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| #include <iostream> | |
| #include <ctime> | |
| using namespace std; | |
| void main() | |
| { | |
| srand((unsigned)time(NULL)); // Set random seed | |
| Neuron* neuron = new Neuron(2, 0.1); |
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| class A: | |
| def __init__(self, percentage): | |
| self.percentage = percentage | |
| if __name__ == '__main__': | |
| data = [A(12), A(32), A(22)] |
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| sudo apt-get update | |
| sudo apt-get install python-dev libffi-dev libssl-dev zlib1g-dev libncurses5-dev libsqlite3-dev -y | |
| tar xvf Python-2.7.*.tar.xz | |
| cd Python-2.7.* | |
| ./configure | |
| sudo make -j 4 | |
| sudo make altinstall | |
| which python2 |
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| from random import shuffle | |
| tags = ['a', 'b', 'c', 'result'] | |
| def get_triangle_type(a, b, c): | |
| if a == 90 or b == 90 or c == 90: | |
| return "직각" | |
| elif a > 90 or b > 90 or c > 90: | |
| return "둔각" | |
| elif a == b == c == 60: |
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| import json | |
| import pandas as pd | |
| from collections import defaultdict | |
| multi_depth_defaultdict = lambda: defaultdict(multi_depth_defaultdict) | |
| df = pd.read_excel('category.xlsx') | |
| data = multi_depth_defaultdict() |
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| """ | |
| 1. main 함수에서 `async def A`를 호출 후 await 하고, 반환값을 받아 다시 `async def B`를 호출하는 방식 | |
| """ | |
| import asyncio | |
| from socket import socket, AF_INET, SOCK_STREAM |
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| from keras.utils import np_utils | |
| from keras.datasets import mnist | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Activation | |
| (x_train, y_train), (x_test, y_test) = mnist.load_data() | |
| x_train = x_train.reshape(x_train.shape[0], 784).astype('float32') / 255.0 | |
| x_test = x_test.reshape(x_test.shape[0], 784).astype('float32') / 255.0 | |
| y_train = np_utils.to_categorical(y_train) | |
| y_test = np_utils.to_categorical(y_test) |
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| 6 | 148 | 72 | 35 | 0 | 33.6 | 0.627 | 50 | 1 | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 85 | 66 | 29 | 0 | 26.6 | 0.351 | 31 | 0 | |
| 8 | 183 | 64 | 0 | 0 | 23.3 | 0.672 | 32 | 1 | |
| 1 | 89 | 66 | 23 | 94 | 28.1 | 0.167 | 21 | 0 | |
| 0 | 137 | 40 | 35 | 168 | 43.1 | 2.288 | 33 | 1 | |
| 5 | 116 | 74 | 0 | 0 | 25.6 | 0.201 | 30 | 0 | |
| 3 | 78 | 50 | 32 | 88 | 31.0 | 0.248 | 26 | 1 | |
| 10 | 115 | 0 | 0 | 0 | 35.3 | 0.134 | 29 | 0 | |
| 2 | 197 | 70 | 45 | 543 | 30.5 | 0.158 | 53 | 1 | |
| 8 | 125 | 96 | 0 | 0 | 0.0 | 0.232 | 54 | 1 |
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| http://tykimos.github.io/warehouse/2017-3-8_CNN_Getting_Started_handwriting_shape.zip |
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| # Whole data | |
| seq = ['g8', 'e8', 'e4', 'f8', 'd8', 'd4', 'c8', 'd8', 'e8', 'f8', 'g8', 'g8', 'g4', | |
| 'g8', 'e8', 'e8', 'e8', 'f8', 'd8', 'd4', 'c8', 'e8', 'g8', 'g8', 'e8', 'e8', 'e4', | |
| 'd8', 'd8', 'd8', 'd8', 'd8', 'e8', 'f4', 'e8', 'e8', 'e8', 'e8', 'e8', 'f8', 'g4', | |
| 'g8', 'e8', 'e4', 'f8', 'd8', 'd4', 'c8', 'e8', 'g8', 'g8', 'e8', 'e8', 'e4'] |
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