Henry Kim Kcrong

## main.cpp
#include <iostream>
#include <ctime>
using namespace std;

void main()
{
	srand((unsigned)time(NULL));	// Set random seed

	Neuron* neuron = new Neuron(2, 0.1);

## random_percentage_choice_by_attr
class A:
    def __init__(self, percentage):
        self.percentage = percentage

if __name__ == '__main__':
    data = [A(12), A(32), A(22)]

## install_new_python
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

## make_tri_data.py
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:

## script.py
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()

## async_await_help.py


"""
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

## first_sample.py
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)

## data.csv

          
            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

## data url
http://tykimos.github.io/warehouse/2017-3-8_CNN_Getting_Started_handwriting_shape.zip

## seq.py
# 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']
	#include <iostream>
	#include <ctime>
	using namespace std;

	void main()
	{
	srand((unsigned)time(NULL)); // Set random seed

	Neuron* neuron = new Neuron(2, 0.1);
	class A:
	def __init__(self, percentage):
	self.percentage = percentage

	if __name__ == '__main__':
	data = [A(12), A(32), A(22)]
	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
	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:
	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()


	"""
	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
	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)
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
	# 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']