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| package main | |
| import "fmt" | |
| func orChan(chans ...chan interface{}) chan interface{} { | |
| switch len(chans) { | |
| case 0: | |
| return nil | |
| case 1: | |
| return chans[0] |
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| package main | |
| import ( | |
| "fmt" | |
| ) | |
| type TreeNode struct { | |
| Val int | |
| Left *TreeNode | |
| Right *TreeNode |
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| deb http://mirrors.163.com/ubuntu/ bionic main restricted universe multiverse | |
| deb http://mirrors.163.com/ubuntu/ bionic-security main restricted universe multiverse | |
| deb http://mirrors.163.com/ubuntu/ bionic-updates main restricted universe multiverse | |
| deb http://mirrors.163.com/ubuntu/ bionic-proposed main restricted universe multiverse | |
| deb http://mirrors.163.com/ubuntu/ bionic-backports main restricted universe multiverse | |
| deb-src http://mirrors.163.com/ubuntu/ bionic main restricted universe multiverse | |
| deb-src http://mirrors.163.com/ubuntu/ bionic-security main restricted universe multiverse | |
| deb-src http://mirrors.163.com/ubuntu/ bionic-updates main restricted universe multiverse | |
| deb-src http://mirrors.163.com/ubuntu/ bionic-proposed main restricted universe multiverse | |
| deb-src http://mirrors.163.com/ubuntu/ bionic-backports main restricted universe multiverse |
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| #set connection data accordingly | |
| source_host=*.*.*.* | |
| source_port=6379 | |
| target_host=*.*.*.* | |
| target_port=6379 | |
| passwd=redisPasswd | |
| key=testKey | |
| redis-cli --raw -h $source_host -p $source_port -a $passwd DUMP "$key" | head -c -1 | redis-cli -x -h $target_host -p $target_port -a $passwd RESTORE "$key" 0 |
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| // https://stackoverflow.com/questions/14184099/fastest-way-to-remove-duplicate-documents-in-mongodb | |
| var duplicates = []; | |
| db.getCollection("appstore").aggregate([ | |
| { $match: { | |
| game: { "$eq": 'yyygg' } // discard selection criteria | |
| }}, | |
| { $group: { | |
| _id: { userReviewId: "$userReviewId"}, // can be grouped on multiple properties |
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| import pymysql | |
| db_conf_dev = { | |
| 'NAME': '', | |
| 'USER': '', | |
| 'PASSWORD': '', | |
| 'HOST': '', | |
| 'PORT': 3307 | |
| } | |
| db_conf_prod = { |
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| image: "python:3.7" | |
| before_script: | |
| - python --version | |
| - pip install -r requirements.txt | |
| stages: | |
| - Static Analysis | |
| - Test |
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| class GAN(): | |
| def __init__(self): | |
| self.img_rows = 28 | |
| self.img_cols = 28 | |
| self.channels = 1 | |
| self.img_shape = (self.img_rows, self.img_cols, self.channels) | |
| optimizer = Adam(0.0002, 0.5) | |
| # Build and compile the discriminator |
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| activation_model = keras.models.Model(inputs=model.input, outputs=[layer.output for layer in model.layers]) | |
| layer_names = [] | |
| for layer in model.layers[:8]: | |
| layer_names.append(layer.name) | |
| images_per_row = 8 | |
| for layer_name, layer_activation in zip(layer_names, activations): | |
| n_features = layer_activation.shape[-1] | |
| size1 = layer_activation.shape[1] |
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| #!/usr/bin/python3 | |
| for i in iter(int, 1): | |
| ... |