def _getInstances(self, classAttr):
# create attributes
self.classAttr = classAttr
attName2Obj = {}
attVector = FastVector()
for attName in self.numericAttributes:
attr = Attribute(attName)
attVector.addElement(attr)
attName2Obj[attName] = attr
for (attName, domain) in self.attName2Domain.iteritems():
vDomain = FastVector(len(domain))
for v in domain:
#print v
vDomain.addElement(String(str(v)))
attr = Attribute(attName, vDomain)
attVector.addElement(attr)
attName2Obj[attName] = attr
self.attName2Obj = attName2Obj
# create Instances object
instances = Instances("instances", attVector, len(self.instances))
for i in self.instances:
inst = self._makeInstance(i)
instances.add(inst)
instances.setClass(attName2Obj[classAttr])
return instances
def _getInstances(self, classAttr):
# create attributes
self.classAttr = classAttr
attName2Obj = {}
attVector = FastVector()
for attName in self.numericAttributes:
attr = Attribute(attName)
attVector.addElement(attr)
attName2Obj[attName] = attr
for (attName, domain) in self.attName2Domain.iteritems():
vDomain = FastVector(len(domain))
for v in domain:
#print v
vDomain.addElement(String(str(v)))
attr = Attribute(attName, vDomain)
attVector.addElement(attr)
attName2Obj[attName] = attr
self.attName2Obj = attName2Obj
# create Instances object
instances = Instances("instances", attVector, len(self.instances))
for i in self.instances:
inst = self._makeInstance(i)
instances.add(inst)
instances.setClass(attName2Obj[classAttr])
return instances
def readDataFromResultsTable(attributes, rt):
data = Instances("results", ArrayList(attributes), rt.size())
nrOfFeatures = len(attributes)
for i in range(0, rt.size()):
inst = DenseInstance(nrOfFeatures)
for j in range(0, nrOfFeatures):
value = rt.getValue(attributes[j].name(), i)
inst.setValue(attributes[j], value)
data.add(inst)
return data
def createTrainingInstances(matchingExamples, mismatchingExamples):
""" Expects the matchingExamples to be a list of feature lists,
i.e. the feature vector is a list. """
numFeatures = len(matchingExamples[0])
attributes = [Attribute(str(i) + " numeric") for i in range(numFeatures)]
attributes.append(Attribute("class", ArrayList(["true", "false"])))
trainingData = Instances("matches", ArrayList(attributes), len(matchingExamples) + len(mismatchingExamples))
trainingData.setClassIndex(len(attributes) -1) # the last index
for f in matchingExamples:
trainingData.add(DenseInstance(1.0, f + [1])) # 1 is True
for f in mismatchingExamples:
trainingData.add(DenseInstance(1.0, f + [0])) # 0 is False
return trainingData
def classify(classifier, matches):
""" Expects one vector numFeatures length """
""" returns a list of [result, distributionforinstance match]"""
attributes = createAttributes(matches[0])
instances = Instances("tests", attributes, 1)
instances.setClassIndex(len(attributes) -1)
distribution=[] ###
for match in matches:
instances.add(DenseInstance(1.0, match + [0]))
for i in range(len(matches)):
result=classifier.classifyInstance(instances.instance(i))
dist=(classifier.distributionForInstance(instances.instance(i)))
results=[result, dist[1]]
return results
def build_instances(state,dataset):
class_attributes = ["Sunny", "Fog", "Rain", "Snow", "Hail", "Thunder", "Tornado"]
header = ["state","lat", "lon", "day","temp","dewp","weather"]
#build attributes based on the header and types
attributes = []
for h in header[:-1]:
attributes.append(Attribute(h))
#add the classification attribute
classification_vector = FastVector(len(class_attributes))
for c in class_attributes:
classification_vector.addElement(c)
attributes.append(Attribute("toClassify", classification_vector))
fvWekaAttributes = FastVector(len(dataset[0]))
for a in attributes:
fvWekaAttributes.addElement(a)
training_set = Instances("C4.5Set", fvWekaAttributes, len(dataset))
training_set.setClassIndex(len(header)-1)
for d in dataset:
inst = Instance(len(d))
for i in range(len(d)-1):
try:
inst.setValue(fvWekaAttributes.elementAt(i), float(d[i]))
except:
pass
#print "failed on", i, d[i], d[i].__class__
inst.setValue(fvWekaAttributes.elementAt(len(d)-1), d[-1])
training_set.add(inst)
j48 = J48()
j48.buildClassifier(training_set)
return state,parse_tree(str(j48))
def createTrainingData(img, samples, class_names, n_samples=0, ops=None):
""" img: a 2D RandomAccessibleInterval.
samples: a sequence of long[] (or int numeric sequence or Localizable) and class_index pairs; can be a generator.
n_samples: optional, the number of samples (in case samples is e.g. a generator).
class_names: a list of class names, as many as different class_index.
ops: optional, the sequence of ImgMath ops to apply to the img, defaults to filterBank(img)
return an instance of WEKA Instances
"""
ops = ops if ops else filterBank(img)
if 0 == n_samples:
n_samples = len(samples)
# Define a WEKA Attribute for each feature (one for op in the filter bank, plus the class)
attribute_names = ["attr-%i" % (i+1) for i in xrange(len(ops))]
attributes = ArrayList()
for name in attribute_names:
attributes.add(Attribute(name))
# Add an attribute at the end for the classification classes
attributes.add(Attribute("class", class_names))
# Create the training data structure
training_data = Instances("training", attributes, n_samples)
training_data.setClassIndex(len(attributes) -1)
opImgs = [compute(op).into(ArrayImgs.floats([img.dimension(0), img.dimension(1)])) for op in ops]
ra = Views.collapse(Views.stack(opImgs)).randomAccess()
for position, class_index in samples:
ra.setPosition(position)
tc = ra.get()
vector = array((tc.get(i).getRealDouble() for i in xrange(len(opImgs))), 'd')
vector += array([class_index], 'd')
training_data.add(DenseInstance(1.0, vector))
return training_data
target = ArrayImgs.floats([width, height])
interval = FinalInterval([14, 14], [17, 17])
n_samples = Intervals.numElements(interval)
for ci, v in enumerate([fillValue, backgroundValue]):
for _ in xrange(training_data.size() /
4): # the other 2/4 are the membrane and mit boundary
other = syntheticEM([], width, height, 0, v, noise=True)
vectors = [zeros(len(attributes), 'd') for _ in xrange(n_samples)]
for k, op in enumerate(filterBank(IL.wrap(other),
sumType=DoubleType())):
imgOp = compute(op).into(target)
for i, v in enumerate(Views.interval(imgOp, interval)):
vectors[i][k] = v.getRealDouble()
for vector in vectors:
vector[-1] = ci + 2 # class index
training_data.add(DenseInstance(1.0, vector))
# Create a classifier: support vector machine (SVM, an SMO in WEKA)
classifier = SMO()
classifier.buildClassifier(training_data)
print classifier.toString()
# Save the trained classifier for later
SerializationHelper.write("/tmp/svm-em-mem-mit", classifier)
"""
imp = WindowManager.getImage("180-220-sub512x512-30.tif") # IJ.getImage() # e.g. 8-bit EM of Drosophila neurons 180-220-sub512x512-30.tif
#imp = IJ.openImage("/home/albert/lab/TEM/abd/microvolumes/Seg/180-220-sub/180-220-sub512x512-30.tif")
img = IL.wrap(imp)
"""
"""
from hr.irb.fastRandomForest import FastRandomForest
