本文整理汇总了Python中Utils.Utils.readcsv方法的典型用法代码示例。如果您正苦于以下问题：Python Utils.readcsv方法的具体用法？Python Utils.readcsv怎么用？Python Utils.readcsv使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Utils.Utils的用法示例。
在下文中一共展示了Utils.readcsv方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: checkMissedCount
# 需要导入模块: from Utils import Utils [as 别名]
# 或者: from Utils.Utils import readcsv [as 别名]
def checkMissedCount(self, imageName, centers):
csvArray = Utils.readcsv(imageName)
totalCount = len(csvArray)
totalMitoticPoints = len(csvArray)
missedCount = 0
for i in csvArray:
found = False
for center in centers:
if self.isInAdmissibleRadius(i, center):
found = True
break
if not found:
print "missed! %s" % (str(i))
missedCount += 1
return (missedCount, totalCount, totalMitoticPoints)示例2: checkCandidates
# 需要导入模块: from Utils import Utils [as 别名]
# 或者: from Utils.Utils import readcsv [as 别名]
def checkCandidates(self):
imageCollections = data_io.get_train_df()
featureGetter = FeatureGetter()
(namesObservations, coordinates, train) = featureGetter.getTransformedDatasetChecking(imageCollections)
imageNames = namesObservations
currentImage = imageNames[0]
csvArray = Utils.readcsv(imageNames[0])
mitoticPointsDetected = 0
totalMitoticPoints = len(csvArray)
finalTrain = []
for i in range(len(coordinates)):
if imageNames[i] != currentImage:
csvArray = Utils.readcsv(imageNames[i])
totalMitoticPoints += len(csvArray)
currentImage = imageNames[i]
for point in csvArray:
if ((point[0]-coordinates[i][0]) ** 2 + (point[1]-coordinates[i][1]) ** 2)< 30**2:
mitoticPointsDetected += 1
csvArray.remove(point)
finalTrain.append(train[i])
break
finalTrain = np.array(finalTrain)
allArea = finalTrain[:,0]
allPerimeter = finalTrain[:,1]
allRoundness = finalTrain[:,2]
totalObservations = len(coordinates)
print "Minimum Area: %f" % np.min(allArea)
print "Minimum Perimeter: %f" % np.min(allPerimeter)
print "Minimum Roundness: %f" % np.min(allRoundness)
print "Maximum Area: %f" % np.max(allArea)
print "Maximum Perimeter: %f" % np.max(allPerimeter)
print "Maximum Roundness: %f" % np.max(allRoundness)
print "Total number of candidates: %d" % (totalObservations)
print "Total number of mitotic points: %d" %(totalMitoticPoints)
print "Mitotic points detected: %d" %(mitoticPointsDetected)
print "Mitotic points missed: %d" %(totalMitoticPoints-mitoticPointsDetected)示例3: getTargetVector
# 需要导入模块: from Utils import Utils [as 别名]
# 或者: from Utils.Utils import readcsv [as 别名]
def getTargetVector(self, coordinates, names, observations, balancingMode=5, overSampling=100):
if balancingMode == 0: # No balancing
balancer = DummyBalancer()
elif balancingMode == 1:
balancer = NearestNeighborBalancer(observations)
elif balancingMode == 2:
balancer = KMeansBalancer(observations)
elif balancingMode == 3:
balancer = PostKMeansBalancer(observations)
elif balancingMode == 4:
balancer = CircularBalancer(observations)
elif balancingMode == 5:
balancer = RandomBalancer(observations)
else:
raise ValueError("Incorrect balancing mode.")
target = np.zeros(len(coordinates))
currentImage = ""
pointsArray = 0
indexesPicked = []
indexesToPick = []
for obsNum in range(len(coordinates)):
if names[obsNum] != currentImage:
indexesPicked.extend(balancer.balance(indexesToPick, pointsArray))
currentImage = names[obsNum]
csvArray = Utils.readcsv(currentImage)
indexesToPick = []
pointsArray = 0
for point in csvArray:
if self.isInAdmissibleRadius(point, coordinates[obsNum]):
target[obsNum] = 1
indexesPicked.append(obsNum)
pointsArray += 1
break
if target[obsNum] == 0:
indexesToPick.append(obsNum)
if overSampling != 0:
newValuesAdded = SmoteWorker.run(observations[np.where(target == 1)[0]], overSampling)
target = np.concatenate((target, np.ones(len(newValuesAdded))))
indexesPicked.extend(range(len(coordinates), len(coordinates) + len(newValuesAdded)))
newObservations = np.concatenate((observations, newValuesAdded))
else:
newObservations = observations
target = np.array(target)
balancer.observations = newObservations
indexesPicked = balancer.postBalance(indexesPicked, target)
return (indexesPicked, target, newObservations)