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xuming06
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add demo.
1 parent 1a6c889 commit 16b1b10

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‎tool/compare_each_in_set.py‎

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# -*- coding: utf-8 -*-
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"""
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@author:XuMing（[email protected])
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@description:
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"""
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def get_pairs(input_lst):
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out_lst = []
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for i in range(len(input_lst)):
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m = input_lst[i]
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for j in range(i, len(input_lst)):
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n = input_lst[j]
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if m == n: continue
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out_lst.append([m, n])
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return out_lst
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lst = ['a','b']
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print(get_pairs(lst))

‎tool/fake.py‎

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def main():
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# 执行凤巢数据检测
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# wordseg_agent = wordseg_init()
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rule_set1 = load_comb_rule_vocabs("./data/Inputdata/jjy.rule.txt") # 规则库1，金融教育医疗行业，gb18030格式，转为unicode
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rule_set2 = load_comb_rule_vocabs("./data/Inputdata/quanhangye.rule.txt") # 规则库3,其他行业，gb18030格式，转为unicode

‎tool/parrots_demo.py‎

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# -*- coding: utf-8 -*-
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"""
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@author:XuMing（[email protected])
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@description:
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"""
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import parrots
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print(parrots.speak('给老人让坐'))
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‎tool/pycorrector_demo.py‎

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# -*- coding: utf-8 -*-
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"""
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@author:XuMing（[email protected])
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@description:
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"""
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import pycorrector
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print(pycorrector.correct('给老人让坐'))
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‎tool/rocket_demo.py‎

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# -*- coding: utf-8 -*-
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"""
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@author:XuMing（[email protected])
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@description:
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"""
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import base64
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import os
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import time
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import numpy as np
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import tensorflow as tf
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from keras.layers import Conv2D, UpSampling2D, InputLayer
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from keras.models import Sequential
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from keras.preprocessing.image import img_to_array, load_img
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from skimage.color import rgb2lab, lab2rgb
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from skimage.io import imsave
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def build_model():
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model = Sequential()
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model.add(InputLayer(input_shape=(None, None, 1)))
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model.add(Conv2D(8, (3, 3), activation='relu', padding='same', strides=2))
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model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
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model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))
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model.add(Conv2D(16, (3, 3), activation='relu', padding='same', strides=2))
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model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
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model.add(Conv2D(32, (3, 3), activation='relu', padding='same', strides=2))
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model.add(UpSampling2D((2, 2)))
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model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
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model.add(UpSampling2D((2, 2)))
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model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))
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model.add(UpSampling2D((2, 2)))
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model.add(Conv2D(2, (3, 3), activation='tanh', padding='same'))
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model.compile(optimizer='adam', loss='mse')
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return model
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build_model()
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