rem stringlengths 0 322k | add stringlengths 0 2.05M | context stringlengths 8 228k |
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for attr in self.data.domain.attributes: self.inputAttributesList.insertItem(self.createListItem(attr.varType, attr.name)) if self.data.domain.classVar: self.inputAttributesList.insertItem(self.createListItem(self.data.domain.classVar.varType, self.data.domain.classVar.name)) | for attr in self.data.domain.variables: self.inputAttributesList.insertItem(self.icons[attr.varType], attr.name) | def setInputAttributesListElements(self): self.internalSelectionUpdateFlag = self.internalSelectionUpdateFlag + 1 self.inputAttributesList.clear() if self.data: #set up normal attributes for attr in self.data.domain.attributes: self.inputAttributesList.insertItem(self.createListItem(attr.varType, attr.name)) |
self.inputAttributesList.insertItem(self.createListItem(attr.varType, attr.name)) | self.inputAttributesList.insertItem(self.icons[attr.varType], attr.name) | def setInputAttributesListElements(self): self.internalSelectionUpdateFlag = self.internalSelectionUpdateFlag + 1 self.inputAttributesList.clear() if self.data: #set up normal attributes for attr in self.data.domain.attributes: self.inputAttributesList.insertItem(self.createListItem(attr.varType, attr.name)) |
data.domain.addmeta(orange.newmetaid(), orange.StringVariable("name")) for ex in data: ex["name"] = str(ex.getclass()) | def handleListSelectionChange(self, listBox): if (self.internalSelectionUpdateFlag==0): self.internalSelectionUpdateFlag = self.internalSelectionUpdateFlag + 1 if (self.inputAttributesList<>listBox): self.inputAttributesList.clearSelection() if (self.attributesList<>listBox): self.attributesList.clearSelection() if (se... | |
s += reduce(lambda x,y: x+' : '+y, map(lambda x: "%8.6f"%x[1], filter(lambda x,s=attsel: s[x[0]], enumerate(p)))) | s += reduce(lambda x,y: x+' : '+y, map(lambda x: "%5.3f"%x[1], filter(lambda x,s=attsel: s[x[0]], enumerate(p)))) | def updateTableOutcomes(self): if self.freezeAttChange: # program-based changes should not alter the table immediately return if not self.data or not self.classifiers: return attsel = [self.lbClasses.isSelected(i) for i in range(len(self.data.domain.attributes))] showatt = attsel.count(1) # sindx is the column where t... |
print "NONE" | def cmpclasses(clist): ref = clist[0] for c in clist[1:]: if c<>ref: return 0 return 1 | |
self.recentFiles=[] self.selectedFileName = "None" | self.recentFiles=["(none)"] | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "File Widget") |
box = QHGroupBox("Data File", self.controlArea) self.filecombo=QComboBox(box) | self.box = QHGroupBox("Data File", self.controlArea) self.filecombo=QComboBox(self.box) | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "File Widget") |
button = OWGUI.button(box, self, '...', callback = self.browseFile, disabled=0) | button = OWGUI.button(self.box, self, '...', callback = self.browseFile, disabled=0) | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "File Widget") |
self.setFilelist() | self.setFileList() | def activateLoadedSettings(self): # remove missing data set names self.recentFiles=filter(os.path.exists,self.recentFiles) self.setFilelist() if self.selectedFileName != "": if os.path.exists(self.selectedFileName): self.openFile(self.selectedFileName) else: self.selectedFileName = "" |
if self.selectedFileName != "": if os.path.exists(self.selectedFileName): self.openFile(self.selectedFileName) else: self.selectedFileName = "" | if len(self.recentFiles) > 0 and os.path.exists(self.recentFiles[0]): self.openFile(self.recentFiles[0]) self.connect(self.filecombo, SIGNAL('activated(int)'), self.selectFile) | def activateLoadedSettings(self): # remove missing data set names self.recentFiles=filter(os.path.exists,self.recentFiles) self.setFilelist() if self.selectedFileName != "": if os.path.exists(self.selectedFileName): self.openFile(self.selectedFileName) else: self.selectedFileName = "" |
self.connect(self.filecombo,SIGNAL('activated(int)'),self.selectFile) | def selectFile(self,n): if n < len(self.recentFiles) : name = self.recentFiles[n] self.recentFiles.remove(name) self.recentFiles.insert(0, name) if len(self.recentFiles) > 0: self.setFileList() self.openFile(self.recentFiles[0]) | def activateLoadedSettings(self): # remove missing data set names self.recentFiles=filter(os.path.exists,self.recentFiles) self.setFilelist() if self.selectedFileName != "": if os.path.exists(self.selectedFileName): self.openFile(self.selectedFileName) else: self.selectedFileName = "" |
if self.recentFiles==[]: | if len(self.recentFiles) == 0 or self.recentFiles[0] == "(none)": | def browseFile(self): "Display a FileDialog and select a file" if self.recentFiles==[]: startfile="." else: startfile=self.recentFiles[0] filename=QFileDialog.getOpenFileName(startfile, 'Tab-delimited files (*.tab *.txt)\nC4.5 files (*.data)\nAssistant files (*.dat)\nRetis files (*.rda *.rdo)\nAll files(*.*)', None,'Op... |
filename=QFileDialog.getOpenFileName(startfile, | filename = str(QFileDialog.getOpenFileName(startfile, | def browseFile(self): "Display a FileDialog and select a file" if self.recentFiles==[]: startfile="." else: startfile=self.recentFiles[0] filename=QFileDialog.getOpenFileName(startfile, 'Tab-delimited files (*.tab *.txt)\nC4.5 files (*.data)\nAssistant files (*.dat)\nRetis files (*.rda *.rdo)\nAll files(*.*)', None,'Op... |
None,'Open Orange Data File') self.openFile(str(filename)) | None,'Open Orange Data File')) if filename == "": return if filename in self.recentFiles: self.recentFiles.remove(filename) self.recentFiles.insert(0, filename) self.setFileList() self.openFile(self.recentFiles[0]) def setFileList(self): self.filecombo.clear() for file in self.recentFiles: if file == "(none)": self.... | def browseFile(self): "Display a FileDialog and select a file" if self.recentFiles==[]: startfile="." else: startfile=self.recentFiles[0] filename=QFileDialog.getOpenFileName(startfile, 'Tab-delimited files (*.tab *.txt)\nC4.5 files (*.data)\nAssistant files (*.dat)\nRetis files (*.rda *.rdo)\nAll files(*.*)', None,'Op... |
def xsp(self, l): n = len(l) if n>1: return n, 's' else: return n, '' | def xsp(self, l): n = len(l) if n>1: return n, 's' else: return n, '' | |
"Open a file, create data from it and send it over the data channel" if fn!="(none)": | if fn != "(none)": | def openFile(self,fn): "Open a file, create data from it and send it over the data channel" if fn!="(none)": fileExt=lower(os.path.splitext(fn)[1]) if fileExt in (".txt",".tab",".xls"): data = orange.ExampleTable(fn) elif fileExt in (".c45",): data = orange.C45ExampleGenerator(fn) else: return # update recent file lis... |
self.addFileToList(fn) | def openFile(self,fn): "Open a file, create data from it and send it over the data channel" if fn!="(none)": fileExt=lower(os.path.splitext(fn)[1]) if fileExt in (".txt",".tab",".xls"): data = orange.ExampleTable(fn) elif fileExt in (".c45",): data = orange.C45ExampleGenerator(fn) else: return # update recent file lis... | |
self.selectedFileName = fn | def sp(l): n = len(l) if n <> 1: return n, 's' else: return n, '' | |
def addFileToList(self,fn): if fn in self.recentFiles: self.recentFiles.remove(fn) self.recentFiles.insert(0, fn) self.setFilelist() def setFilelist(self): "Set the GUI filelist" self.filecombo.clear() if self.recentFiles!=[]: for file in self.recentFiles: (dir,filename)=os.path.split(file) self.filecombo.insertIte... | def sp(l): n = len(l) if n <> 1: return n, 's' else: return n, '' | |
OWWidget.__init__(self, parent, signalManager, name, 'Attribute Distance') | OWWidget.__init__(self, parent, signalManager) | def __init__(self, parent=None, signalManager = None, name='AttributeDistance'): self.callbackDeposit = [] # deposit for OWGUI callback functions OWWidget.__init__(self, parent, signalManager, name, 'Attribute Distance') |
self.resize(100,100) | self.resize(215,100) | def __init__(self, parent=None, signalManager = None, name='AttributeDistance'): self.callbackDeposit = [] # deposit for OWGUI callback functions OWWidget.__init__(self, parent, signalManager, name, 'Attribute Distance') |
data = orange.ExampleTable(r'../../doc/datasets/voting') | import os if os.path.isfile(r'../../doc/datasets/voting'): data = orange.ExampleTable(r'../../doc/datasets/voting') else: data = orange.ExampleTable('voting') | def dataset(self, data): if data and len(data.domain.attributes): self.data = orange.Preprocessor_discretize(data, method=orange.EquiNDiscretization(numberOfIntervals=5)) print self.data.domain self.classIntCB.setDisabled(self.data.domain.classVar == None) self.computeMatrix() else: self.send("Distance Matrix", None) |
return testOnData(classifiers, (testset, testweight), testResults, iterationNumber) | return testResults | def learnAndTestOnTestData(learners, learnset, testset, testResults=None, iterationNumber=0, pps=[], **argkw): storeclassifiers = argkw.get("storeclassifiers", 0) or argkw.get("storeClassifiers", 0) learnset, learnweight = demangleExamples(learnset) testset, testweight = demangleExamples(testset) storeclassifiers = ar... |
return testOnData(classifiers, (testset, learnweight), testResults, iterationNumber) | return testResults | def learnAndTestOnLearnData(learners, learnset, testResults=None, iterationNumber=0, pps=[], **argkw): storeclassifiers = argkw.get("storeclassifiers", 0) or argkw.get("storeClassifiers", 0) learnset, learnweight = demangleExamples(learnset) hasLorT = 0 for pp in pps: if pp[0]=="B": learnset = pp[1](learnset) else: h... |
text = _("%(h)d h, %(min)d min, %(sec)d sec") % vars() | text = "%(h)d h, %(min)d min, %(sec)d sec" % vars() | def progressBarSet(self, value): if value > 0: self.progressBarValue = value diff = time.time() - self.startTime total = diff * 100.0/float(value) remaining = max(total - diff, 0) h = int(remaining/3600) min = int((remaining - h*3600)/60) sec = int(remaining - h*3600 - min*60) if h > 0: text = _("%(h)d h, %(min)d min, ... |
text = _("%(min)d min, %(sec)d sec") % vars() self.setCaption(self.captionTitle + _(" (%(value).2f%% complete, remaining time: %(text)s)") % vars()) | text = "%(min)d min, %(sec)d sec" % vars() self.setCaption(self.captionTitle + " (%(value).2f%% complete, remaining time: %(text)s)" % vars()) | def progressBarSet(self, value): if value > 0: self.progressBarValue = value diff = time.time() - self.startTime total = diff * 100.0/float(value) remaining = max(total - diff, 0) h = int(remaining/3600) min = int((remaining - h*3600)/60) sec = int(remaining - h*3600 - min*60) if h > 0: text = _("%(h)d h, %(min)d min, ... |
master._guiElements = getattr(master, "_guiElements", []) + [("spin", wb, value, spinCallback, min, max)] | master._guiElements = getattr(master, "_guiElements", []) + [("spin", wb, value, spinCallback, min, max, spinCallback)] | def checkWithSpin(widget, master, label, min, max, checked, value, posttext = None, step = 1, tooltip=None, checkCallback=None, spinCallback=None, getwidget=None, labelWidth=None, debuggingEnabled = 1): hb = QHBox(widget) wa = checkBox(hb, master, checked, label, callback = checkCallback, labelWidth = labelWidth) wb =... |
examples = examples.select(orange.Domain(attributes, examples.domain.classVar)) | newDomain = orange.Domain(attributes, examples.domain.classVar) newDomain.addmetas(examples.domain.getmetas()) examples = examples.select(newDomain) | def __call__(self, examples, weight=0): imputer = getattr(self, "imputer", None) or None if getattr(self, "removeMissing", 0): examples = orange.Preprocessor_dropMissing(examples) |
def createLogRegExampleTable(data): newDomain = orange.Domain(data.domain.attributes+[data.domain.classVar]) newDomain.addmeta(orange.newmetaid(), orange.FloatVariable("weight")) dataOrig = data.select(newDomain) dataFinal = dataOrig.select(newDomain) for d in dataFinal: d["weight"]=1000000. | def createLogRegExampleTable(data, weightID): setsOfData = [] | def createLogRegExampleTable(data): newDomain = orange.Domain(data.domain.attributes+[data.domain.classVar]) newDomain.addmeta(orange.newmetaid(), orange.FloatVariable("weight")) dataOrig = data.select(newDomain) #original data dataFinal = dataOrig.select(newDomain) #final results will be stored in this object |
newData = orange.ExampleTable(dataOrig) | def createLogRegExampleTable(data): newDomain = orange.Domain(data.domain.attributes+[data.domain.classVar]) newDomain.addmeta(orange.newmetaid(), orange.FloatVariable("weight")) dataOrig = data.select(newDomain) #original data dataFinal = dataOrig.select(newDomain) #final results will be stored in this object | |
newDomain = orange.Domain(dataOrig.domain.attributes+[atDisc,dataOrig.domain.classVar]) for (id, metaVar) in dataOrig.domain.getmetas().items(): newDomain.addmeta(id, metaVar) dataOrig = dataOrig.select(newDomain) dataFinal = dataFinal.select(newDomain) newData = newData.select(newDomain) for d in dataOrig: | newDomain = orange.Domain(data.domain.attributes+[atDisc,data.domain.classVar]) newDomain.addmetas(data.domain.getmetas()) newData = orange.ExampleTable(newDomain,data) altData = orange.ExampleTable(newDomain,data) for i,d in enumerate(newData): | def createLogRegExampleTable(data): newDomain = orange.Domain(data.domain.attributes+[data.domain.classVar]) newDomain.addmeta(orange.newmetaid(), orange.FloatVariable("weight")) dataOrig = data.select(newDomain) #original data dataFinal = dataOrig.select(newDomain) #final results will be stored in this object |
for d in dataFinal: d[atDisc] = 0 for d in newData: | d[weightID] = 1*data[i][weightID] for i,d in enumerate(altData): | def createLogRegExampleTable(data): newDomain = orange.Domain(data.domain.attributes+[data.domain.classVar]) newDomain.addmeta(orange.newmetaid(), orange.FloatVariable("weight")) dataOrig = data.select(newDomain) #original data dataFinal = dataOrig.select(newDomain) #final results will be stored in this object |
d[weightID] = 0.000001*data[i][weightID] elif at.varType == orange.VarTypes.Discrete: | def createLogRegExampleTable(data): newDomain = orange.Domain(data.domain.attributes+[data.domain.classVar]) newDomain.addmeta(orange.newmetaid(), orange.FloatVariable("weight")) dataOrig = data.select(newDomain) #original data dataFinal = dataOrig.select(newDomain) #final results will be stored in this object | |
if at.varType == orange.VarTypes.Discrete: dataOrigOld = orange.ExampleTable(dataOrig) dataFinalOld = orange.ExampleTable(dataFinal) | def createLogRegExampleTable(data): newDomain = orange.Domain(data.domain.attributes+[data.domain.classVar]) newDomain.addmeta(orange.newmetaid(), orange.FloatVariable("weight")) dataOrig = data.select(newDomain) #original data dataFinal = dataOrig.select(newDomain) #final results will be stored in this object | |
newDomain = orange.Domain(filter(lambda x: x!=at, dataOrig.domain.attributes)+[atNew,dataOrig.domain.classVar]) for (id, metaVar) in dataOrig.domain.getmetas().items(): newDomain.addmeta(id, metaVar) dataOrig = dataOrig.select(newDomain) dataFinal = dataFinal.select(newDomain) newData = newData.select(newDomain) for d ... | newDomain = orange.Domain(filter(lambda x: x!=at, data.domain.attributes)+[atNew,data.domain.classVar]) newDomain.addmetas(data.domain.getmetas()) newData = orange.ExampleTable(newDomain,data) altData = orange.ExampleTable(newDomain,data) for i,d in enumerate(newData): d[atNew] = data[i][at] d[weightID] = 1*data[i][wei... | def createLogRegExampleTable(data): newDomain = orange.Domain(data.domain.attributes+[data.domain.classVar]) newDomain.addmeta(orange.newmetaid(), orange.FloatVariable("weight")) dataOrig = data.select(newDomain) #original data dataFinal = dataOrig.select(newDomain) #final results will be stored in this object |
for at in newData: at["weight"]=0.1 for d in newData: dataFinal.append(orange.Example(d)) return dataFinal def findZero(model, at): if at.varType == orange.VarTypes.Discrete: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"="+at.name+"X": return model.beta[i_a+1] els... | d[weightID] = 0.000001*data[i][weightID] newData.extend(altData) setsOfData.append(newData) return setsOfData learner = LogRegLearner(imputer = orange.ImputerConstructor_average(), removeSingular = self.removeSingular) | def createLogRegExampleTable(data): newDomain = orange.Domain(data.domain.attributes+[data.domain.classVar]) newDomain.addmeta(orange.newmetaid(), orange.FloatVariable("weight")) dataOrig = data.select(newDomain) #original data dataFinal = dataOrig.select(newDomain) #final results will be stored in this object |
orig_model = LogRegLearner(examples) | orig_model = learner(examples,weight) if orig_model.fit_status: print "Warning: model did not converge" | def findZero(model, at): if at.varType == orange.VarTypes.Discrete: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"="+at.name+"X": return model.beta[i_a+1] else: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"Disc": retu... |
extended_examples = createLogRegExampleTable(examples) extended_model = LogRegLearner(extended_examples, extended_examples.domain.getmeta("weight")) | if weight == 0: weight = orange.newmetaid() examples.addMetaAttribute(weight, 1.0) extended_set_of_examples = createLogRegExampleTable(examples, weight) extended_models = [learner(extended_examples, weight) \ for extended_examples in extended_set_of_examples] | def findZero(model, at): if at.varType == orange.VarTypes.Discrete: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"="+at.name+"X": return model.beta[i_a+1] else: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"Disc": retu... |
for at in range(len(nexamples.domain.attributes)): att = nexamples.domain.attributes[at] beta_add = findZero(extended_model, att) | for m in extended_models: beta_add = m.beta[m.continuizedDomain.attributes[-1]] | def findZero(model, at): if at.varType == orange.VarTypes.Discrete: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"="+at.name+"X": return model.beta[i_a+1] else: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"Disc": retu... |
logistic_prior = extended_model.beta[0]+beta | logistic_prior = orig_model.beta[0]+beta | def findZero(model, at): if at.varType == orange.VarTypes.Discrete: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"="+at.name+"X": return model.beta[i_a+1] else: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"Disc": retu... |
bayes = orange.BayesLearner(nexamples) | bayes = orange.BayesLearner(examples) | def findZero(model, at): if at.varType == orange.VarTypes.Discrete: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"="+at.name+"X": return model.beta[i_a+1] else: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"Disc": retu... |
k = (bayes_prior-extended_model.beta[0])/(logistic_prior-extended_model.beta[0]) betas_ap = [k*x for x in betas_ap] | dif = bayes_prior - logistic_prior positives = sum(filter(lambda x: x>=0, betas_ap)) negatives = -sum(filter(lambda x: x<0, betas_ap)) if not negatives == 0: kPN = positives/negatives diffNegatives = dif/(1+kPN) diffPositives = kPN*diffNegatives kNegatives = (negatives-diffNegatives)/negatives kPositives = positives/(p... | def findZero(model, at): if at.varType == orange.VarTypes.Discrete: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"="+at.name+"X": return model.beta[i_a+1] else: for i_a in range(len(model.domain.attributes)): if model.domain.attributes[i_a].name == at.name+"Disc": retu... |
return 0 | llh = 0.0 for i,x_i in enumerate(x): pr = Pr(x_i,betas) llh += y[i]*log(max(pr,1e-6)) + (1-y[i])*log(max(1-pr,1e-6)) return llh def diag(vector): mat = identity(len(vector), Float) for i,v in enumerate(vector): mat[i][i] = v return mat | def lh(x,y,betas): return 0 |
def __init__(self, penalty=0): | def __init__(self, penalty=0, se_penalty = False): | def __init__(self, penalty=0): self.penalty = penalty |
oldBetas = array([1.0] * (len(data.domain.attributes)+1)) | def __call__(self, data, weight=0): ml = data.native(0) for i in range(len(data.domain.attributes)): a = data.domain.attributes[i] if a.varType == orange.VarTypes.Discrete: for m in ml: m[i] = a.values.index(m[i]) for m in ml: m[-1] = data.domain.classVar.values.index(m[-1]) | |
likelihood = 0 for i in range(10): | pen_matrix = array([self.penalty] * (len(data.domain.attributes)+1)) if self.se_penalty: | def __call__(self, data, weight=0): ml = data.native(0) for i in range(len(data.domain.attributes)): a = data.domain.attributes[i] if a.varType == orange.VarTypes.Discrete: for m in ml: m[i] = a.values.index(m[i]) for m in ml: m[-1] = data.domain.classVar.values.index(m[-1]) |
tmpA = inverse(matrixmultiply(transpose(X), matrixmultiply(W, X))+self.penalty*identity(len(data.domain.attributes)+1, Float)) tmpB = matrixmultiply(transpose(X), matrixmultiply(W, z)) betas = matrixmultiply(tmpA, tmpB) likelihood_new = lh(X,y,betas) likelihood = likelihood_new | tmpA = inverse(matrixmultiply(transpose(X), matrixmultiply(W, X))+diag(pen_matrix)) tmpB = matrixmultiply(transpose(X), y-p) betas = oldBetas + matrixmultiply(tmpA,tmpB) likelihood_new = lh(X,y,betas)-self.penalty*sum([b*b for b in betas]) print likelihood_new | def __call__(self, data, weight=0): ml = data.native(0) for i in range(len(data.domain.attributes)): a = data.domain.attributes[i] if a.varType == orange.VarTypes.Discrete: for m in ml: m[i] = a.values.index(m[i]) for m in ml: m[-1] = data.domain.classVar.values.index(m[-1]) |
XX = sqrt(diagonal(inverse(matrixmultiply(transpose(X),X)))) yhat = array([Pr(X[i], betas) for i in range(len(data))]) ss = sum((y - yhat) ** 2) / (N - len(data.domain.attributes) - 1) sigma = math.sqrt(ss) | p = array([Pr(X[i], betas) for i in range(len(data))]) W = identity(len(data), Float) pp = p * (1.0-p) for i in range(N): W[i,i] = pp[i] diXWX = sqrt(diagonal(inverse(matrixmultiply(transpose(X), matrixmultiply(W, X))))) xTemp = matrixmultiply(matrixmultiply(inverse(matrixmultiply(transpose(X), matrixmultiply(W, X))),t... | def __call__(self, data, weight=0): ml = data.native(0) for i in range(len(data.domain.attributes)): a = data.domain.attributes[i] if a.varType == orange.VarTypes.Discrete: for m in ml: m[i] = a.values.index(m[i]) for m in ml: m[-1] = data.domain.classVar.values.index(m[-1]) |
def estimateBeta(self,X,y,betas,const_betas,X_anch,y_anch): N,N_anch = len(y),len(y_anch) r,r_anch = array([dot(X[i], betas) for i in range(N)]),\ array([dot(X_anch[i], betas) for i in range(N_anch)]) p = array([Pr_bx(ri) for ri in r]) X_sq = X*X max_delta = [1.]*len(const_betas) likelihood = -1.e+10 likelihood_new = ... | def __call__(self, data, weight=0): ml = data.native(0) for i in range(len(data.domain.attributes)): a = data.domain.attributes[i] if a.varType == orange.VarTypes.Discrete: for m in ml: m[i] = a.values.index(m[i]) for m in ml: m[-1] = data.domain.classVar.values.index(m[-1]) | |
print tempData.domain | def __call__(self, examples): if getattr(self, "imputer", 0): examples = self.imputer(examples)(examples) if getattr(self, "removeMissing", 0): examples = orange.Preprocessor_dropMissing(examples) attr = [] remain_attr = examples.domain.attributes[:] | |
tempData = orange.Preprocessor_dropMissing(examples.select(tempDomain)) | tempDomain = continuizer(orange.Preprocessor_dropMissing(examples.select(tempDomain))) tempData = orange.Preprocessor_dropMissing(examples.select(tempDomain)) | def __call__(self, examples): if getattr(self, "imputer", 0): examples = self.imputer(examples)(examples) if getattr(self, "removeMissing", 0): examples = orange.Preprocessor_dropMissing(examples) attr = [] remain_attr = examples.domain.attributes[:] |
tempData = orange.Preprocessor_dropMissing(examples.select(tempDomain)) print tempData.domain | tempDomain = continuizer(orange.Preprocessor_dropMissing(examples.select(tempDomain))) tempData = orange.Preprocessor_dropMissing(examples.select(tempDomain)) | def __call__(self, examples): if getattr(self, "imputer", 0): examples = self.imputer(examples)(examples) if getattr(self, "removeMissing", 0): examples = orange.Preprocessor_dropMissing(examples) attr = [] remain_attr = examples.domain.attributes[:] |
if not bestAt: stop = 1 continue | def __call__(self, examples): if getattr(self, "imputer", 0): examples = self.imputer(examples)(examples) if getattr(self, "removeMissing", 0): examples = orange.Preprocessor_dropMissing(examples) attr = [] remain_attr = examples.domain.attributes[:] | |
self.optimizationDlg = kNNOptimization(None, self.signalManager, self.graph, "Polyviz") self.graph.kNNOptimization = self.optimizationDlg self.optimizationDlg.optimizeGivenProjectionButton.show() | def __init__(self,parent=None, signalManager = None): OWWidget.__init__(self, parent, signalManager, "Polyviz", TRUE) | |
if colorIndex != -1 and self.rawdata[i][colorIndex].isSpecial() == 1: continue | if not validData[i]: continue | def updateData(self, xAttr, yAttr, colorAttr, shapeAttr = "", sizeShapeAttr = "", showColorLegend = 0, labelAttr = None, **args): self.removeDrawingCurves() # my function, that doesn't delete selection curves self.removeMarkers() self.tips.removeAll() if not self.enabledLegend: self.enableLegend(0) #self.enableLegend(... |
if hasattr(self, "split"): | hasSplit = hasattr(self, "split") if hasSplit: | def instance(self): learner = orange.TreeLearner() |
self.graph.anchorData = orangeom.optimizeAnchors(Numeric.transpose(self.graph.scaledData).tolist(), classes, self.graph.anchorData, self.attractG, -self.repelG, steps) | ai = self.graph.attributeNameIndex attrIndices = [ai[label] for label in self.getShownAttributeList()] self.graph.anchorData = orangeom.optimizeAnchors(Numeric.transpose(self.graph.scaledData).tolist(), classes, self.graph.anchorData, attrIndices, self.attractG, -self.repelG, steps) | def freeAttributes(self, iterations, steps): attrList = self.getShownAttributeList() classes = [int(x.getclass()) for x in self.graph.rawdata] for i in range(iterations): self.graph.anchorData = orangeom.optimizeAnchors(Numeric.transpose(self.graph.scaledData).tolist(), classes, self.graph.anchorData, self.attractG, -s... |
class OWPrediction(OWWidget): | class OWPredictions(OWWidget): | def paint(self, painter, colorgroup, rect, selected): g = QColorGroup(colorgroup) g.setColor(QColorGroup.Base, Qt.lightGray) QTableItem.paint(self, painter, g, rect, selected) |
ow = OWPrediction() | ow = OWPredictions() | def cmpclasses(clist): ref = clist[0] for c in clist[1:]: if c<>ref: return 0 return 1 |
elif 0: | elif 1: | def cmpclasses(clist): ref = clist[0] for c in clist[1:]: if c<>ref: return 0 return 1 |
if not brightness: return color | if brightness == None: return color | def __getitem__(self, index, brightness = None): if type(index) == tuple: index, brightness = index if self.numberOfColors == -1: # is this color for continuous attribute? col = QColor() col.setHsv(index*self.maxHueVal, brightness or 255, 255) # index must be between 0 and 1 return col else: if index < len(sel... |
color.setHsv(h, brightness, v) | color.setHsv(h, int(brightness), v) | def __getitem__(self, index, brightness = None): if type(index) == tuple: index, brightness = index if self.numberOfColors == -1: # is this color for continuous attribute? col = QColor() col.setHsv(index*self.maxHueVal, brightness or 255, 255) # index must be between 0 and 1 return col else: if index < len(sel... |
(string,ok) = QInputDialog.getText("Rename", "Enter new name for the widget \"" + exName + "\":", exName) if ok and self.tempWidget != None and str(string) != exName: | (newName ,ok) = QInputDialog.getText("Rename", "Enter new name for the widget \"" + exName + "\":", exName) newName = str(newName) if ok and self.tempWidget != None and newName != exName: | def renameActiveWidget(self): exName = str(self.tempWidget.caption) (string,ok) = QInputDialog.getText("Rename", "Enter new name for the widget \"" + exName + "\":", exName) if ok and self.tempWidget != None and str(string) != exName: for widget in self.doc.widgets: if widget.caption.lower() == str(string).lower(): QMe... |
if widget.caption.lower() == str(string).lower(): QMessageBox.critical(self,'Qrange Canvas','Unable to rename widget. An instance with that name already exists.', QMessageBox.Ok + QMessageBox.Default) | if widget.caption.lower() == newName.lower(): QMessageBox.critical(self,'Orange Canvas','Unable to rename widget. An instance with that name already exists.', QMessageBox.Ok + QMessageBox.Default) | def renameActiveWidget(self): exName = str(self.tempWidget.caption) (string,ok) = QInputDialog.getText("Rename", "Enter new name for the widget \"" + exName + "\":", exName) if ok and self.tempWidget != None and str(string) != exName: for widget in self.doc.widgets: if widget.caption.lower() == str(string).lower(): QMe... |
self.tempWidget.updateText(string) | self.tempWidget.updateText(newName) | def renameActiveWidget(self): exName = str(self.tempWidget.caption) (string,ok) = QInputDialog.getText("Rename", "Enter new name for the widget \"" + exName + "\":", exName) if ok and self.tempWidget != None and str(string) != exName: for widget in self.doc.widgets: if widget.caption.lower() == str(string).lower(): QMe... |
cache.findNearest = orange.FindNearestConstructor_BruteForce(cache.data, distanceConstructor=orange.ExamplesDistanceConstructor_Euclidean(), includeSame=False) | cache.findNearest = orange.FindNearestConstructor_BruteForce(cache.data, distanceConstructor=orange.ExamplesDistanceConstructor_Euclidean(), includeSame=True) | def tubedRegression(cache, dimensions, progressCallback = None): if not cache.findNearest: cache.findNearest = orange.FindNearestConstructor_BruteForce(cache.data, distanceConstructor=orange.ExamplesDistanceConstructor_Euclidean(), includeSame=False) if not cache.attrStat: cache.attrStat = orange.DomainBasicAttrStat(c... |
nn = cache.findNearest(ref_example, cache.nNeighbours, True) | nn = cache.findNearest(ref_example, 0, True) | def tubedRegression(cache, dimensions, progressCallback = None): if not cache.findNearest: cache.findNearest = orange.FindNearestConstructor_BruteForce(cache.data, distanceConstructor=orange.ExamplesDistanceConstructor_Euclidean(), includeSame=False) if not cache.attrStat: cache.attrStat = orange.DomainBasicAttrStat(c... |
for ex in nn: | for ex in nn[:effNeighbours]: | def tubedRegression(cache, dimensions, progressCallback = None): if not cache.findNearest: cache.findNearest = orange.FindNearestConstructor_BruteForce(cache.data, distanceConstructor=orange.ExamplesDistanceConstructor_Euclidean(), includeSame=False) if not cache.attrStat: cache.attrStat = orange.DomainBasicAttrStat(c... |
if div and n>=3: | if div: | def tubedRegression(cache, dimensions, progressCallback = None): if not cache.findNearest: cache.findNearest = orange.FindNearestConstructor_BruteForce(cache.data, distanceConstructor=orange.ExamplesDistanceConstructor_Euclidean(), includeSame=False) if not cache.attrStat: cache.attrStat = orange.DomainBasicAttrStat(c... |
import orngMisc if MQCNotation: qVar = orange.EnumVariable("Q", values = ["M%s(%s)" % ("".join(["+-"[x] for x in v if x<2]), ", ".join([cache.attributes[i][0] for i,x in zip(dimensions, v) if x<2])) for v in orngMisc.LimitedCounter([3]*nDimensions)]) else: qVar = orange.EnumVariable("Q", values = ["M(%s)" % ", ".join([... | qVar = createClassVar([cache.attributes[i][0] for i in dimensions], MQCNotation) | def createQTable(cache, data, dimensions, outputAttr = -1, threshold = 0, MQCNotation = False, derivativeAsMeta = False, differencesAsMeta = False, originalAsMeta = False): nDimensions = len(dimensions) needQ = outputAttr < 0 or derivativeAsMeta if needQ: import orngMisc if MQCNotation: qVar = orange.EnumVariable("Q",... |
if isCDTEmpty(CDT[0]): | if isCDTEmpty(CDTs[0]): | def computeCDT(res, classIndex=-1, **argkw): """Obsolete, don't use""" import corn if classIndex<0: if res.baseClass>=0: classIndex = res.baseClass else: classIndex = 1 useweights = res.weights and not argkw.get("unweighted", 0) weightByClasses = argkw.get("weightByClasses", True) if (res.numberOfIterations>1): CDTs ... |
cdt = cdtComputer(*(ite, ) + computerArgs) if not isCDTEmpty(cdt[0]): return [x[-1]/divideByIfIte for x in ROCsFromCDT(cdt)], True | cdts = cdtComputer(*(ite, ) + computerArgs) if not isCDTEmpty(cdts[0]): return [(cdt.C+cdt.T/2)/(cdt.C+cdt.D+cdt.T)/divideByIfIte for cdt in cdts], True | def AUC_x(cdtComputer, ite, all_ite, divideByIfIte, computerArgs): cdt = cdtComputer(*(ite, ) + computerArgs) if not isCDTEmpty(cdt[0]): return [x[-1]/divideByIfIte for x in ROCsFromCDT(cdt)], True if all_ite: cdt = cdtComputer(*(all_ite, ) + computerArgs) if not isCDTEmpty(cdt[0]): return [x[-1] for x in ROCsFromCDT(... |
return [x[-1] for x in ROCsFromCDT(cdt)], False | return [(cdt.C+cdt.T/2)/(cdt.C+cdt.D+cdt.T) for cdt in cdts], False | def AUC_x(cdtComputer, ite, all_ite, divideByIfIte, computerArgs): cdt = cdtComputer(*(ite, ) + computerArgs) if not isCDTEmpty(cdt[0]): return [x[-1]/divideByIfIte for x in ROCsFromCDT(cdt)], True if all_ite: cdt = cdtComputer(*(all_ite, ) + computerArgs) if not isCDTEmpty(cdt[0]): return [x[-1] for x in ROCsFromCDT(... |
return AUC_i(res, -1, useWeights) | return AUC_i(res, -1, useWeights)[0] | def AUC_binary(res, useWeights = True): if res.numberOfIterations > 1: return AUC_iterations(AUC_i, splitByIterations(res), (-1, useWeights, res, res.numberOfIterations)) else: return AUC_i(res, -1, useWeights) |
return (beta, coeffs, coeff_names, basis, m, lambda x:math.exp(x)/(1.0+math.exp(x))) | return (beta, coeffs, coeff_names, basis, m, lambda x:math.exp(x)/(1.0+math.exp(x)), 0) | def __call__(self,classifier,examples, buckets): # todo todo - support for loess for i in xrange(len(examples.domain.attributes)): for j in xrange(len(examples)): if examples[j][i].isSpecial(): raise "A missing value found in instance %d, attribute %s. Missing values are not allowed."%(j,examples.domain.attributes[i].n... |
return (beta, coeffs, coeff_names, basis, m, lambda x:math.exp(x)/(1.0+math.exp(x))) | return (beta, coeffs, coeff_names, basis, m, lambda x:math.exp(x)/(1.0+math.exp(x)),0) | def __call__(self,classifier,examples, buckets): # skip domain translation and masking robustc = classifier.classifier primitivec = robustc.classifier beta = -primitivec.beta[0] |
if classifier.model["label"][0] == 0: beta = -beta xcoeffs = [-x for x in xcoeffs] flip = 1 else: flip = 0 | def __call__(self,classifier,examples, buckets): if classifier.model['kernel_type'] != 0: raise "Use SVM with a linear kernel." if classifier.model["svm_type"] != 0: raise "Use ordinary SVM classification." if classifier.model["nr_class"] != 2: raise "This is not SVM with a binary class." | |
return (beta, coeffs, coeff_names, basis, m, _treshold) | return (beta, coeffs, coeff_names, basis, m, _treshold, flip) | def __call__(self,classifier,examples, buckets): if classifier.model['kernel_type'] != 0: raise "Use SVM with a linear kernel." if classifier.model["svm_type"] != 0: raise "Use ordinary SVM classification." if classifier.model["nr_class"] != 2: raise "This is not SVM with a binary class." |
def __init__(self,coeff,estdomain,estimator): | def __init__(self,coeff,estdomain,estimator,flip): | def __init__(self,coeff,estdomain,estimator): self.coeff = coeff self.estdomain = estdomain self.cv = self.estdomain.classVar(0) self.estimator = estimator |
ex = orange.Example(self.estdomain,[r*self.coeff,self.cv]) | if self.flip: ex = orange.Example(self.estdomain,[r*self.coeff,self.cv]) else: ex = orange.Example(self.estdomain,[-r*self.coeff,self.cv]) | def __call__(self, r): # got a margin ex = orange.Example(self.estdomain,[r*self.coeff,self.cv]) # need a dummy class value p = self.estimator(ex,orange.GetProbabilities) return p[1] |
(beta, coeffs, coeff_names, basis, m, _probfunc) = self.parser(classifier.classifier,examples, buckets) return (beta, coeffs, coeff_names, basis, m, _marginConverter(self.marginc.coeff, self.marginc.estdomain, self.marginc.estimator)) | (beta, coeffs, coeff_names, basis, m, _probfunc, flip) = self.parser(classifier.classifier,examples, buckets) return (beta, coeffs, coeff_names, basis, m, _marginConverter(self.marginc.coeff, self.marginc.estdomain, self.marginc.estimator, flip), 0) | def __call__(self,classifier,examples, buckets): (beta, coeffs, coeff_names, basis, m, _probfunc) = self.parser(classifier.classifier,examples, buckets) return (beta, coeffs, coeff_names, basis, m, _marginConverter(self.marginc.coeff, self.marginc.estdomain, self.marginc.estimator)) |
(beta, coeffs, coeff_names, basis, m, probfunc) = parser(classifier,examples, buckets) | (beta, coeffs, coeff_names, basis, m, probfunc, flip) = parser(classifier,examples, buckets) | def __init__(self, examples, classifier, dimensions = 2, buckets = 3, getpies = 0, getexamples = 1): # error detection if len(examples.domain.classVar.values) != 2: raise "The domain does not have a binary class. Binary class is required." |
t = orange.ExampleTable('c:/proj/domains/d_pima.tab') | t = orange.ExampleTable('c:/proj/domains/voting.tab') | def printpie(e,p): x = 0 for i in range(len(m.coeff_names)): for j in m.coeff_names[i][1:]: if e[x] > 0.001: print '\t%2.1f%% : '%(100*e[x]),m.coeff_names[i][0],'=', if type(j)==type(1.0): print t[idx][m.coeff_names[i][0]] # continuous else: print j # discrete x += 1 if e[x] > 0.001: print '\t%2.1f%% : '%(100*e[x]),"BA... |
self.evaluationTime = 2 | def __init__(self): self.attributeCount = 2 self.optimizationType = MAXIMUM_NUMBER_OF_ATTRS self.qualityMeasure = INFORMATION_GAIN self.attrDisc = MEAS_RELIEFF self.percentDataUsed = 100 | |
self.aprioriDistribution = orange.Distribution(self.data.domain.classVar.name, self.data) | self.aprioriDistribution = orange.Distribution(self.data.domain.classVar.name, self.data) self.aprioriProbabilities = [nrOfCases / float(len(self.data)) for nrOfCases in self.aprioriDistribution] | def setData(self, data): self.data = None self.evaluatedAttributes = None self.aprioriDistribution = None self.logits = {} self.arguments = {} self.classVals = [] |
def isOptimizationCanceled(self): return (time.time() - self.startTime) / 60 >= self.evaluationTime | def getContingencys(self, attrs, data = None): if not data: data = self.data conts = {} for attr in attrs: conts[attr] = orange.ContingencyAttrClass(attr, data) return conts def isEvaluationCanceled(self): stop = 0 if self.timeLimit > 0: stop = (time.time() - self.startTime) / 60 >= self.timeLimit return stop | def isOptimizationCanceled(self): return (time.time() - self.startTime) / 60 >= self.evaluationTime |
triedPossibilities = 0; totalPossibilities = 0 | def evaluateProjections(self): if not self.data or not self.classVals: return | |
if self.optimizationType == 0: totalPossibilities = orngVisFuncts.combinationsCount(self.attributeCount, len(evaluatedAttrs)) else: for i in range(1, self.attributeCount+1): totalPossibilities += orngVisFuncts.combinationsCount(i, len(evaluatedAttrs)) | triedPossibilities = 0; totalPossibilities = 0 for i in range(minLength, maxLength+1): totalPossibilities += orngVisFuncts.combinationsCount(i, len(evaluatedAttrs)) totalStr = orngVisFuncts.createStringFromNumber(totalPossibilities) self.cvIndices = None | def evaluateProjections(self): if not self.data or not self.classVals: return |
if self.isOptimizationCanceled(): | if self.isEvaluationCanceled(): | def evaluateProjections(self): if not self.data or not self.classVals: return |
triedPossibilities += 1 | def evaluateProjections(self): if not self.data or not self.classVals: return | |
self.setStatusBarText("Evaluated %s visualizations..." % (orngVisFuncts.createStringFromNumber(triedPossibilities))) | self.setStatusBarText("Evaluated %s/%s visualizations..." % (orngVisFuncts.createStringFromNumber(triedPossibilities), totalStr)) | def evaluateProjections(self): if not self.data or not self.classVals: return |
if self.testingMethod == PROPORTION_TEST: pick = orange.MakeRandomIndices2(stratified = orange.MakeRandomIndices.StratifiedIfPossible, p0 = 0.7, randomGenerator = 0) indices = [pick(d) for i in range(10)] elif self.testingMethod == CROSSVALIDATION: ind = orange.MakeRandomIndicesCV(d, 10, randomGenerator = 0, stratified... | if not self.cvIndices: if self.testingMethod == PROPORTION_TEST: pick = orange.MakeRandomIndices2(stratified = orange.MakeRandomIndices.StratifiedIfPossible, p0 = 0.7, randomGenerator = 0) self.cvIndices = [pick(d) for i in range(10)] elif self.testingMethod == CROSSVALIDATION: ind = orange.MakeRandomIndicesCV(d, 10, r... | def _Evaluate(self, attrs): newFeature, quality = FeatureByCartesianProduct(self.data, attrs) retVal = -1 if self.qualityMeasure in [CHI_SQUARE, CRAMERS_PHI]: aprioriSum = sum(self.aprioriDistribution) retVal = 0.0 |
for ind in indices: | for ind in self.cvIndices: | def _Evaluate(self, attrs): newFeature, quality = FeatureByCartesianProduct(self.data, attrs) retVal = -1 if self.qualityMeasure in [CHI_SQUARE, CRAMERS_PHI]: aprioriSum = sum(self.aprioriDistribution) retVal = 0.0 |
def findTargetIndex(self, score, funct): top = 0; bottom = len(self.results) while (bottom-top) > 1: mid = (bottom + top)/2 if funct(score, self.results[mid][SCORE]) == score: bottom = mid else: top = mid if len(self.results) == 0: return 0 if funct(score, self.results[top][SCORE]) == score: return top else: return ... | def _Evaluate(self, attrs): newFeature, quality = FeatureByCartesianProduct(self.data, attrs) retVal = -1 if self.qualityMeasure in [CHI_SQUARE, CRAMERS_PHI]: aprioriSum = sum(self.aprioriDistribution) retVal = 0.0 | |
predictions = [] for val in self.classVals: predictions.append(self.logits[val] + sum([v[0] for v in self.arguments[val]])) | predictions = [self.logits[val] + sum([v[0] for v in self.arguments[val]]) for val in self.classVals] | def findArguments(self, example = None): self.arguments = dict([(val, []) for val in self.classVals]) |
probabilities = [] for val in predictions: if val < -50: p = 0 else: p = e**val / (1 + e**val) probabilities.append(p) | probabilities = [e**val / (1 + e**val) for val in predictions] | def findArguments(self, example = None): self.arguments = dict([(val, []) for val in self.classVals]) |
aprioriProbabilities = [nrOfCases / float(lenData) for nrOfCases in self.aprioriDistribution] | def evaluateArgument(self, example, attrList, score): attrVals = [example[attr] for attr in attrList] if "?" in attrVals: return None # the testExample has a missing value at one of the visualized attributes | |
if (1-actualProbabilities[i]) * aprioriProbabilities[i] > 0.0: val = (actualProbabilities[i] * (1-aprioriProbabilities[i])) / ((1-actualProbabilities[i]) * aprioriProbabilities[i]) | if (1-actualProbabilities[i]) * self.aprioriProbabilities[i] > 0.0: val = (actualProbabilities[i] * (1-self.aprioriProbabilities[i])) / ((1-actualProbabilities[i]) * self.aprioriProbabilities[i]) | def evaluateArgument(self, example, attrList, score): attrVals = [example[attr] for attr in attrList] if "?" in attrVals: return None # the testExample has a missing value at one of the visualized attributes |
aprioriPc0 = max(approxZero, aprioriProbabilities[i]); aprioriPc1 = max(approxZero, 1-aprioriProbabilities[i]) | aprioriPc0 = max(approxZero, self.aprioriProbabilities[i]); aprioriPc1 = max(approxZero, 1-self.aprioriProbabilities[i]) | def evaluateArgument(self, example, attrList, score): attrVals = [example[attr] for attr in attrList] if "?" in attrVals: return None # the testExample has a missing value at one of the visualized attributes |
aprioriProbabilities = [nrOfCases / float(lenData) for nrOfCases in aprioriDistribution] actualProbabilities = [] | def estimateClassProbabilities(self, data, example, attrList, subData = None, subDataDistribution = None, aprioriDistribution = None, probabilityEstimation = -1, mValue = -1): if probabilityEstimation == -1: probabilityEstimation = self.probabilityEstimation if aprioriDistribution == None: aprioriDistribution = self.ap... |
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