876	HIGHWAY	1245	?	?	THROUGH	STEEL	LONG	F	SUSPEN
-E22	A	24	1876	HIGHWAY	1200	4	G	THROUGH	WOOD	SHORT	S	WOOD
-E24	O	45	1878	RR	?	2	G	?	STEEL	?	?	SIMPLE-T
-E25	M	10	1882	RR	?	2	G	?	STEEL	?	?	SIMPLE-T
-E27	A	39	1883	RR	?	2	G	THROUGH	STEEL	?	F	SIMPLE-T
-E26	M	12	1883	RR	1150	2	G	THROUGH	STEEL	MEDIUM	S	SIMPLE-T
-E30	A	31	1884	RR	?	2	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E29	A	26	1884	HIGHWAY	1080	2	G	THROUGH	STEEL	MEDIUM	?	SUSPEN
-E28	M	3	1884	HIGHWAY	1000	2	G	THROUGH	STEEL	MEDIUM	S	ARCH
-E32	A	30	1887	HIGHWAY	?	2	G	THROUGH	IRON	MEDIUM	F	SIMPLE-T
-E31	M	8	1887	RR	1161	2	G	THROUGH	STEEL	MEDIUM	S	SIMPLE-T
-E34	O	41	1888	RR	4558	2	G	THROUGH	STEEL	LONG	F	SIMPLE-T
-E33	M	19	1889	HIGHWAY	1120	?	G	THROUGH	IRON	MEDIUM	F	SIMPLE-T
-E36	O	45	1890	HIGHWAY	?	2	G	THROUGH	IRON	SHORT	F	SIMPLE-T
-E35	A	27	1890	HIGHWAY	1000	2	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E38	M	17	1891	HIGHWAY	?	2	G	THROUGH	IRON	MEDIUM	F	SIMPLE-T
-E37	M	18	1891	RR	1350	2	G	THROUGH	STEEL	MEDIUM	S	SIMPLE-T
-E39	A	25	1892	HIGHWAY	?	2	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E4	A	27	1892	AQUEDUCT	1092	1	N	THROUGH	WOOD	SHORT	S	WOOD
-E40	M	22	1893	HIGHWAY	?	2	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E41	M	11	1894	HIGHWAY	?	2	G	THROUGH	IRON	MEDIUM	F	SIMPLE-T
-E42	M	9	1895	HIGHWAY	2367	2	G	THROUGH	STEEL	LONG	F	SIMPLE-T
-E44	O	48	1896	HIGHWAY	?	2	G	THROUGH	STEEL	LONG	F	SUSPEN
-E43	M	7	1896	HIGHWAY	1040	2	G	THROUGH	STEEL	LONG	F	ARCH
-E46	A	37	1897	RR	4000	2	G	DECK	STEEL	LONG	F	SIMPLE-T
-E45	M	14	1897	RR	2264	?	G	THROUGH	STEEL	?	F	SIMPLE-T
-E47	M	15	1898	RR	2000	2	G	THROUGH	STEEL	MEDIUM	S	SIMPLE-T
-E58	A	33	1900	HIGHWAY	1200	2	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E48	A	38	1900	HIGHWAY	2000	2	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E94	M	13	1901	RR	?	2	G	THROUGH	STEEL	LONG	F	SIMPLE-T
-E49	A	34	1902	HIGHWAY	1850	2	G	THROUGH	STEEL	MEDIUM	F	CANTILEV
-E95	M	16	1903	RR	1300	2	G	THROUGH	STEEL	MEDIUM	S	SIMPLE-T
-E87	A	35	1903	RR	3000	2	G	THROUGH	STEEL	MEDIUM	S	SIMPLE-T
-E51	M	6	1903	RR	1417	2	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E50	M	21	1903	RR	1154	?	G	THROUGH	STEEL	LONG	F	SIMPLE-T
-E89	M	4	1904	RR	1200	2	G	THROUGH	STEEL	MEDIUM	S-F	SIMPLE-T
-E53	A	28	1904	RR	965	4	G	THROUGH	STEEL	MEDIUM	S-F	SIMPLE-T
-E52	M	2	1904	RR	1504	?	G	THROUGH	STEEL	LONG	F	CANTILEV
-E54	Y	?	1908	HIGHWAY	1240	?	G	?	STEEL	MEDIUM	F	SIMPLE-T
-E56	M	23	1909	HIGHWAY	?	?	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E55	A	36	1909	HIGHWAY	1730	2	G	THROUGH	STEEL	LONG	F	SIMPLE-T
-E57	O	49	1910	RR	1620	2	G	THROUGH	STEEL	LONG	F	CANTILEV
-E59	O	43	1911	HIGHWAY	1652	2	G	THROUGH	STEEL	LONG	F	CANTILEV
-E107	A	39	1914	RR	?	?	G	?	STEEL	?	F	NIL
-E92	M	10	1914	RR	2210	?	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E61	O	41	1915	RR	2822	2	G	THROUGH	STEEL	LONG	F	SIMPLE-T
-E60	A	24	1915	HIGHWAY	1000	4	G	THROUGH	STEEL	LONG	F	SIMPLE-T
-E62	A	37	1918	RR	2300	2	N	DECK	STEEL	LONG	F	CONT-T
-E63	A	31	1920	RR	2122	2	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E65	A	30	1921	WALK	?	?	G	THROUGH	STEEL	?	F	SUSPEN
-E64	A	29	1923	HIGHWAY	885	4	G	THROUGH	STEEL	MEDIUM	F	ARCH
-E66	A	32	1924	HIGHWAY	2365	4	G	THROUGH	STEEL	MEDIUM	S	ARCH
-E70	A	27	1926	HIGHWAY	860	4	G	THROUGH	STEEL	MEDIUM	S-F	SUSPEN
-E69	A	26	1926	HIGHWAY	884	4	G	THROUGH	STEEL	MEDIUM	S-F	SUSPEN
-E101	O	46	1927	HIGHWAY	1770	2	G	THROUGH	STEEL	LONG	S-F	CANTILEV
-E73	A	38	1927	HIGHWAY	1508	?	G	THROUGH	STEEL	MEDIUM	S	ARCH
-E72	M	5	1927	HIGHWAY	2663	4	N	DECK	STEEL	MEDIUM	S-F	CANTILEV
-E67	M	1	1927	HIGHWAY	1330	4	G	THROUGH	STEEL	LONG	F	CANTILEV
-E75	A	30	1928	HIGHWAY	2678	4	G	DECK	STEEL	MEDIUM	F	ARCH
-E74	M	20	1928	HIGHWAY	2220	2	G	DECK	STEEL	MEDIUM	S-F	CANTILEV
-E71	A	25	1928	HIGHWAY	860	4	G	THROUGH	STEEL	MEDIUM	S-F	SUSPEN
-E68	M	17	1928	HIGHWAY	2250	2	G	THROUGH	STEEL	MEDIUM	S	SIMPLE-T
-E78	O	40	1931	HIGHWAY	1365	4	G	THROUGH	STEEL	LONG	F	ARCH
-E77	O	42	1931	HIGHWAY	1450	4	N	THROUGH	STEEL	LONG	F	ARCH
-E76	M	6	1931	HIGHWAY	1500	4	G	THROUGH	STEEL	LONG	F	SUSPEN
-E93	M	11	1937	HIGHWAY	1690	4	N	DECK	STEEL	LONG	S-F	CONT-T
-E79	A	34	1939	HIGHWAY	1800	4	G	DECK	STEEL	MEDIUM	F	CANTILEV
-E108	A	39.5	1945	HIGHWAY	1060	4	G	DECK	STEEL	MEDIUM	S-F	CONT-T
-E107N	A	39.7	1945	RR	840	2	G	THROUGH	STEEL	MEDIUM	S-F	SIMPLE-T
-E105	A	38.5	1945	HIGHWAY	1710	2	N	DECK	STEEL	MEDIUM	S-F	CONT-T
-E103	O	48	1945	HIGHWAY	2160	2	G	THROUGH	STEEL	LONG	F	CANTILEV
-E97	Y	52	1945	HIGHWAY	?	?	G	THROUGH	STEEL	MEDIUM	S	ARCH
-E96	Y	51	1945	RR	?	?	G	THROUGH	STEEL	MEDIUM	F	SIMPLE-T
-E99	M	23	1950	HIGHWAY	1320	2	G	THROUGH	STEEL	MEDIUM	S-F	SIMPLE-T
-E98	M	22	1951	HIGHWAY	900	4	G	THROUGH	STEEL	MEDIUM	F	CONT-T
-E81	M	14	1951	HIGHWAY	2423	4	G	DECK	STEEL	LONG	F	CONT-T
-E80	M	19	1951	HIGHWAY	1031	4	G	THROUGH	STEEL	LONG	F	CANTILEV
-E88	A	37	1955	HIGHWAY	2300	4	N	DECK	STEEL	LONG	F	CONT-T
-E82	O	42	1955	HIGHWAY	804	?	G	THROUGH	STEEL	?	F	SIMPLE-T
-E102	O	47	1959	HIGHWAY	1700	2	G	THROUGH	STEEL	LONG	F	CONT-T
-E83	M	1	1959	HIGHWAY	1000	6	G	THROUGH	STEEL	LONG	F	ARCH
-E86	A	33	1961	HIGHWAY	980	4	G	DECK	STEEL	MEDIUM	S-F	CONT-T
-E85	M	9	1962	HIGHWAY	2213	4	G	DECK	STEEL	LONG	F	CONT-T
-E84	A	24	1969	HIGHWAY	870	6	G	THROUGH	STEEL	MEDIUM	F	ARCH
-E91	O	44	1975	HIGHWAY	3756	6	G	THROUGH	STEEL	LONG	F	ARCH
-E90	M	7	1978	HIGHWAY	950	6	G	THROUGH	STEEL	LONG	F	ARCH
-E100	O	43	1982	HIGHWAY	?	?	G	?	?	?	F	?
-E109	A	28	1986	HIGHWAY	?	?	G	?	?	?	F	?

Orange/doc/modules/bupa.tab

-mcv	alkphos	sgpt	sgot	gammagt	drinks	selector
-c	c	c	c	c	c	d
-						class
-85	92	45	27	31	0.0	1
-85	64	59	32	23	0.0	2
-86	54	33	16	54	0.0	2
-91	78	34	24	36	0.0	2
-87	70	12	28	10	0.0	2
-98	55	13	17	17	0.0	2
-88	62	20	17	9	0.5	1
-88	67	21	11	11	0.5	1
-92	54	22	20	7	0.5	1
-90	60	25	19	5	0.5	1
-89	52	13	24	15	0.5	1
-82	62	17	17	15	0.5	1
-90	64	61	32	13	0.5	1
-86	77	25	19	18	0.5	1
-96	67	29	20	11	0.5	1
-91	78	20	31	18	0.5	1
-89	67	23	16	10	0.5	1
-89	79	17	17	16	0.5	1
-91	107	20	20	56	0.5	1
-94	116	11	33	11	0.5	1
-92	59	35	13	19	0.5	1
-93	23	35	20	20	0.5	1
-90	60	23	27	5	0.5	1
-96	68	18	19	19	0.5	1
-84	80	47	33	97	0.5	1
-92	70	24	13	26	0.5	1
-90	47	28	15	18	0.5	1
-88	66	20	21	10	0.5	1
-91	102	17	13	19	0.5	1
-87	41	31	19	16	0.5	1
-86	79	28	16	17	0.5	1
-91	57	31	23	42	0.5	1
-93	77	32	18	29	0.5	1
-88	96	28	21	40	0.5	1
-94	65	22	18	11	0.5	1
-91	72	155	68	82	0.5	2
-85	54	47	33	22	0.5	2
-79	39	14	19	9	0.5	2
-85	85	25	26	30	0.5	2
-89	63	24	20	38	0.5	2
-84	92	68	37	44	0.5	2
-89	68	26	39	42	0.5	2
-89	101	18	25	13	0.5	2
-86	84	18	14	16	0.5	2
-85	65	25	14	18	0.5	2
-88	61	19	21	13	0.5	2
-92	56	14	16	10	0.5	2
-95	50	29	25	50	0.5	2
-91	75	24	22	11	0.5	2
-83	40	29	25	38	0.5	2
-89	74	19	23	16	0.5	2
-85	64	24	22	11	0.5	2
-92	57	64	36	90	0.5	2
-94	48	11	23	43	0.5	2
-87	52	21	19	30	0.5	2
-85	65	23	29	15	0.5	2
-84	82	21	21	19	0.5	2
-88	49	20	22	19	0.5	2
-96	67	26	26	36	0.5	2
-90	63	24	24	24	0.5	2
-90	45	33	34	27	0.5	2
-90	72	14	15	18	0.5	2
-91	55	4	8	13	0.5	2
-91	52	15	22	11	0.5	2
-87	71	32	19	27	1.0	1
-89	77	26	20	19	1.0	1
-89	67	5	17	14	1.0	2
-85	51	26	24	23	1.0	2
-103	75	19	30	13	1.0	2
-90	63	16	21	14	1.0	2
-90	63	29	23	57	2.0	1
-90	67	35	19	35	2.0	1
-87	66	27	22	9	2.0	1
-90	73	34	21	22	2.0	1
-86	54	20	21	16	2.0	1
-90	80	19	14	42	2.0	1
-87	90	43	28	156	2.0	2
-96	72	28	19	30	2.0	2
-91	55	9	25	16	2.0	2
-95	78	27	25	30	2.0	2
-92	101	34	30	64	2.0	2
-89	51	41	22	48	2.0	2
-91	99	42	33	16	2.0	2
-94	58	21	18	26	2.0	2
-92	60	30	27	297	2.0	2
-94	58	21	18	26	2.0	2
-88	47	33	26	29	2.0	2
-92	65	17	25	9	2.0	2
-92	79	22	20	11	3.0	1
-84	83	20	25	7	3.0	1
-88	68	27	21	26	3.0	1
-86	48	20	20	6	3.0	1
-99	69	45	32	30	3.0	1
-88	66	23	12	15	3.0	1
-89	62	42	30	20	3.0	1
-90	51	23	17	27	3.0	1
-81	61	32	37	53	3.0	2
-89	89	23	18	104	3.0	2
-89	65	26	18	36	3.0	2
-92	75	26	26	24	3.0	2
-85	59	25	20	25	3.0	2
-92	61	18	13	81	3.0	2
-89	63	22	27	10	4.0	1
-90	84	18	23	13	4.0	1
-88	95	25	19	14	4.0	1
-89	35	27	29	17	4.0	1
-91	80	37	23	27	4.0	1
-91	109	33	15	18	4.0	1
-91	65	17	5	7	4.0	1
-88	107	29	20	50	4.0	2
-87	76	22	55	9	4.0	2
-87	86	28	23	21	4.0	2
-87	42	26	23	17	4.0	2
-88	80	24	25	17	4.0	2
-90	96	34	49	169	4.0	2
-86	67	11	15	8	4.0	2
-92	40	19	20	21	4.0	2
-85	60	17	21	14	4.0	2
-89	90	15	17	25	4.0	2
-91	57	15	16	16	4.0	2
-96	55	48	39	42	4.0	2
-79	101	17	27	23	4.0	2
-90	134	14	20	14	4.0	2
-89	76	14	21	24	4.0	2
-88	93	29	27	31	4.0	2
-90	67	10	16	16	4.0	2
-92	73	24	21	48	4.0	2
-91	55	28	28	82	4.0	2
-83	45	19	21	13	4.0	2
-90	74	19	14	22	4.0	2
-92	66	21	16	33	5.0	1
-93	63	26	18	18	5.0	1
-86	78	47	39	107	5.0	2
-97	44	113	45	150	5.0	2
-87	59	15	19	12	5.0	2
-86	44	21	11	15	5.0	2
-87	64	16	20	24	5.0	2
-92	57	21	23	22	5.0	2
-90	70	25	23	112	5.0	2
-99	59	17	19	11	5.0	2
-92	80	10	26	20	6.0	1
-95	60	26	22	28	6.0	1
-91	63	25	26	15	6.0	1
-92	62	37	21	36	6.0	1
-95	50	13	14	15	6.0	1
-90	76	37	19	50	6.0	1
-96	70	70	26	36	6.0	1
-95	62	64	42	76	6.0	1
-92	62	20	23	20	6.0	1
-91	63	25	26	15	6.0	1
-82	56	67	38	92	6.0	2
-92	82	27	24	37	6.0	2
-90	63	12	26	21	6.0	2
-88	37	9	15	16	6.0	2
-100	60	29	23	76	6.0	2
-98	43	35	23	69	6.0	2
-91	74	87	50	67	6.0	2
-92	87	57	25	44	6.0	2
-93	99	36	34	48	6.0	2
-90	72	17	19	19	6.0	2
-97	93	21	20	68	6.0	2
-93	50	18	25	17	6.0	2
-90	57	20	26	33	6.0	2
-92	76	31	28	41	6.0	2
-88	55	19	17	14	6.0	2
-89	63	24	29	29	6.0	2
-92	79	70	32	84	7.0	1
-92	93	58	35	120	7.0	1
-93	84	58	47	62	7.0	2
-97	71	29	22	52	8.0	1
-84	99	33	19	26	8.0	1
-96	44	42	23	73	8.0	1
-90	62	22	21	21	8.0	1
-92	94	18	17	6	8.0	1
-90	67	77	39	114	8.0	1
-97	71	29	22	52	8.0	1
-91	69	25	25	66	8.0	2
-93	59	17	20	14	8.0	2
-92	95	85	48	200	8.0	2
-90	50	26	22	53	8.0	2
-91	62	59	47	60	8.0	2
-92	93	22	28	123	9.0	1
-92	77	86	41	31	10.0	1
-86	66	22	24	26	10.0	2
-98	57	31	34	73	10.0	2
-95	80	50	64	55	10.0	2
-92	108	53	33	94	12.0	2
-97	92	22	28	49	12.0	2
-93	77	39	37	108	16.0	1
-94	83	81	34	201	20.0	1
-87	75	25	21	14	0.0	1
-88	56	23	18	12	0.0	1
-84	97	41	20	32	0.0	2
-94	91	27	20	15	0.5	1
-97	62	17	13	5	0.5	1
-92	85	25	20	12	0.5	1
-82	48	27	15	12	0.5	1
-88	74	31	25	15	0.5	1
-95	77	30	14	21	0.5	1
-88	94	26	18	8	0.5	1
-91	70	19	19	22	0.5	1
-83	54	27	15	12	0.5	1
-91	105	40	26	56	0.5	1
-86	79	37	28	14	0.5	1
-91	96	35	22	135	0.5	1
-89	82	23	14	35	0.5	1
-90	73	24	23	11	0.5	1
-90	87	19	25	19	0.5	1
-89	82	33	32	18	0.5	1
-85	79	17	8	9	0.5	1
-85	119	30	26	17	0.5	1
-78	69	24	18	31	0.5	1
-88	107	34	21	27	0.5	1
-89	115	17	27	7	0.5	1
-92	67	23	15	12	0.5	1
-89	101	27	34	14	0.5	1
-91	84	11	12	10	0.5	1
-94	101	41	20	53	0.5	2
-88	46	29	22	18	0.5	2
-88	122	35	29	42	0.5	2
-84	88	28	25	35	0.5	2
-90	79	18	15	24	0.5	2
-87	69	22	26	11	0.5	2
-65	63	19	20	14	0.5	2
-90	64	12	17	14	0.5	2
-85	58	18	24	16	0.5	2
-88	81	41	27	36	0.5	2
-86	78	52	29	62	0.5	2
-82	74	38	28	48	0.5	2
-86	58	36	27	59	0.5	2
-94	56	30	18	27	0.5	2
-87	57	30	30	22	0.5	2
-98	74	148	75	159	0.5	2
-94	75	20	25	38	0.5	2
-83	68	17	20	71	0.5	2
-93	56	25	21	33	0.5	2
-101	65	18	21	22	0.5	2
-92	65	25	20	31	0.5	2
-92	58	14	16	13	0.5	2
-86	58	16	23	23	0.5	2
-85	62	15	13	22	0.5	2
-86	57	13	20	13	0.5	2
-86	54	26	30	13	0.5	2
-81	41	33	27	34	1.0	1
-91	67	32	26	13	1.0	1
-91	80	21	19	14	1.0	1
-92	60	23	15	19	1.0	1
-91	60	32	14	8	1.0	1
-93	65	28	22	10	1.0	1
-90	63	45	24	85	1.0	2
-87	92	21	22	37	1.0	2
-83	78	31	19	115	1.0	2
-95	62	24	23	14	1.0	2
-93	59	41	30	48	1.0	2
-84	82	43	32	38	2.0	1
-87	71	33	20	22	2.0	1
-86	44	24	15	18	2.0	1
-86	66	28	24	21	2.0	1
-88	58	31	17	17	2.0	1
-90	61	28	29	31	2.0	1
-88	69	70	24	64	2.0	1
-93	87	18	17	26	2.0	1
-98	58	33	21	28	2.0	1
-91	44	18	18	23	2.0	2
-87	75	37	19	70	2.0	2
-94	91	30	26	25	2.0	2
-88	85	14	15	10	2.0	2
-89	109	26	25	27	2.0	2
-87	59	37	27	34	2.0	2
-93	58	20	23	18	2.0	2
-88	57	9	15	16	2.0	2
-94	65	38	27	17	3.0	1
-91	71	12	22	11	3.0	1
-90	55	20	20	16	3.0	1
-91	64	21	17	26	3.0	2
-88	47	35	26	33	3.0	2
-82	72	31	20	84	3.0	2
-85	58	83	49	51	3.0	2
-91	54	25	22	35	4.0	1
-98	50	27	25	53	4.0	2
-86	62	29	21	26	4.0	2
-89	48	32	22	14	4.0	2
-82	68	20	22	9	4.0	2
-83	70	17	19	23	4.0	2
-96	70	21	26	21	4.0	2
-94	117	77	56	52	4.0	2
-93	45	11	14	21	4.0	2
-93	49	27	21	29	4.0	2
-84	73	46	32	39	4.0	2
-91	63	17	17	46	4.0	2
-90	57	31	18	37	4.0	2
-87	45	19	13	16	4.0	2
-91	68	14	20	19	4.0	2
-86	55	29	35	108	4.0	2
-91	86	52	47	52	4.0	2
-88	46	15	33	55	4.0	2
-85	52	22	23	34	4.0	2
-89	72	33	27	55	4.0	2
-95	59	23	18	19	4.0	2
-94	43	154	82	121	4.0	2
-96	56	38	26	23	5.0	2
-90	52	10	17	12	5.0	2
-94	45	20	16	12	5.0	2
-99	42	14	21	49	5.0	2
-93	102	47	23	37	5.0	2
-94	71	25	26	31	5.0	2
-92	73	33	34	115	5.0	2
-87	54	41	29	23	6.0	1
-92	67	15	14	14	6.0	1
-98	101	31	26	32	6.0	1
-92	53	51	33	92	6.0	1
-97	94	43	43	82	6.0	1
-93	43	11	16	54	6.0	1
-93	68	24	18	19	6.0	1
-95	36	38	19	15	6.0	1
-99	86	58	42	203	6.0	1
-98	66	103	57	114	6.0	1
-92	80	10	26	20	6.0	1
-96	74	27	25	43	6.0	2
-95	93	21	27	47	6.0	2
-86	109	16	22	28	6.0	2
-91	46	30	24	39	7.0	2
-102	82	34	78	203	7.0	2
-85	50	12	18	14	7.0	2
-91	57	33	23	12	8.0	1
-91	52	76	32	24	8.0	1
-93	70	46	30	33	8.0	1
-87	55	36	19	25	8.0	1
-98	123	28	24	31	8.0	1
-82	55	18	23	44	8.0	2
-95	73	20	25	225	8.0	2
-97	80	17	20	53	8.0	2
-100	83	25	24	28	8.0	2
-88	91	56	35	126	9.0	2
-91	138	45	21	48	10.0	1
-92	41	37	22	37	10.0	1
-86	123	20	25	23	10.0	2
-91	93	35	34	37	10.0	2
-87	87	15	23	11	10.0	2
-87	56	52	43	55	10.0	2
-99	75	26	24	41	12.0	1
-96	69	53	43	203	12.0	2
-98	77	55	35	89	15.0	1
-91	68	27	26	14	16.0	1
-98	99	57	45	65	20.0	1

Orange/doc/modules/bus-mysql.sql

-DROP TABLE bus;
-CREATE TABLE bus 
-  (id varchar(5), 
-   line integer, 
-   daytime varchar, 
-   temp float, 
-   weather varchar, 
-   arrival varchar);
-
-LOAD DATA LOCAL INFILE 'bus.txt' INTO TABLE bus;
-SELECT * from bus;

Orange/doc/modules/bus-postgres.sql

-DROP TABLE bus;
-CREATE TABLE bus 
-  (id varchar(5), 
-   line integer, 
-   daytime varchar, 
-   temp float, 
-   weather varchar, 
-   arrival varchar);
-
-\COPY "bus" FROM 'bus.txt' USING DELIMITERS '	'
-SELECT * from bus;

Orange/doc/modules/bus.sql

---DROP TABLE bus;
-CREATE TABLE bus 
-  (id varchar(5), 
-   line enum('9','10','11'), 
-   daytime enum('morning','evening', 'midday'), 
-   temp float, 
-   weather enum('rainy','sunny'), 
-   arrival enum('late','on-time'));
-LOAD DATA LOCAL INFILE 'bus.txt' INTO TABLE bus;
-
-SELECT * FROM bus;

Orange/doc/modules/bus.txt

-1	10	morning	10	sunny	late
-2	11	morning	13	rainy	late
-3	9	morning	15	rainy	late
-4	10	evening	25	sunny	on-time
-5	9	evening	29	sunny	on-time
-6	11	morning	26	sunny	late
-7	9	evening	9	rainy	on-time
-8	9	midday	20	rainy	late
-9	11	midday	21	sunny	late
-10	10	evening	5	rainy	on-time
-11	10	midday	8	rainy	late
-12	9	morning	5	rainy	on-time

Orange/doc/modules/busclass.sql

-USE test;
-DROP TABLE busclass;
-CREATE TABLE busclass 
-  (m$id varchar(5), 
-   line enum('9','10','11'), 
-   daytime enum('morning','evening', 'midday'), 
-   temp float, weather enum('rainy','sunny'), 
-   c$arrival enum('late','on-time'));
-LOAD DATA LOCAL INFILE 'bus.txt' INTO TABLE busclass;
-
-SELECT * FROM busclass;

Orange/doc/modules/classification-rules2.py

-# Description: Demonstrates the use of orngCN2 rules
-# Category:    classification, rules
-# Classes:     CN2Learner, CN2UnorderedLearner, CN2UnorderedLearner
-# Uses:        titanic.tab
-
-import orange
-import orngCN2
-
-data = orange.ExampleTable("titanic.tab")
-
-# create learner
-learner = orngCN2.CN2Learner()
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)
-print "*****"
-
-learner = orngCN2.CN2UnorderedLearner()
-
-learner.ruleFinder = orange.RuleBeamFinder()
-learner.ruleFinder.evaluator = orngCN2.mEstimate(m=50)
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)
-print "****"
-
-learner = orngCN2.CN2SDUnorderedLearner()
-
-learner.ruleFinder.ruleStoppingValidator = orange.RuleValidator_LRS(alpha=0.01,min_coverage=10,max_rule_complexity = 2)
-learner.ruleFinder.ruleFilter = orange.RuleBeamFilter_Width(width = 50)
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)
-print "****"
-
-learner = orngCN2.CN2UnorderedLearner()
-
-learner.ruleFinder = orange.RuleBeamFinder()
-learner.ruleFinder.evaluator = orngCN2.WRACCEvaluator()
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)
-print "****"
-
-

Orange/doc/modules/classification_rules1.py

-import orange
-import orngCN2
-
-data = orange.ExampleTable("titanic.tab")
-
-# create learner
-learner = orange.RuleLearner()
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)
-print "*****"
-
-learner.ruleFinder = orange.RuleBeamFinder()
-learner.ruleFinder.evaluator = orngCN2.mEstimate(m=50)
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)
-print "****"
-
-learner.ruleFinder.ruleStoppingValidator = orange.RuleValidator_LRS(alpha=0.01,min_coverage=10,max_rule_complexity = 2)
-learner.ruleFinder.ruleFilter = orange.RuleBeamFilter_Width(width = 50)
-
-cl = learner(data)
-for r in cl.rules:
-    print orngCN2.ruleToString(r)

Orange/doc/modules/correspondance.py

-# Description: Demonstrates the use of correspondence analysis
-# Category:    correspondence, projection
-# Classes:     CA
-# Uses:        bridges.tab
-
-import orange
-import orngCA
-
-data = orange.ExampleTable("bridges")
-cm = orange.ContingencyAttrAttr("PURPOSE", "MATERIAL", data)
-ca = orngCA.CA([list(col) for col in cm])
-
-def report(coors, labels):
-    for coor, label in zip(coors, labels):
-        print "  %-10s (%.3f, %.3f)" % (label + ":", coor[0], coor[1])
-        
-print "PURPOSE"
-report(ca.getPrincipalColProfilesCoordinates(), data.domain["PURPOSE"].values)
-print 
-
-print "MATERIAL"
-report(ca.getPrincipalRowProfilesCoordinates(), data.domain["PURPOSE"].values)
-print 

Orange/doc/modules/default.htm

-<html>
-<HEAD>
-<LINK REL=StyleSheet HREF="../style.css" TYPE="text/css">
-</HEAD>
-<body>
-
-<h1>Orange Modules</h1>
-
-<p>Orange modules are intended to extend basic Orange's functionality, or provide wrappers for easier use of some data mining techniques. They are already included in normal Orange distribution. Following set of pages provides description, demos and examples for selected modules.</p>
-
-<P>Note: The documentation posted on the web is updated from the CVS in
-real-time and refers to the latest snapshot of Orange. If you encounter
-any inconsistencies please compare the standalone documentation with the
-one on the web.</P>
-
-<dl>
-<dt><a href="orngAssoc.htm">orngAssoc</a></dt><dd>A few things for association rules.</dd>
-<dt><a href="orngBayes.htm">orngBayes</a></dt><dd>Wrapper around Orange's naive Bayesian learner that makes it easier to use m-estimation; it can also print out the model.</dd>
-<dt><a href="orngCA.htm">orngCA</a></dt><dd>Correspondence analysis.</dd>
-<dt><a href="orngC45.htm">orngC45</a></dt><dd>A module with a function that prints out C4.5 trees in exactly the same format as Quinlan's C4.5.</dd>
-<dt><a href="orngCI.htm">orngCI</a></dt><dd>Constructive induction (function decomposition methods, HINT, Kramer's constructive induction method).</dd>
-<dt><a href="orngClustering.htm">orngClustering</a></dt><dd>Various clustering methods and associated miscellaneous utilities.</dd>
-<dt><a href="orngCN2.htm">orngCN2</a></dt><dd>A set of classes and functions for learning rules (based on CN2).</dd>
-<dt><a href="orngDisc.htm">orngDisc</a></dt><dd>Wrapper around Orange's categorization techniques for continuous attributes.</dd>
-<dt><a href="orngEnsemble.htm">orngEnsemble</a></dt><dd>Bagging,
-    boosting, random forests.</dd>
-<dt>orngEval</dt><dd>Obsolete, included for compatibility with past version of Orange. Use orngTest and orngStat instead.</dd>
-<dt><a href="orngFSS.htm">orngFSS</a></dt><dd>Feature subset selection.</dd>
-<dt><a href="orngImpute.htm">orngImpute</a></dt><dd>Imputation wrappers for learners and classifiers.</dd>
-<dt><a href="orngLinProj.htm">orngLinProj</a></dt><dd>Implements the FreeViz method by Demsar et al.</dd>
-<dt><a href="orngLookup.htm">orngLookup</a></dt><dd>Functions for working with classifiers with stored tables of examples.</dd>
-<dt><a href="orngLR.htm">orngLR</a></dt><dd>Wrappers for easier use of Orange's classes for logistic regression</dd>
-<dt><a href="orngMDS.htm">orngMDS</a></dt><dd>Multidimensional scaling.</dd>
-<dt><a href="orngMisc.htm">orngMisc</a></dt><dd>Miscellaneous functions, including various counters and selections of optimal objects in a sequence.</dd>
-<dt><a href="orngMySQL.htm">orngMySQL</a></dt><dd>Interface to MySQL.</dd>
-<dt><a href="orngNetwork.htm">orngNetwork</a></dt><dd>Network analysis and layout optimization.</dd>
-<dt><a href="orngOutlier.htm">orngOutlier</a></dt><dd>Outlier detection.</dd>
-<dt><a href="orngReinforcement.htm">orngReinforcement</a></dt><dd>Reinforcement
-learning.</dd>
-<dt><a href="orngServerFiles.htm">orngServerFiles</a></dt><dd>Orange's file repository.</dd>
-<dt><a href="orngSQL.htm">orngSQL</a></dt><dd>A new interface to any <a href="http://www.python.org/dev/peps/pep-0249/">PEP 249</a> compliant RDBS. Supports both MySQL and Postgres.</dd>
-<dt><a href="orngStat.htm">orngStat</a></dt><dd>Computation of various statistics such as accuracy, sensitivity, specificity, and area under ROC from the experimental data from module orngTest.</dd>