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for i in range(len(self.learners)): self.tab.setText(i, 0, self.learners[i].name) | for i in range(len(self.results.classifierNames)): self.tab.setText(i, 0, self.results.classifierNames[i]) | def setStatTable(self): self.tab.setNumCols(len(self.stat)+1) self.tabHH=self.tab.horizontalHeader() self.tabHH.setLabel(0, 'Classifier') for i in range(len(self.stat)): self.tabHH.setLabel(i+1, self.stat[i][1]) |
l1 = orange.BayesLearner() l1.name = 'Naive Bayes' | l1 = orange.MajorityLearner() l1.name = 'Maj 1' | def finish(self): self.widget.progressBarFinished() |
ow.learner(l3, 1) import orngTree l4 = orngTree.TreeLearner(minSubset=2) l4.name = "Decision Tree" | ow.learner(l3, 3) l1.name = 'Maj 1 updated' ow.learner(l1, 1) ow.learner(None, 2) l4 = orange.MajorityLearner() l4.name = "Maj 2" | def finish(self): self.widget.progressBarFinished() |
ow.learner(None, 1) | def finish(self): self.widget.progressBarFinished() | |
buttonSize = QSize(40, 30) upDownButtonSize = QSize(37,30) | def __init__(self,parent = None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Data Domain") #initialize base class | |
self.space.setMinimumSize(QSize(650,500)) self.hbox = QHBox(self.space) self.hbox.setSpacing(10) self.vbox1 = QVBox(self.hbox) self.vbox1.setSpacing(10) self.vframe2 = QFrame(self.hbox) self.vframe2Layout = QGridLayout(self.vframe2, 9, 1, 0, 0) self.vframe3 = QFrame(self.hbox) self.vframe3Layout = QBoxLayout(self.vfr... | self.mainArea.setFixedWidth(1) ca=QFrame(self.controlArea) ca.adjustSize() gl=QGridLayout(ca,4,3,5) boxAvail = QVGroupBox(ca) boxAvail.setTitle('Available Attributes') gl.addMultiCellWidget(boxAvail, 0,2,0,0) self.inputAttributesList = QListBox(boxAvail,'InputAttributes') | def __init__(self,parent = None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Data Domain") #initialize base class |
self.horizontalAttributesFrame = QFrame(self.vframe2) self.vframe2Layout.addMultiCellWidget(self.horizontalAttributesFrame,0,3, 0,0,0) self.horizontalAttributesFrameLayout = QHBoxLayout(self.horizontalAttributesFrame, 5, 5) self.attributesButton = OWGUI.button(self.horizontalAttributesFrame, self, ">",self.onAttribute... | vbAttr = QVBox(ca) gl.addWidget(vbAttr, 0,1) self.attributesButtonUp = OWGUI.button(vbAttr, self, "Up", self.onAttributesButtonUpClick) self.attributesButton = OWGUI.button(vbAttr, self, ">",self.onAttributesButtonClicked) self.attributesButtonDown = OWGUI.button(vbAttr, self, "Down", self.onAttributesButtonDownClick) ... | def __init__(self,parent = None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Data Domain") #initialize base class |
self.connect(self.attributesList, SIGNAL('currentChanged(QListBoxItem*)'), self.onAttributesCurrentChange) self.verticalAttributeFrame = QFrame(self.horizontalAttributesFrame) self.horizontalAttributesFrameLayout.addWidget(self.verticalAttributeFrame, 0, Qt.AlignLeft) self.verticalAttributeFrameLayout = QVBoxLayout(se... | self.connect(self.attributesList, SIGNAL('currentChanged(QListBoxItem*)'), self.onAttributesCurrentChange) self.connect(self.attributesList, SIGNAL('doubleClicked(QListBoxItem*)'), self.onAttributesDoubleClick) self.classButton = OWGUI.button(ca, self, ">", self.onClassButtonClicked) gl.addWidget(self.classButton, 1,... | def __init__(self,parent = None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Data Domain") #initialize base class |
self.horizontalClassFrameLayout.addStretch(200) self.horizontalMetaFrame = QFrame(self.vframe2) self.vframe2Layout.addMultiCellWidget(self.horizontalMetaFrame,6,9,0,0,0) self.horizontalMetaFrameLayout = QHBoxLayout(self.horizontalMetaFrame, 5, 5) self.metaButton = OWGUI.button(self.horizontalMetaFrame, self, ">",self... | self.connect(self.classList, SIGNAL('doubleClicked(QListBoxItem*)'), self.onClassDoubleClick) vbMeta = QVBox(ca) gl.addWidget(vbMeta, 2,1) self.metaButtonUp = OWGUI.button(vbMeta, self, "Up", self.onMetaButtonUpClick) self.metaButton = OWGUI.button(vbMeta, self, ">",self.onMetaButtonClicked) self.metaButtonDown = OWG... | def __init__(self,parent = None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Data Domain") #initialize base class |
self.verticalMetaFrame = QFrame(self.horizontalMetaFrame) self.horizontalMetaFrameLayout.addWidget(self.verticalMetaFrame, 0, Qt.AlignLeft) self.verticalMetaFrameLayout = QVBoxLayout(self.verticalMetaFrame,5,5) self.metaButtonUp = OWGUI.button(self.verticalMetaFrame, self, "Up", self.onMetaButtonUpClick) self.metaButto... | self.connect(self.metaList, SIGNAL('doubleClicked(QListBoxItem*)'), self.onMetaDoubleClick) boxApply = QHBox(ca) gl.addMultiCellWidget(boxApply, 3,3,0,2) self.applyButton = OWGUI.button(boxApply, self, "Apply", callback = self.setOutput) self.applyButton.setEnabled(False) self.applyButton.setMaximumWidth(101) self.re... | def __init__(self,parent = None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Data Domain") #initialize base class |
self.applyButton.setEnabled(False) attributes = []; for i in range(0, self.attributesList.count()): attributes.append(self.data.domain[str(self.attributesList.text(i))]) if self.classList.count()>0: domain = orange.Domain(attributes, self.data.domain[str(self.classList.text(0))],self.data.domain) | if self.data: self.applyButton.setEnabled(False) attributes = []; for i in range(0, self.attributesList.count()): attributes.append(self.data.domain[str(self.attributesList.text(i))]) if self.classList.count()>0: domain = orange.Domain(attributes, self.data.domain[str(self.classList.text(0))],self.data.domain) else: ... | def setOutput(self): self.applyButton.setEnabled(False) attributes = []; for i in range(0, self.attributesList.count()): attributes.append(self.data.domain[str(self.attributesList.text(i))]) #create domain without class attribute if self.classList.count()>0: domain = orange.Domain(attributes, self.data.domain[str(self... |
domain = orange.Domain(attributes, None,self.data.domain) for i in range(0,self.metaList.count()): domain.addmeta(orange.newmetaid(), self.data.domain[str(self.metaList.text(i))]) newdata = orange.ExampleTable(domain, self.data) newdata.name = self.data.name self.send("OutputData", newdata) if self.classList.count()>... | self.send("OutputData", None) | def setOutput(self): self.applyButton.setEnabled(False) attributes = []; for i in range(0, self.attributesList.count()): attributes.append(self.data.domain[str(self.attributesList.text(i))]) #create domain without class attribute if self.classList.count()>0: domain = orange.Domain(attributes, self.data.domain[str(self... |
self.handleListSelectionChange(self.metaList) | self.handleListSelectionChange(self.metaList) def onAttributesDoubleClick(self, item): self.onAttributesButtonClicked() def onClassDoubleClick(self, item): self.onClassButtonClicked() def onMetaDoubleClick(self, item): self.onMetaButtonClicked() | def onMetaCurrentChange(self, item): self.handleListSelectionChange(self.metaList) |
for key in self.links.keys(): links = self.links[key] for i in range(len(links)): (widget, nameFrom, nameTo, e) = links[i] if widget == widgetTo: links[i] = (widget, nameFrom, nameTo, enabled) if enabled: widgetTo.updateNewSignalData(widgetFrom, nameTo, widgetFrom.linksOut[nameFrom][0], widgetFrom.linksOut[nameFrom][1]... | links = self.links[widgetFrom] for i in range(len(links)): (widget, nameFrom, nameTo, e) = links[i] if widget == widgetTo: links[i] = (widget, nameFrom, nameTo, enabled) if enabled: widgetTo.updateNewSignalData(widgetFrom, nameTo, widgetFrom.linksOut[nameFrom][0], widgetFrom.linksOut[nameFrom][1]) if enabled: self.pro... | def setLinkEnabled(self, widgetFrom, widgetTo, enabled): for key in self.links.keys(): links = self.links[key] for i in range(len(links)): (widget, nameFrom, nameTo, e) = links[i] if widget == widgetTo: links[i] = (widget, nameFrom, nameTo, enabled) if enabled: widgetTo.updateNewSignalData(widgetFrom, nameTo, widgetFro... |
f.write("\t%d -> %d [URL=\"%d-%d\",dir=%s,%scolor=%s,label=\"%s%%\",weight=%d];\n"%(i,j,min(i,j),max(i,j),dir,style,color,mc,(perc/30+1))) | f.write("\t%d -> %d [URL=\"%d-%d\",dir=%s,%scolor=%s,label=\"%s\",weight=%d];\n"%(i,j,min(i,j),max(i,j),dir,style,color,mc,(perc/30+1))) | def exportGraph(self, f, absolute_int=10, positive_int = 0, negative_int = 0, best_attributes = 0, print_bits = 1, black_white = 0, significant_digits = 2, postscript = 1, pretty_names = 1, url = 0, widget_coloring=1, pcutoff = 1): NA = len(self.names) |
OWGUI.radioButtonsInBox(opt, self, "RefreshMode", ["Every step", "Every 10 steps", "Every 100 steps"], "Refresh after optimization") | OWGUI.radioButtonsInBox(opt, self, "RefreshMode", ["Every step", "Every 10 steps", "Every 100 steps"], "Refresh After Optimization") | def __init__(self, parent=None, signalManager=None, name="Multi Dimensional Scaling"): OWWidget.__init__(self, parent, signalManager, name) |
for k in self.lineKeys: removeCurve(k) | def removeCurve(keys): self.removeCurve(keys[0]) if len(keys)==2: self.removeCurve(keys[1]) | |
listSet = \ """ cd = &%(containertype)s::st_classDescription; cd->name = "%(name)s"; cd->type = &typeid(%(containertype)s>); cd->base = &TOrange::st_classDescription; cd->properties = TOrange_properties; cd->components = TOrange_components; """ | listdef = re.compile(r'\s*#define\s+(?P<name>\w+)\s+(?P<containertype>((_?TOrangeVector)|(TOrangeMap_K?V?))[<].+[>])') | |
class ListDefinition: def __init__(self, name, elementtype): self.name = name self.elementtype = elementtype | def __init__(self, name, parent, abstract = 0): if name == "type": print 0 self.name = name self.parent = parent self.abstract = abstract self.properties = [] self.components = [] self.confirmed = 0 self.extended = 0 | |
found = listdef.search(line) if found: lists.append(found.group("name", "containertype")+(hppfile,)) | found = listdef.match(line) if found: | def detectBuiltInProperties(hppfile): ff = open(hppfile, "rt") istdidt = hppfile=="tdidt.hpp" iscallback = hppfile=="callback.hpp" currentClass = None candidate = candidateBase = "" lcount = 0 for line in ff: lcount += 1 if istdidt: found = tdidtdef.match(line) if found: storeClass(currentClass, hppfile) currentClass... |
def renewFile(pppfile, newfile): oldexists = os.path.isfile(pppfile) if oldexists: if not samefiles(pppfile, newfile): os.remove(pppfile) os.rename(newfile, pppfile) print "Renewing " + pppfile else: os.remove(newfile) else: os.rename(newfile, pppfile) print "Creating " + pppfile | def detectBuiltInProperties(hppfile): ff = open(hppfile, "rt") istdidt = hppfile=="tdidt.hpp" iscallback = hppfile=="callback.hpp" currentClass = None candidate = candidateBase = "" lcount = 0 for line in ff: lcount += 1 if istdidt: found = tdidtdef.match(line) if found: storeClass(currentClass, hppfile) currentClass... | |
off.write(('TClassDescription %s::st_classDescription("%s", &typeid(%s), &%s::st_classDescription, %s_properties, %s_components);\n' + | off.write(('TClassDescription %s::st_classDescription = { "%s", &typeid(%s), &%s::st_classDescription, %s_properties, %s_components };\n' + | for classdef in files[hppfile]: classname = classdef.name off.write("\n\n/****** %s *****/\n\n" % classname) off.write("TPropertyDescription %s_properties[] = {\n" % classname) for ctype, cname, pname, pdesc, ro, ob, builtin in classdef.properties: if builtin or (ctype=="TExample"): if type(pname)==tuple: off.write('... |
off.write(('TClassDescription %s::st_classDescription("%s", &typeid(%s), NULL, %s_properties, %s_components );\n' + | off.write(('TClassDescription %s::st_classDescription = { "%s", &typeid(%s), NULL, %s_properties, %s_components };\n' + | for classdef in files[hppfile]: classname = classdef.name off.write("\n\n/****** %s *****/\n\n" % classname) off.write("TPropertyDescription %s_properties[] = {\n" % classname) for ctype, cname, pname, pdesc, ro, ob, builtin in classdef.properties: if builtin or (ctype=="TExample"): if type(pname)==tuple: off.write('... |
renewFile(pppfile, newfile) def writeLists(): newfile = "ppp/orvector.ppp.new" pppfile = "ppp/orvector.ppp" off = open(newfile, "wt") off.write(notice % "orvector.hpp" + "\n\n") includefiles = dict([(n[2], 0) for n in lists]) includefiles = includefiles.keys() includefiles.sort() for i in includefiles: off.write('... | oldexists = os.path.isfile(pppfile) if oldexists: if not samefiles(pppfile, newfile): os.remove(pppfile) os.rename(newfile, pppfile) print "Renewing " + pppfile else: os.remove(newfile) else: os.rename(newfile, pppfile) print "Creating " + pppfile | for classdef in files[hppfile]: classname = classdef.name off.write("\n\n/****** %s *****/\n\n" % classname) off.write("TPropertyDescription %s_properties[] = {\n" % classname) for ctype, cname, pname, pdesc, ro, ob, builtin in classdef.properties: if builtin or (ctype=="TExample"): if type(pname)==tuple: off.write('... |
lists = [] maps = [] | def writeLists(): newfile = "ppp/orvector.ppp.new" pppfile = "ppp/orvector.ppp" off = open(newfile, "wt") off.write(notice % "orvector.hpp" + "\n\n") includefiles = dict([(n[2], 0) for n in lists]) includefiles = includefiles.keys() includefiles.sort() for i in includefiles: off.write('#include "%s"\n' % i) off.wri... | |
writeLists() | def writeLists(): newfile = "ppp/orvector.ppp.new" pppfile = "ppp/orvector.ppp" off = open(newfile, "wt") off.write(notice % "orvector.hpp" + "\n\n") includefiles = dict([(n[2], 0) for n in lists]) includefiles = includefiles.keys() includefiles.sort() for i in includefiles: off.write('#include "%s"\n' % i) off.wri... | |
self.removeCurve(self.zoomKey) self.tempSelectionCurve = None | def onMouseReleased(self, e): self.mouseCurrentlyPressed = 0 self.mouseCurrentButton = 0 | |
def __init__(self, master, buttons): | def __init__(self, master, buttons, callback = None): | def __init__(self, master, buttons): self.master = master self.buttons = buttons |
if self.callback: self.callback(self) | def __call__(self, *a): self.master.grandTreatment = 3 for i, b in enumerate(self.buttons): b.setOn(i == 3) | |
self.inputs = [("Classified Examples", ExampleTableWithClass, self.cdata, Default)] | self.inputs = [("Classified Examples", ExampleTableWithClass, self.cdata, Default), ("Learner for Imputation", orange.Learner, self.setModel)] | def __init__(self,parent=None, signalManager = None, name = "Imputer"): OWWidget.__init__(self, parent, signalManager, name) self.inputs = [("Classified Examples", ExampleTableWithClass, self.cdata, Default)] self.outputs = [("Classified Examples", ExampleTableWithClass, Default), ("Imputer", orange.ImputerConstructor... |
self.lastDomain = None | self.individual = None | def __init__(self,parent=None, signalManager = None, name = "Imputer"): OWWidget.__init__(self, parent, signalManager, name) self.inputs = [("Classified Examples", ExampleTableWithClass, self.cdata, Default)] self.outputs = [("Classified Examples", ExampleTableWithClass, Default), ("Imputer", orange.ImputerConstructor... |
QWidget(self.controlArea).setFixedSize(19, 8) | OWGUI.separator(self.controlArea, 19, 8) | def __init__(self,parent=None, signalManager = None, name = "Imputer"): OWWidget.__init__(self, parent, signalManager, name) self.inputs = [("Classified Examples", ExampleTableWithClass, self.cdata, Default)] self.outputs = [("Classified Examples", ExampleTableWithClass, Default), ("Imputer", orange.ImputerConstructor... |
self.data = None | def __init__(self,parent=None, signalManager = None, name = "Imputer"): OWWidget.__init__(self, parent, signalManager, name) self.inputs = [("Classified Examples", ExampleTableWithClass, self.cdata, Default)] self.outputs = [("Classified Examples", ExampleTableWithClass, Default), ("Imputer", orange.ImputerConstructor... | |
self.adjustSize() | def __init__(self,parent=None, signalManager = None, name = "Imputer"): OWWidget.__init__(self, parent, signalManager, name) self.inputs = [("Classified Examples", ExampleTableWithClass, self.cdata, Default)] self.outputs = [("Classified Examples", ExampleTableWithClass, Default), ("Imputer", orange.ImputerConstructor... | |
if self.noButtonsSet or self.grandTreatment < 3: | if (self.noButtonsSet or self.grandTreatment < 3) and hasattr(self, "lines"): | def setGridButtons(self): if self.noButtonsSet or self.grandTreatment < 3: for l in self.lines: for i, b in enumerate(l): b.setOn(self.grandTreatment==i) self.noButtonsSet = 0 |
self.imputer = None | self.imputer = orange.ImputerConstructor_model(self.model or orange.MajorityLearner()) | def constructImputer(self, *a): if self.grandTreatment == 0: self.imputer = orange.ImputerConstructor_average(imputeClass = self.imputeClass) elif self.grandTreatment == 1: # not implemented yet self.imputer = None elif self.grandTreatment == 2: self.imputer = orange.ImputerConstructor_random(imputeClass = self.imputeC... |
self.imputer = None | if self.data: imputerConstructors = [] for i, line in enumerate(self.lines): if line[1].isOn(): imputerConstructors.append(self.model or orange.MajorityLearner()) elif line[2].isOn(): imputerConstructors.append(orange.RandomLearner()) elif line[3].isOn(): attr = self.data.domain[i] if attr.varType == orange.VarTypes.Di... | def constructImputer(self, *a): if self.grandTreatment == 0: self.imputer = orange.ImputerConstructor_average(imputeClass = self.imputeClass) elif self.grandTreatment == 1: # not implemented yet self.imputer = None elif self.grandTreatment == 2: self.imputer = orange.ImputerConstructor_random(imputeClass = self.imputeC... |
self.openContext("", data) def setModel(self, model): self.model = model self.sendIf() | def cdata(self,data): if not data: self.data = None self.send("Classified Examples", None) else: if not self.data or data.domain != self.data.domain: self.data = data if not data.domain.classVar: self.imputeClass = 0 self.cbImputeClass.setDisabled(True) else: self.cbImputeClass.setDisabled(False) pass self.updateRadios... | |
main = QGroupBox("Individual settings", self.mainArea) | if self.individual: self.mainArea.removeChild(self.individual) main = self.individual = QGroupBox("Individual settings", self.mainArea) | def updateRadios(self): if not self.data: return attributes = self.data.domain lastColumn = len(self.generalTreats) |
self.connect(cb, SIGNAL("activated ( int )"), EditClicked(self, thisLine)) | self.connect(cb, SIGNAL("activated ( int )"), EditClicked(self, thisLine, self.editClicked)) self.lineInputs.append(cb) | def updateRadios(self): if not self.data: return attributes = self.data.domain lastColumn = len(self.generalTreats) |
self.connect(cb, SIGNAL("textChanged ( const QString & )"), EditClicked(self, thisLine)) hbox.show() self.setGridButtons() | self.connect(cb, SIGNAL("textChanged ( const QString & )"), EditClicked(self, thisLine, self.editClicked)) self.lineInputs.append(cb) main.updateGeometry() main.show() | def updateRadios(self): if not self.data: return attributes = self.data.domain lastColumn = len(self.generalTreats) |
self.mainArea.adjustSize() | self.updateGeometry() cr = self.childrenRect() self.setFixedSize(cr.width(), cr.height()) | def updateRadios(self): if not self.data: return attributes = self.data.domain lastColumn = len(self.generalTreats) |
ow.cdata(data) | def updateRadios(self): if not self.data: return attributes = self.data.domain lastColumn = len(self.generalTreats) | |
OWWidget.__init__(self, parent, signalManager, "Categorize") | OWWidget.__init__(self, parent, signalManager, "Discretize") | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") |
self.methods = [("Entropy-based discretization", orange.EntropyDiscretization()), ("Equal-Frequency Intervals", orange.EquiNDiscretization()), ("Equal-Width Intervals", orange.EquiDistDiscretization())] | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") | |
self.catBox = QVGroupBox(self.controlArea) self.catBox.setTitle('Categorization Method') QToolTip.add(self.catBox,"Method that will be used for categorization") | box = OWGUI.widgetBox(self.controlArea, "Discretization") | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") |
self.catMethods=["Entropy-based discretization", "Equal-Frequency Intervals", "Equal-Width Intervals"] self.cat = QComboBox(self.catBox) for cm in self.catMethods: self.cat.insertItem(cm) self.cat.setCurrentItem(self.Categorization) | items = [x[0] for x in self.methods] self.methodBtns = OWGUI.radioButtonsInBox(box, self, "Categorization", items, callback=self.setCatMethod, box="Method") | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") |
self.nIntLab = QLabel("Number of intervals: %i" % self.NumberOfIntervals, self.controlArea) self.nInt = QSlider(2, 20, 1, self.NumberOfIntervals, QSlider.Horizontal, self.controlArea) self.nInt.setTickmarks(QSlider.Below) self.nIntLab.setDisabled(self.Categorization==0) | self.nInt = OWGUI.qwtHSlider(box, self, "NumberOfIntervals", label="Intervals: ", minValue=2, maxValue=20, step=1, precision=0, maxWidth=120).box | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") |
QWidget(self.controlArea).setFixedSize(16, 16) sp = QSpacerItem(20,20) | OWGUI.button(box, self, "&Apply", callback = self.categorize) | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") |
self.applyBtn = QPushButton("&Apply", self.controlArea) QWidget(self.controlArea).setFixedSize(16, 16) self.showInt = QCheckBox("Show &Intervals", self.controlArea) self.showInt.setChecked(self.ShowIntervals) | OWGUI.radioButtonsInBox(self.controlArea, self, "ShowIntervals", ["Cut-Off Points", "Intervals"], box="Show", callback=self.setTable) | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") |
self.g.setTitle('Categorization Results') | self.g.setTitle('Discretization Results') | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") |
self.connect(self.cat,SIGNAL("activated(int)"), self.setCatMethod) self.connect(self.nInt,SIGNAL("valueChanged(int)"), self.setNInt) self.connect(self.applyBtn,SIGNAL("clicked()"),self.categorize) self.connect(self.showInt,SIGNAL("toggled(bool)"), self.showIntervals) | def data(self, dataset): self.dataset=dataset if not self.dataset: self.res.setNumRows(0) self.remList.clear() return if self.dataset.domain.classVar and self.dataset.domain.classVar.varType == orange.VarTypes.Continuous: self.methodBtns.buttons[0].setDisabled(1) if self.Categorization == 0: self.setCatMethod(1) | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") |
self.resize(500,500) def data(self,dataset): self.dataset=dataset | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Categorize") | |
self.res.setNumRows(len(self.discretizedAtts) - len(self.removedAtt)) | self.res.setNumRows(len(self.discretizedAtts) - len(self.removed)) | def setTable(self): self.res.setNumCols(2) self.res.setNumRows(len(self.discretizedAtts) - len(self.removedAtt)) |
self.resHeader.setLabel(1, 'Values') | self.resHeader.setLabel(1, ["Cut-Off Values", "Intervals"][self.ShowIntervals]) | def setTable(self): self.res.setNumCols(2) self.res.setNumRows(len(self.discretizedAtts) - len(self.removedAtt)) |
if 'D_' + att not in self.removedAtt: self.res.setText(i, 0, att) | if 'D_' + att.name not in removed: self.res.setText(i, 0, att.name) | def setTable(self): self.res.setNumCols(2) self.res.setNumRows(len(self.discretizedAtts) - len(self.removedAtt)) |
values = reduce(lambda x,y: x+', '+y, self.catData.domain['D_'+att].values) | values = reduce(lambda x,y: x+', '+y, self.discData.domain['D_'+att.name].values) | def setTable(self): self.res.setNumCols(2) self.res.setNumRows(len(self.discretizedAtts) - len(self.removedAtt)) |
discretizer = self.catData.domain['D_'+att].getValueFrom.transformer | discretizer = self.discData.domain['D_'+att.name].getValueFrom.transformer | def setTable(self): self.res.setNumCols(2) self.res.setNumRows(len(self.discretizedAtts) - len(self.removedAtt)) |
if len(self.removedAtt)==0: | if len(self.removed)==0: | def setTable(self): self.res.setNumCols(2) self.res.setNumRows(len(self.discretizedAtts) - len(self.removedAtt)) |
for i in self.removedAtt: self.remList.insertItem(i[2:]) | for i in self.removed: self.remList.insertItem(i.name[2:]) | def setTable(self): self.res.setNumCols(2) self.res.setNumRows(len(self.discretizedAtts) - len(self.removedAtt)) |
self.discretizedAtts = [] for a in self.dataset.domain.attributes: if a.varType == orange.VarTypes.Continuous: self.discretizedAtts.append(a.name) | self.discretizedAtts = filter(lambda x: x.varType == orange.VarTypes.Continuous, self.dataset.domain.attributes) | def categorize(self): if self.dataset == None: return |
catMethod = self.Categorization if catMethod == 0: discretizer = orange.EntropyDiscretization() else: nInt = self.NumberOfIntervals if catMethod==1: discretizer = orange.EquiNDiscretization(numberOfIntervals=nInt) else: discretizer = orange.EquiDistDiscretization(numberOfIntervals=nInt) self.catData = orange.Preproces... | discretizer = self.methods[self.Categorization][1] discretizer.numberOfIntervals=int(self.NumberOfIntervals) self.discData = orange.Preprocessor_discretize(self.dataset, method=discretizer) | def categorize(self): if self.dataset == None: return |
attrlist = [] self.removedAtt = [] nrem=0 for i in self.catData.domain.attributes: if (len(i.values)>1): attrlist.append(i) else: self.removedAtt.append(i.name) attrlist.append(self.catData.domain.classVar) self.newData = self.catData.select(attrlist) | self.kept = filter(lambda x: len(x.values)>1, self.discData.domain.attributes) self.removed = filter(lambda x: len(x.values)<=1, self.discData.domain.attributes) self.kept.append(self.discData.domain.classVar) self.newData = self.discData.select(self.kept) | def categorize(self): if self.dataset == None: return |
def setCatMethod(self, value): self.Categorization = value self.nIntLab.setDisabled(value==0) self.nInt.setDisabled(value==0) def setNInt(self, value): if str(value) == '': value = '5' v = int(str(value)) if (v<2) or (v>20): v = 5 self.nIntLab.setText ("Number Of Intervals: %i" % self.NumberOfIntervals) self.NumberOfI... | def setCatMethod(self, method = None): if method <> None: self.Categorization = method self.nInt.setDisabled(self.Categorization==0) | def categorize(self): if self.dataset == None: return |
dataset = orange.ExampleTable(r'..\datasets\adult_sample') ow.cdata(dataset) | dataset = orange.ExampleTable(r'../../doc/datasets/adult_sample') ow.data(dataset) | def showIntervals(self, value): self.ShowIntervals = value self.setTable() |
equal = fnew.read() == fold.read() | equal = [rstrip(x) for x in fnew.readlines()] == [rstrip(x) for x in fold.readlines()] | def samefiles(name): fnew, fold = open(newname % name, "rt"), open(oldname % name, "rt") equal = fnew.read() == fold.read() fnew.close() fold.close() return equal |
if data.domain.classVar.varType == orange.VarTypes.Discrete: if self.kValueFormula == 0: self.kValue = int(sqrt(len(data))) elif self.kValueFormula == 1: self.kValue = int(len(data) / len(data.domain.classVar.values)) else: self.kValue = 10 | if self.kValueFormula == 0 or not data.domain.classVar or data.domain.classVar.varType == orange.VarTypes.Continuous: self.kValue = int(sqrt(len(data))) elif self.kValueFormula == 1: self.kValue = int(len(data) / len(data.domain.classVar.values)) | def setData(self, data): self.data = data |
self.data = None self.testdata = None self.learnDict = {}; self.learners = [] self.results = None; self.scores = None | self.data = None self.testdata = None self.learners = None self.results = None self.scores = None | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "TestLearners") self.inputs = [("Data", ExampleTableWithClass, self.cdata, Default), ("Separate Test Data", ExampleTableWithClass, self.testdata), ("Learner", orange.Learner, self.learner, Multiple)] self.outputs = [("... |
self.resize(500,400) | self.resize(600,400) | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "TestLearners") self.inputs = [("Data", ExampleTableWithClass, self.cdata, Default), ("Separate Test Data", ExampleTableWithClass, self.testdata), ("Learner", orange.Learner, self.learner, Multiple)] self.outputs = [("... |
if self.results and learner: | if not self.data: return if learner: | def test(self, learner=None): # testing if self.results and learner: learners = [learner] else: learners = self.learnDict.values() if not learners: return |
learners = self.learnDict.values() if not learners: return | learners = self.learners | def test(self, learner=None): # testing if self.results and learner: learners = [learner] else: learners = self.learnDict.values() if not learners: return |
pb = ProgressBar(self, iterations=self.nFolds) | pb = None | def test(self, learner=None): # testing if self.results and learner: learners = [learner] else: learners = self.learnDict.values() if not learners: return |
res = orngTest.leaveOneOut(learners, self.data) | pb = ProgressBar(self, iterations=len(self.data)) res = orngTest.leaveOneOut(learners, self.data, callback=pb.advance) | def test(self, learner=None): # testing if self.results and learner: learners = [learner] else: learners = self.learnDict.values() if not learners: return |
res = orngTest.proportionTest(learners, self.data, self.pLearning/100., times=self.pRepeat) | pb = ProgressBar(self, iterations=self.pRepeat) res = orngTest.proportionTest(learners, self.data, self.pLearning/100., times=self.pRepeat, callback=pb.advance) | def test(self, learner=None): # testing if self.results and learner: learners = [learner] else: learners = self.learnDict.values() if not learners: return |
if len(self.learners) > len(self.scores[0]): | if learner.id not in [l.id for l in self.learners]: | def test(self, learner=None): # testing if self.results and learner: learners = [learner] else: learners = self.learnDict.values() if not learners: return |
indx = self.learners.index(learner) | indx = [l.id for l in self.learners].index(learner.id) | def test(self, learner=None): # testing if self.results and learner: learners = [learner] else: learners = self.learnDict.values() if not learners: return |
self.scores.append([-1 for c in range(len(self.learners))]) | self.scores.append([-1 for c in range(len(self.results.learners))]) | def test(self, learner=None): # testing if self.results and learner: learners = [learner] else: learners = self.learnDict.values() if not learners: return |
pb.finish() | if pb: pb.finish() | def test(self, learner=None): # testing if self.results and learner: learners = [learner] else: learners = self.learnDict.values() if not learners: return |
self.setStatTable() | def cdata(self, data): if not data: self.data = None self.results = None self.setStatTable() return # have to handle this appropriately if self.testdata and data.domain <> self.testdata.domain: self.testdata = None self.results = None self.setStatTable() self.data = orange.Filter_hasClassValue(data) self.classindex = 0... | |
if not learner: indx = self.learners.index(self.learnDict[id]) | if learner: learner.id = id self.test(learner) if self.learners: if id not in [l.id for l in self.learners]: self.learners.append(learner) else: self.learners = [learner] self.applyBtn.setDisabled(FALSE) else: print 'REMOVE', id, 'FROM', self.learners ids = [l.id for l in self.learners] if id not in ids: return indx =... | def learner(self, learner, id=None): if not learner: # remove a learner and corresponding results # print 'Remove', id indx = self.learners.index(self.learnDict[id]) if self.results: for i,r in enumerate(self.results.results): del r.classes[indx] del r.probabilities[indx] del self.results.classifierNames[indx] self.res... |
del self.results.classifierNames[indx] self.results.numberOfLearners -= 1 for (i, stat) in enumerate(self.stat): del self.scores[i][indx] | del self.results.classifierNames[indx] self.results.numberOfLearners -= 1 for (i, stat) in enumerate(self.stat): del self.scores[i][indx] self.setStatTable() self.send("Evaluation Results", self.results) | def learner(self, learner, id=None): if not learner: # remove a learner and corresponding results # print 'Remove', id indx = self.learners.index(self.learnDict[id]) if self.results: for i,r in enumerate(self.results.results): del r.classes[indx] del r.probabilities[indx] del self.results.classifierNames[indx] self.res... |
del self.learnDict[id] self.setStatTable() self.send("Evaluation Results", self.results) else: if not self.learnDict.has_key(id): self.learners.append(learner) else: self.learners[self.learners.index(self.learnDict[id])] = learner self.learnDict[id] = learner if self.data: self.test(learner) self.applyBtn.setDisabled(... | def learner(self, learner, id=None): if not learner: # remove a learner and corresponding results # print 'Remove', id indx = self.learners.index(self.learnDict[id]) if self.results: for i,r in enumerate(self.results.results): del r.classes[indx] del r.probabilities[indx] del self.results.classifierNames[indx] self.res... | |
self.results = None | def sChanged(self, value, id): if self.sampleMethod <> id: self.sampleMethod = id self.results = None if self.data: self.test() | |
self.tab.setNumRows(len(self.learners)) | self.tab.setNumRows(self.results.numberOfLearners) | def setStatTable(self): if not self.results: self.tab.setNumRows(0) return self.tab.setNumCols(len(self.stat)+1) self.tabHH=self.tab.horizontalHeader() self.tabHH.setLabel(0, 'Classifier') for i in range(len(self.stat)): self.tabHH.setLabel(i+1, self.stat[i][1]) |
for i in range(len(self.learners)): | for i in range(self.results.numberOfLearners): | def setStatTable(self): if not self.results: self.tab.setNumRows(0) return self.tab.setNumCols(len(self.stat)+1) self.tabHH=self.tab.horizontalHeader() self.tabHH.setLabel(0, 'Classifier') for i in range(len(self.stat)): self.tabHH.setLabel(i+1, self.stat[i][1]) |
testcase = 0 | testcase = 1 | def finish(self): self.widget.progressBarFinished() |
ow.learner(None, 2) | ow.cdata(data) | def finish(self): self.widget.progressBarFinished() |
if x2-x1 != 0: m1 = (y2-y1)/(x2-x1) else: m1 = 1e+12 if X2-X1 != 0: m2 = (Y2-Y1)/(X2-X1) else: m2 = 1e+12; b1 = -1 b2 = -1 c1 = (y1-m1*x1) c2 = (Y1-m2*X1) det_inv = 1/(m1*b2 - m2*b1) xi=((b1*c2 - b2*c1)*det_inv) yi=((m2*c1 - m1*c2)*det_inv) | if min(x1,x2) > max(X1, X2) or max(x1,x2) < min(X1,X2): return (0, 0, 0) if min(y1,y2) > max(Y1, Y2) or max(y1,y2) < min(Y1,Y2): return (0, 0, 0) if x2-x1 != 0: k1 = (y2-y1)/(x2-x1) else: k1 = 1e+12 if X2-X1 != 0: k2 = (Y2-Y1)/(X2-X1) else: k2 = 1e+12 c1 = (y1-k1*x1) c2 = (Y1-k2*X1) if k1 == 1e+12... | def lineIntersection(self, x1, y1, x2, y2, X1, Y1, X2, Y2): if x2-x1 != 0: m1 = (y2-y1)/(x2-x1) else: m1 = 1e+12 if X2-X1 != 0: m2 = (Y2-Y1)/(X2-X1) else: m2 = 1e+12; |
minExamples = self.preNodeInst and self.preNodeInstP, minSubset = self.preLeafInst and self.preLeafInstP, maxMajority = self.preNodeMaj and self.preNodeMajP/100.0, | def setLearner(self): if hasattr(self, "btnApply"): self.btnApply.setFocus() self.learner = orngTree.TreeLearner(measure = self.measures[self.estim][1], reliefK = self.relK, reliefM = self.relM, binarization = self.bin, minExamples = self.preNodeInst and self.preNodeInstP, minSubset = self.preLeafInst and self.preLeafI... | |
mForPruning = self.postMPruning and self.postM, | def setLearner(self): if hasattr(self, "btnApply"): self.btnApply.setFocus() self.learner = orngTree.TreeLearner(measure = self.measures[self.estim][1], reliefK = self.relK, reliefM = self.relM, binarization = self.bin, minExamples = self.preNodeInst and self.preNodeInstP, minSubset = self.preLeafInst and self.preLeafI... | |
if self.preNodeInst: self.learner.minExamples = self.preNodeInstP if self.preLeafInst: self.learner.minSubset = self.preLeafInstP if self.preNodeMaj: self.learner.maxMajority = self.preNodeMajP / 100.0 if self.postMPruning: self.learner.mForPruning = self.postM | def setLearner(self): if hasattr(self, "btnApply"): self.btnApply.setFocus() self.learner = orngTree.TreeLearner(measure = self.measures[self.estim][1], reliefK = self.relK, reliefM = self.relM, binarization = self.bin, minExamples = self.preNodeInst and self.preNodeInstP, minSubset = self.preLeafInst and self.preLeafI... | |
returnTable.append(1-temp) | returnTable.append(1-(temp/float(lenClassValues))) | def kNNClassifyData(self, table): temp = 0.0 knn = orange.kNNLearner(table, k=self.kValue, rankWeight = 0) returnTable = [] if table.domain.classVar.varType == orange.VarTypes.Discrete: classValues = list(table.domain.classVar.values) for j in range(len(table)): qApp.processEvents() # allow processing of other ... |
addResultFunct(valueDict[key], closureDict[key], polygonVerticesDict[key], permutationAttributes, int(graph.objects[polygonVerticesDict[key][0]].getclass()), enlargedClosureDict[key], otherDict[key]) classesDict[key] = int(graph.objects[polygonVerticesDict[key][0]].getclass()) | addResultFunct(valueDict[key], closureDict[key], polygonVerticesDict[key], permutationAttributes, otherDict[key][OTHER_CLASS], enlargedClosureDict[key], otherDict[key]) classesDict[key] = otherDict[key][OTHER_CLASS] | def getOptimalClusters(self, attributes, minLength, maxLength, addResultFunct): dataSize = len(self.rawdata) self.triedPossibilities = 0 |
addResultFunct(valueDict[key], closureDict[key], polygonVerticesDict[key], attrs, self.rawdata.domain.classVar.values.index(otherDict[key][0]), enlargedClosureDict[key], otherDict[key]) classesDict[key] = self.rawdata.domain.classVar.values.index(otherDict[key][0]) | addResultFunct(valueDict[key], closureDict[key], polygonVerticesDict[key], attrs, otherDict[key][OTHER_CLASS], enlargedClosureDict[key], otherDict[key]) classesDict[key] = otherDict[key][OTHER_CLASS] | def getOptimalClusters(self, attributes, minLength, maxLength, addResultFunct): dataSize = len(self.rawdata) self.triedPossibilities = 0 |
print "NEW" | def __new__(cls, examples=None, **kwds): print "NEW" learner = object.__new__(cls, **kwds) if examples: return learner(examples) else: return learner | |
print 'INIT' | def __init__(self, m=0.0, name='std naive bayes', **kwds): print 'INIT' self.__dict__ = kwds self.m = m self.name = name | |
print 'CALL' | def __call__(self, examples, weight=None, **kwds): print 'CALL' for k in kwds.keys(): self.__dict__[k] = kwds[k] domain = examples.domain | |
self.graph.anchorsData = anchors self.graph.updateData(self.getShownAttributeList()) self.graph.repaint() | self.graph.anchorData = anchors self.updateGraph() | def randomAnchors(self): import random attrList = self.getShownAttributeList() anchors = [self.ranch(a) for a in attrList] if not self.lockToCircle: maxdist = math.sqrt(max([x[0]**2+x[1]**2 for x in anchors])) anchors = [(x[0]/maxdist, x[1]/maxdist, x[2]) for x in anchors] self.graph.anchorsData = anchors self.graph.up... |
print m | def freeAttributes(self, iterations, steps, singleStep = False): attrList = self.getShownAttributeList() classes = [int(x.getclass()) for x in self.graph.rawdata] optimizer = self.lockToCircle and orangeom.optimizeAnchorsRadial or orangeom.optimizeAnchors ai = self.graph.attributeNameIndex attrIndices = [ai[label] for ... | |
while len(attrs2) < len(contAttrs): | ableToAdd = 1 while ableToAdd: ableToAdd = 0 | def evaluateAttributes(data, contMeasure, discMeasure): attrs = []; contAttrs = [] corr = MeasureCorrelation() for attr in data.domain.attributes: if data.domain.classVar.varType == orange.VarTypes.Continuous and attr.varType == orange.VarTypes.Continuous: attrs.append((corr(attr.name, data), attr.name)) elif data.doma... |
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