本文整理汇总了Python中Utils.Utils.loadFeatures方法的典型用法代码示例。如果您正苦于以下问题：Python Utils.loadFeatures方法的具体用法？Python Utils.loadFeatures怎么用？Python Utils.loadFeatures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.loadFeatures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: mergeFiles
# 需要导入模块: from Utils import Utils [as 别名]
# 或者: from Utils.Utils import loadFeatures [as 别名]
def mergeFiles(self, trainFeaturesFile, testFeaturesFile):
(namesObservationsTr, coordinatesTr, train) = Utils.loadFeatures(trainFeaturesFile)
(namesObservationsTe, coordinatesTe, test) = Utils.loadFeatures(testFeaturesFile)
namesObservations = np.concatenate((namesObservationsTr,namesObservationsTe))
coordinates = np.concatenate((coordinatesTr, coordinatesTe))
dataset = np.concatenate((train, test))
namesObservations = np.reshape(namesObservations, (namesObservations.shape[0],1))
return (namesObservations, coordinates, dataset) 示例2: run
# 需要导入模块: from Utils import Utils [as 别名]
# 或者: from Utils.Utils import loadFeatures [as 别名]
def run(self):
print "Preparing the environment"
self.prepareEnvironment()
print "Reading in the training data"
imageCollections = data_io.get_train_df()
wndchrmWorker = WndchrmWorkerTrain()
print "Getting features"
if not self.loadWndchrm: #Last wndchrm set of features
featureGetter = FeatureGetter()
fileName = data_io.get_savez_name()
if not self.load: #Last features calculated from candidates
(namesObservations, coordinates, train) = Utils.calculateFeatures(fileName, featureGetter, imageCollections)
else:
(namesObservations, coordinates, train) = Utils.loadFeatures(fileName)
print "Getting target vector"
(indexes, target, obs) = featureGetter.getTargetVector(coordinates, namesObservations, train)
print "Saving images"
imageSaver = ImageSaver(coordinates[indexes], namesObservations[indexes],
imageCollections, featureGetter.patchSize, target[indexes])
imageSaver.saveImages()
print "Executing wndchrm algorithm and extracting features"
(train, target) = wndchrmWorker.executeWndchrm()
else:
(train, target) = wndchrmWorker.loadWndchrmFeatures()
print "Training the model"
model = RandomForestClassifier(n_estimators=500, verbose=2, n_jobs=1, min_samples_split=30, random_state=1, compute_importances=True)
model.fit(train, target)
print model.feature_importances_
print "Saving the classifier"
data_io.save_model(model)示例3: runWithoutWndchrm
# 需要导入模块: from Utils import Utils [as 别名]
# 或者: from Utils.Utils import loadFeatures [as 别名]
def runWithoutWndchrm(self):
print "Reading in the training data"
imageCollections = data_io.get_train_df()
print "Getting features"
featureGetter = FeatureGetter()
fileName = data_io.get_savez_name()
if not self.load: #Last features calculated from candidates
(namesObservations, coordinates, train) = Utils.calculateFeatures(fileName, featureGetter, imageCollections)
else:
(namesObservations, coordinates, train) = Utils.loadFeatures(fileName)
print "Getting target vector"
(indexes, target, obs) = featureGetter.getTargetVector(coordinates, namesObservations, train)
print "Training the model"
classifier = RandomForestClassifier(n_estimators=500, verbose=2, n_jobs=1, min_samples_split=10, random_state=1, compute_importances=True)
#classifier = KNeighborsClassifier(n_neighbors=50)
model = Pipeline([('scaling', MinMaxScaler()), ('classifying', classifier)])
model.fit(obs[indexes], target[indexes])
print "Saving the classifier"
data_io.save_model(model)