# Changes in [10016:46695dd492e7:10017:cd5aa69e57f7] in orange

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• ## Orange/testing/regression/results_reference/datatable_merge.py.txt

 r9954 Domain 1:  [a1, a2], {-2:m1, -3:m2} Domain 2:  [a1, a3], {-2:m1, -4:m3} Merged:    [a1, a2, a3], {-2:m1, -3:m2, -4:m3} Domain 1:  [a1, a2], {-3:m1, -4:m2} Domain 2:  [a1, a3], {-3:m1, -5:m3} Merged:    [a1, a2, a3], {-3:m1, -4:m2, -5:m3} [1, 2], {"m1":3, "m2":4}
• ## Orange/testing/regression/results_reference/discretization.py.txt

 r10016 Cut-off points: <2.90000009537, 3.29999995232> Manual construction of IntervalDiscretizer - single attribute Manual construction of Interval discretizer - single attribute [5.1, '>5.00', 'Iris-setosa'] [4.9, '(3.00, 5.00]', 'Iris-setosa'] [4.9, '(3.00, 5.00]', 'Iris-setosa'] Manual construction of IntervalDiscretizer - all attributes Manual construction of Interval discretizer - all attributes ['>5.00', '(3.00, 5.00]', '<=3.00', '<=3.00', 'Iris-setosa'] ['(3.00, 5.00]', '<=3.00', '<=3.00', '<=3.00', 'Iris-setosa'] Equal interval size discretization Discretization with equal width intervals D_sepal length: <<4.90, [4.90, 5.50), [5.50, 6.10), [6.10, 6.70), [6.70, 7.30), >7.30> D_sepal width: <<2.40, [2.40, 2.80), [2.80, 3.20), [3.20, 3.60), [3.60, 4.00), >4.00> Quartile discretization Quartile (equal frequency) discretization D_sepal length: <<=4.95, (4.95, 5.35], (5.35, 5.75], (5.75, 6.25], (6.25, 6.65], >6.65> D_sepal width: <<=2.65, (2.65, 2.85], (2.85, 3.05], (3.05, 3.25], (3.25, 3.45], >3.45> Manual construction of EquiDistDiscretizer - all attributes Manual construction of EqualWidth - all attributes ['>5.00', '[3.00, 4.00)', '<2.00', '<2.00', 'Iris-setosa'] ['[4.00, 5.00)', '[3.00, 4.00)', '<2.00', '<2.00', 'Iris-setosa'] ['[4.00, 5.00)', '[3.00, 4.00)', '<2.00', '<2.00', 'Iris-setosa'] Fayyad-Irani discretization Fayyad-Irani entropy-based discretization sepal length: <5.5, 6.09999990463> sepal width: <2.90000009537, 3.29999995232> Bi-Modal discretization on binary problem Bi-modal discretization on a binary problem sepal length: <5.40000009537, 6.19999980927> sepal width: <2.0, 2.90000009537> Bi-Modal discretization on binary problem Bi-modal discretization on a binary problem sepal length: (5.400, 6.200] sepal width: (2.000, 2.900] Entropy discretization on binary problem Entropy-based discretization on a binary problem sepal length: <5.40000009537, 7.0> sepal width: <2.90000009537>
• ## Orange/testing/regression/results_reference/instance-metavar.py.txt

 r9954 ['young', 'myope', 'no', 'reduced', 'none'], {-2:0.64} ['young', 'myope', 'no', 'reduced', 'none'], {-3:0.64}
• ## Orange/testing/regression/results_reference/instance_merge.py.txt

 r9954 First example:  [1, 2], {"m1":3, "m2":4} Second example:  [1, 2.5], {"m1":3, "m3":4.5} Merge:  [1, 2.5, 3, ?], {"a2":2, "m2":4, -4:4.50, "n2":?} Merge:  [1, 2.5, 3, ?], {"a2":2, "m2":4, -5:4.50, "n2":?}
• ## Orange/testing/regression/results_reference/knnInstanceDistance.py.txt

 r9954 *** Reference instance:  ['young', 'myope', 'no', 'reduced', 'none'] ['young', 'myope', 'no', 'reduced', 'none'], {-2:0.00} ['young', 'myope', 'no', 'normal', 'soft'], {-2:1.00} ['young', 'myope', 'yes', 'reduced', 'none'], {-2:1.00} ['pre-presbyopic', 'myope', 'no', 'reduced', 'none'], {-2:1.00} ['young', 'hypermetrope', 'no', 'reduced', 'none'], {-2:1.00} ['young', 'myope', 'no', 'reduced', 'none'], {-3:0.00} ['young', 'myope', 'no', 'normal', 'soft'], {-3:1.00} ['young', 'myope', 'yes', 'reduced', 'none'], {-3:1.00} ['pre-presbyopic', 'myope', 'no', 'reduced', 'none'], {-3:1.00} ['young', 'hypermetrope', 'no', 'reduced', 'none'], {-3:1.00}
• ## Orange/testing/regression/results_reference/lasso-example.py.txt

 r9954 Actual: 24.00, predicted: 26.54 Actual: 21.60, predicted: 23.85 Actual: 34.70, predicted: 26.35 Actual: 33.40, predicted: 25.73 Actual: 36.20, predicted: 25.55 Actual: 24.00, predicted: 24.87 Actual: 21.60, predicted: 23.56 Actual: 34.70, predicted: 25.73 Actual: 33.40, predicted: 25.34 Actual: 36.20, predicted: 25.30 Variable  Coeff Est  Std Error          p Intercept     22.533 RM      1.962      0.859      0.000   *** AGE     -0.007      0.003      0.160 PTRATIO     -0.627      0.193      0.000   *** B      0.002      0.002      0.240 LSTAT     -0.174      0.103      0.000   *** CRIM     -0.003      0.022      0.530 NOX     -1.563      0.900      0.220 RM      1.928      0.871      0.000   *** TAX     -0.000      0.001      0.450 PTRATIO     -0.220      0.189      0.060     . LSTAT     -0.136      0.099      0.000   *** Signif. codes:  0 *** 0.001 ** 0.01 * 0.05 . 0.1 empty 1 For 8 variables the regression coefficient equals 0: CRIM For 7 variables the regression coefficient equals 0: ZN INDUS CHAS NOX AGE DIS RAD TAX B
• ## Orange/testing/regression/results_reference/svm-recursive-feature-elimination.py.txt

 r9954 [alpha 0, alpha 7, alpha 14, alpha 21, alpha 28, alpha 35, alpha 42, alpha 49, alpha 56, alpha 63, alpha 70, alpha 77, alpha 84, alpha 91, alpha 98, alpha 105, alpha 112, alpha 119, Elu 0, Elu 30, Elu 60, Elu 90, Elu 120, Elu 150, Elu 180, Elu 210, Elu 240, Elu 270, Elu 300, Elu 330, Elu 360, Elu 390, cdc15 10, cdc15 30, cdc15 50, cdc15 70, cdc15 90, cdc15 110, cdc15 130, cdc15 150, cdc15 170, cdc15 190, cdc15 210, cdc15 230, cdc15 250, cdc15 270, cdc15 290, spo 0, spo 2, spo 5, spo 7, spo 9, spo 11, spo5 2, spo5 7, spo5 11, spo- early, spo- mid, heat 0, heat 10, heat 20, heat 40, heat 80, heat 160, dtt 15, dtt 30, dtt 60, dtt 120, cold 0, cold 20, cold 40, cold 160, diau a, diau b, diau c, diau d, diau e, diau f, diau g, function], {-2:gene} [Elu 120, cdc15 150, spo 5, spo- early, spo- mid, heat 10, cold 160, diau e, diau f, diau g, function], {-2:gene} [alpha 0, alpha 7, alpha 14, alpha 21, alpha 28, alpha 35, alpha 42, alpha 49, alpha 56, alpha 63, alpha 70, alpha 77, alpha 84, alpha 91, alpha 98, alpha 105, alpha 112, alpha 119, Elu 0, Elu 30, Elu 60, Elu 90, Elu 120, Elu 150, Elu 180, Elu 210, Elu 240, Elu 270, Elu 300, Elu 330, Elu 360, Elu 390, cdc15 10, cdc15 30, cdc15 50, cdc15 70, cdc15 90, cdc15 110, cdc15 130, cdc15 150, cdc15 170, cdc15 190, cdc15 210, cdc15 230, cdc15 250, cdc15 270, cdc15 290, spo 0, spo 2, spo 5, spo 7, spo 9, spo 11, spo5 2, spo5 7, spo5 11, spo- early, spo- mid, heat 0, heat 10, heat 20, heat 40, heat 80, heat 160, dtt 15, dtt 30, dtt 60, dtt 120, cold 0, cold 20, cold 40, cold 160, diau a, diau b, diau c, diau d, diau e, diau f, diau g, function], {-3:gene} [Elu 120, cdc15 150, spo 5, spo- early, spo- mid, heat 10, cold 160, diau e, diau f, diau g, function], {-3:gene}
• ## Orange/testing/regression/results_tests_20/reference_matrix.py.txt

 r9951 [  0.00000000e+00   1.00000000e+00   1.00000000e+00 ...,   0.00000000e+00 0.00000000e+00  -1.00000002e+30]] /home/miha/work/orange/Orange/testing/regression/xtest_one.py:78: KernelWarning: attribute 'name' is of unsupported type t__officialname = "%s/%s.%s.txt" % (t__outputsdir, t__name, t__sys.platform) [1.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 2.0 0.0 0.0 1.0 --]
• ## Orange/testing/regression/tests_20/reference_matrix.py

 r9952 except: print "Call '%s' failed" % meth.__name__ print t4.domain.attributes, t4.domain.classVar print t4[0] print except: zoo = orange.ExampleTable("../datasets/zoo") zoo_s = orange.ExampleTable(orange.Domain(zoo.domain.attributes+zoo.domain.getmetas().values(), zoo.domain.classVar), zoo) zoo_s = orange.ExampleTable(orange.Domain(zoo.domain.attributes + zoo.domain.getmetas().values(), zoo.domain.classVar), zoo) n = zoo_s.toNumpy() print n[0] n = zoo_s.toNumpyMA() print n[0][0]
• ## docs/reference/rst/code/discretization-entropy.py

 r9943 diff = old.difference(new) print "Redundant features (%d of %d):" % (len(diff), len(data.domain.features)) print ", ".join(x.name for x in diff) print ", ".join(sorted(x.name for x in diff))
• ## docs/reference/rst/code/exclude-from-regression.txt

 r9824 statistics-contingency6.py correspondence.py simple_tree_random_forest.py
• ## docs/reference/rst/code/hierarchical-example-2.py

 r9906 distance = Orange.distance.Euclidean(iris) for i1, instance1 in enumerate(iris): for i2 in range(i1+1, len(iris)): for i2 in range(i1 + 1, len(iris)): matrix[i1, i2] = distance(instance1, iris[i2]) clustering = Orange.clustering.hierarchical.HierarchicalClustering() clustering.linkage = clustering.Average clustering.overwrite_matrix = 1 root = clustering(matrix) def prune(cluster, togo): if cluster.branches: if togo < 0: cluster.branches = None else: for branch in cluster.branches: prune(branch, togo - cluster.height) def listOfClusters0(cluster, alist): if not cluster.branches: alist.append(list(cluster)) else: for branch in cluster.branches: listOfClusters0(branch, alist) def listOfClusters(root): l = [] listOfClusters0(root, l) return l tables = [Orange.data.Table(cluster) for cluster in listOfClusters(root)] prune(root, 1.4) print "%s: %3.0f " % (iris.domain.class_var.values[e], d), print tables = [Orange.data.Table(cluster) for cluster in listOfClusters(root)]
• ## docs/reference/rst/code/svm-linear-weights.py

 r9823 from Orange import data from Orange import data from Orange.classification import svm brown = data.Table("brown-selected") classifier = svm.SVMLearner(brown, kernel_type=svm.kernels.Linear, classifier = svm.SVMLearner(brown, kernel_type=svm.kernels.Linear, normalization=False) weights = svm.get_linear_svm_weights(classifier) print weights print sorted(weights) import pylab as plt plt.hist(weights.values())
• ## docs/reference/rst/code/transformvalue-d2c.py

 r9987 e1 = Orange.feature.Continuous("e=1") e1.getValueFrom = Orange.core.ClassifierFromVar(whichVar = data.domain["e"]) e1.getValueFrom = Orange.core.ClassifierFromVar(whichVar=data.domain["e"]) e1.getValueFrom.transformer = Orange.data.utils.Discrete2Continuous() e1.getValueFrom.transformer.value = int(Orange.data.Value(e, "1"))
• ## docs/reference/rst/code/transformvalue-nc.py

 r9986 newattrs = [] for attr in data.domain.features: attr_c = Orange.feature.Continous(attr.name+"_n") attr_c.getValueFrom = Orange.core.ClassifierFromVar(whichVar = attr) attr_c = Orange.feature.Continuous(attr.name + "_n") attr_c.getValueFrom = Orange.core.ClassifierFromVar(whichVar=attr) transformer = Orange.data.utils.NormalizeContinuous() attr_c.getValueFrom.transformer = transformer
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