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Aleš Erjavec  committed 1242f06

Fixed changed regression tests output.

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  • Parent commits aa91172

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Files changed (10)

File Orange/testing/regression/results_reference/svm-custom-kernel.py.darwin.txt

-SVM - RBF(Euclidean) CA: 0.96
-SVM - RBF(Hamming) CA: 0.886666666667
+SVM - RBF(Euclidean) CA: 0.97
+SVM - RBF(Hamming) CA: 0.87
 SVM - Composite CA: 0.94

File Orange/testing/regression/results_reference/svm-custom-kernel.py.txt

-SVM - RBF(Euclidean) CA: 0.966666666667
-SVM - RBF(Hamming) CA: 0.873333333333
+SVM - RBF(Euclidean) CA: 0.97
+SVM - RBF(Hamming) CA: 0.87
 SVM - Composite CA: 0.94

File Orange/testing/regression/results_reference/svm-easy.py.txt

 Name     CA        AUC
-svm easy 0.83      0.96
-svm      0.76      0.95
+svm easy 0.81      0.96
+svm      0.74      0.94

File Orange/testing/regression/results_reference/svm-linear-weights.py.txt

-['0.0305391660', '0.0464525615', '0.0655766610', '0.1142851330', '0.1147435934', '0.1239657860', '0.1302437856', '0.1308238026', '0.1364165500', '0.1374175856', '0.1387632111', '0.1408812075', '0.1493575565', '0.1518344820', '0.1591399900', '0.1608572883', '0.1657731559', '0.1695697600', '0.1699370279', '0.1820761410', '0.1827041161', '0.1831093664', '0.1843390221', '0.1905684270', '0.1909180450', '0.1919806873', '0.1932982887', '0.2008838837', '0.2061566394', '0.2093389787', '0.2137991275', '0.2175498224', '0.2195202824', '0.2347380743', '0.2496567209', '0.2496831102', '0.2498289071', '0.2645926914', '0.2678048194', '0.2798043763', '0.3092300737', '0.3192185952', '0.3272979394', '0.3347197463', '0.3367415609', '0.3573783044', '0.3658304312', '0.3669869944', '0.3834953229', '0.3890995409', '0.4041756463', '0.4097605066', '0.4242536327', '0.4478969203', '0.4493259506', '0.4580637448', '0.4786304349', '0.4945177545', '0.5474717710', '0.5530522984', '0.5647568024', '0.5838555421', '0.5939775048', '0.5951921790', '0.5965606638', '0.6932491137', '0.6947063827', '0.7078091253', '0.8081597686', '0.8466186966', '0.8647671894', '0.9867498369', '1.0003215809', '1.0683522323', '1.2000832157', '1.4453028351', '1.9466576191', '2.2487957189', '3.2086625764']
+['0.0305', '0.0464', '0.0656', '0.1143', '0.1147', '0.1240', '0.1303', '0.1308', '0.1364', '0.1374', '0.1388', '0.1409', '0.1493', '0.1519', '0.1592', '0.1609', '0.1658', '0.1695', '0.1699', '0.1821', '0.1827', '0.1831', '0.1843', '0.1906', '0.1909', '0.1920', '0.1933', '0.2008', '0.2062', '0.2094', '0.2138', '0.2175', '0.2195', '0.2347', '0.2496', '0.2497', '0.2499', '0.2646', '0.2679', '0.2797', '0.3093', '0.3193', '0.3273', '0.3347', '0.3367', '0.3573', '0.3658', '0.3671', '0.3835', '0.3890', '0.4042', '0.4099', '0.4243', '0.4478', '0.4493', '0.4581', '0.4786', '0.4945', '0.5475', '0.5531', '0.5647', '0.5839', '0.5939', '0.5953', '0.5966', '0.6932', '0.6946', '0.7078', '0.8082', '0.8466', '0.8647', '0.9868', '1.0003', '1.0684', '1.2001', '1.4453', '1.9466', '2.2487', '3.2087']

File Orange/testing/regression/results_tests_20/modules_svm-custom-kernel.py.darwin.txt

-SVM - RBF(Euclidean) CA: 0.96
-SVM - RBF(Hamming) CA: 0.886666666667
+SVM - RBF(Euclidean) CA: 0.97
+SVM - RBF(Hamming) CA: 0.87
 SVM - Composite CA: 0.94

File Orange/testing/regression/results_tests_20/modules_svm-custom-kernel.py.txt

-SVM - RBF(Euclidean) CA: 0.966666666667
-SVM - RBF(Hamming) CA: 0.873333333333
+SVM - RBF(Euclidean) CA: 0.97
+SVM - RBF(Hamming) CA: 0.87
 SVM - Composite CA: 0.94

File Orange/testing/regression/results_tests_20/modules_svm-test.py.darwin.txt

            Name      CA     AUC
-SVM - Linear      0.960   0.999
+SVM - Linear      0.967   0.999
 SVM - Poly        0.933   0.995
 SVM - RBF         0.967   0.999

File Orange/testing/regression/tests_20/modules_svm-custom-kernel.py

 import orngTest, orngStat
 tests=orngTest.crossValidation([l1, l2, l3], data, folds=5)
 [ca1, ca2, ca3]=orngStat.CA(tests)
-print l1.name, "CA:", ca1
-print l2.name, "CA:", ca2
-print l3.name, "CA:", ca3
+print l1.name, "CA: %.2f" % ca1
+print l2.name, "CA: %.2f" % ca2
+print l3.name, "CA: %.2f" % ca3

File docs/reference/rst/code/svm-custom-kernel.py

 tests = evaluation.testing.cross_validation([l1, l2, l3], iris, folds=5)
 [ca1, ca2, ca3] = evaluation.scoring.CA(tests)
 
-print l1.name, "CA:", ca1
-print l2.name, "CA:", ca2
-print l3.name, "CA:", ca3
+print l1.name, "CA: %.2f" % ca1
+print l2.name, "CA: %.2f" % ca2
+print l3.name, "CA: %.2f" % ca3

File docs/reference/rst/code/svm-linear-weights.py

 brown = data.Table("brown-selected")
 classifier = svm.SVMLearner(brown,
                             kernel_type=svm.kernels.Linear,
-                            normalization=False)
+                            normalization=False,
+                            eps=1e-9)
 
 weights = svm.get_linear_svm_weights(classifier)
-print sorted("%.10f" % w for w in weights.values())
+print sorted("%.4f" % w for w in weights.values())
 
 import pylab as plt
 plt.hist(weights.values())