本文整理汇总了Python中sklearn.preprocessing.MinMaxScaler.partial_fit方法的典型用法代码示例。如果您正苦于以下问题：Python MinMaxScaler.partial_fit方法的具体用法？Python MinMaxScaler.partial_fit怎么用？Python MinMaxScaler.partial_fit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sklearn.preprocessing.MinMaxScaler的用法示例。
在下文中一共展示了MinMaxScaler.partial_fit方法的2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: imresize
# 需要导入模块: from sklearn.preprocessing import MinMaxScaler [as 别名]
# 或者: from sklearn.preprocessing.MinMaxScaler import partial_fit [as 别名]
img_2 = imresize(img,[224,224])
train_X = np.zeros((1,3,32,32))
train_X1 = np.zeros((1,3,224,224))
train_X[:,0,:,:] = img_1[:,:,0]
train_X1[:,0,:,:] = img_2[:,:,0]
train_X[:,1,:,:] = img_1[:,:,1]
train_X1[:,1,:,:] = img_2[:,:,1]
train_X[:,2,:,:] = img_1[:,:,2]
train_X1[:,2,:,:] = img_2[:,:,2]
#get features
feat_1 = features(np.array(train_X,dtype=np.float32))
feat_2 = get_features(img_nr, 1)
feat_3 = features_caffe(np.array(train_X1,dtype=np.float32))
print len(feat_3), len(feat_3[0])
scaler1.partial_fit(feat_1)
scaler2.partial_fit(feat_2)
scaler3.partial_fit(feat_3)
learning_rates = [0.01,0.001,0.0001]
#for eta in learning_rates:
#train
for img_nr in img_train:
#load image
if os.path.isfile('/var/node436/local/tstahl/Images/'+ (format(img_nr, "06d")) +'.jpg'):
img = imread('/var/node436/local/tstahl/Images/'+ (format(img_nr, "06d")) +'.jpg')
else:
print 'warning: /var/node436/local/tstahl/Images/'+ (format(img_nr, "06d")) +'.jpg doesnt exist'
img = imresize(img,[32,32])
train_X = np.zeros((1,3,32,32))示例2: WordCluster
# 需要导入模块: from sklearn.preprocessing import MinMaxScaler [as 别名]
# 或者: from sklearn.preprocessing.MinMaxScaler import partial_fit [as 别名]
#.........这里部分代码省略.........
return lv2
def get_words_count(self):
return DB.Vocabulary.select(DB.Vocabulary.lv1,DB.Vocabulary.lv2).where((DB.Vocabulary.lv2 != -1) & (DB.Vocabulary.lv1 != -1)).distinct().count()
def get_samples(self):
'''
获取所有样本
:return: {(lab,filename):[11,222,333,], ...}
'''
docs = {}
for f in DB.Vocabulary.select(DB.Vocabulary.lv1,DB.Vocabulary.lv2,DB.Feature.label,DB.Feature.docname).join(DB.Feature).where((DB.Vocabulary.lv2 != -1) & (DB.Vocabulary.lv1 != -1)).iterator():
assert isinstance(f,DB.Vocabulary)
key = (f.feature.label, f.feature.docname)
if not docs.has_key(key):
docs[key]=[]
docs[key].append(f.lv1)
return docs
def create_classifier(self):
DB.db.connect()
clf = SGDClassifier( loss="modified_huber")
labs_map = NameToIndex()
with DB.db.transaction():
offset = 0
words_count = self.get_words_count()
classes = numpy.arange(0,words_count)
x_all = []
y_all = []
while True:
print ' %d partial_fit %d'%(time(),offset)
query = DB.Vocabulary\
.select(DB.Vocabulary.lv1, DB.Vocabulary.lv2)\
.join(DB.PcaModel, on=(DB.Vocabulary.feature == DB.PcaModel.feature)).order_by( DB.Vocabulary.feature).offset(offset).limit(1000)\
.tuples().iterator()
features = numpy.array(map(lambda x:[x[0]]+list(x[1]),query))
offset += len(features)
if len(features) == 0:
break
Y = features[:,0]
X = features[:,1:]
labs = []
for lab in Y:
labs.append(labs_map.map(lab))
if(len(x_all)<10000):
x_all = x_all + X.tolist()
y_all = y_all + labs
labs = numpy.array(labs)
#clf = LinearSVC()
#clf = OneVsRestClassifier(SVC(probability=True, kernel='linear'))
#clf.fit(X,labs)
clf.partial_fit(X,labs,classes)
print clf.score(x_all,y_all)
DB.TrainingResult.delete().where(DB.TrainingResult.name == self.__class__.__name__+"_clf").execute()
DB.TrainingResult.delete().where(DB.TrainingResult.name == self.__class__.__name__+"_labs_map").execute()
tr = DB.TrainingResult()
tr.name = self.__class__.__name__+"_clf"